research topic examples for ict students

171+ Most Recent And Good ICT Research Topics For Students in 2024

In the fast-changing world of technology and communication, choosing good research topics is essential for students wanting to explore this always-changing field. This list of ICT research topics for students and ideas is like a starting point for students to look into the latest advancements, tackle current problems, and contribute to how technology changes our world.

From looking at how computers learn and protect them from online threats to thinking about what’s right or wrong with new technologies, these research topics cover many interesting areas. Students can explore things like using big amounts of information to help make decisions or finding out how blockchain can keep our information safe. There’s also the chance to look at how technology affects society, like who has access to it, and think about what’s fair when using our personal information.

By looking into the best ICT research topics for students , they can learn more about technology and have a say in its development. Each research topic gives a different way to think about and solve problems, helping students get into technology and communication.

Table of Contents

What Is ICT Research Topics?

ICT research topics are basically subjects that researchers study to learn more about computers and communication. It’s a wide area that includes making computers learn and decide independently, protecting them from online problems, and figuring out how to use lots of information to make better choices.

Researchers are also looking into how to keep our information safe using a special kind of technology called blockchain. They explore the fair use of technology and study how it affects different social groups. Plus, they check out the good and bad sides of using technology daily.

By looking into these topics, researchers help us understand technology better and develop new and better ways to use it. Each topic is like a different way of thinking about and solving problems, ensuring technology improves and works well for everyone.

How Can I Find Good ICT Research Topics For Students?

Trying to find good research topics in ICT for students? Here’s a simple guide to help you out:

• Keep Updated: Stay informed about the latest things happening in the tech world. Read magazines, websites, and other sources to learn about new technologies and their challenges.
• Think About What You Like: Consider what you enjoy in the wide tech world. Whether it’s smart machines, online safety, or working with lots of data, choosing a topic you’re passionate about will keep you interested.
• Look at What Others Are Doing: Check out what scientists write about in academic sources. This can help you see what’s missing in what we know and where there’s room for more research.
• Talk to Your Friends and Teachers: Chat with your friends, classmates, and teachers. They might have ideas or opinions that can help you find an interesting question to study.
• Useful Tech Ideas: Find topics that can be useful in the real world. Think about how your research can help solve problems or make tech better.
• Read the Rules: Check if your school has any rules about the topics you can choose. Following these rules will ensure your topic fits your school’s expectations.
• Join Events: Go to tech conferences and workshops. These events often show off the newest research and might suggest what to study.
• Think and Plan: Take some time to think and make a plan. Create a map of your ideas. This can help you see how different things connect.
• Think About What’s Right: Consider what’s the right thing to do. Choose a topic that follows the rules and is considered fair and good.
• Get Advice: Ask your teachers or other trusted adults for advice once you have ideas. They can help you make your idea better.

List of Best ICT Research Topics For Students In 2024

Here are the various Best ICT Research Topics for students it is such as;

Best ICT Research Topics

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Here is the problem of ICT students for Quantitative research titles:

Sample Research Proposal Topics In Information And Communication Technology PDF

Here are the ICT research topics for students Pdf is given below:

What Are The Best Titles In Research That Are Related To Ict?

Here are some examples of titles that represent diverse aspects of ICT research:

Great ICT Research Topics for Students open doors for exploring the world of technology in ways that are interesting and useful. These topics give students chances to learn by doing, helping them understand and solve real-world problems using Information and Communication Technology (ICT). Choosing from a variety of topics allows students to focus on what they enjoy, whether it’s artificial intelligence, keeping things safe online, or looking at how technology affects our lives.

The goal of these Good ICT Research Topics for Students is to encourage creativity and smart thinking. Whether it’s understanding how tech influences society or thinking about what’s right and fair in the digital world, these topics cover a wide range. Students can pick topics that match their interests and skills.

To sum it up, Good ICT Research Topics for Students not only make learning exciting but also give students the chance to be part of shaping the future of technology. Through these research projects, students become valuable contributors to the ongoing discussions about Information and Communication Technology, making a real impact on the ever-changing world of tech.

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Trends and Topics in Educational Technology, 2022 Edition

• Column: Guest Editorial
• Published: 23 February 2022
• Volume 66 , pages 134–140, ( 2022 )

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• Royce Kimmons 1 &
• Joshua M. Rosenberg 2  

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This editorial continues our annual effort to identify and catalog trends and popular topics in the field of educational technology. Continuing our approach from previous years (Kimmons, 2020 ; Kimmons et al., 2021 ), we use public internet data mining methods (Kimmons & Veletsianos, 2018 ) to extract and analyze data from three large data sources: the Scopus research article database, the Twitter #edtech affinity group, and school and school district Facebook pages. Such data sources can provide valuable insights into what is happening and what is of interest in the field as educators, researchers, and students grapple with crises and the rapidly evolving uses of educational technologies (e.g., Kimmons et al., 2020 ; Trust et al., 2020 ; Veletsianos & Kimmons, 2020 ). Through this analysis, we provide a brief snapshot of what the educational technology field looked like in 2021 via each of these lenses and attempt to triangulate an overall state of our field and vision for what may be coming next.

What Were Trending Topics in Educational Technology Journals in 2021?

Educational technology research topics for 2021 were very similar to previous years, with a few exceptions. In total, we collected titles for 2368 articles via Scopus published in top educational technology journals as identified by Google Scholar. We then analyzed keyword and bigram (two words found together) frequencies in titles to determine the most commonly referenced terms. To assist in making sense of results, we also manually grouped together keywords and bigrams into four information types: contexts, methods, modalities, and topics. Contexts included terms referring to the research setting, such as “COVID-19” or “higher education.” Methods included terms referring to research methods involved in the article, such as “systematic review” or “meta-analysis.” Modalities included terms referring to the technical modality through which the study was occurring, such as “virtual reality” or “online learning.” Last, Topics included terms referring to the intervention, objective, or theoretical goal of the study, such as “computational thinking,” “learning environment,” or “language learning.” The most common bigrams and keywords for each type may be found in Table  1 ; a few items of interest follow.

Bigrams generally provide more specificity for interpreting meaning than do keywords, simply because keywords might have greater variety in usage (e.g., “school” might be used in the context of “primary school,” “secondary school,” “school teacher,” and so forth). So, when interpreting Table 1 , the bigram column is generally more useful for identifying trending topics, though the keyword column may at times be helpful as a clarifying supplement.

“Computational thinking” and “learning environments” were the two most-researched topical bigrams in 2021, and “virtual reality” and “online learning” were the most-researched modality bigrams. Most-referenced methods included “systematic review” and “meta-analysis,” which is noteworthy because such methods are used to conduct secondary analyses on existing studies, and their dominance may suggest an interest in the field to identify what works and to synthesize findings across various contexts within a sea of articles that is ever-increasing in size.

Due to the ongoing COVID-19 pandemic, this contextual term was regularly mentioned in many article titles (5.4%). “Pandemic” (3.4%), “emergency” (1.2%), and “shift to” (e.g., digital, online, blended; 0.9%) were also commonly referenced. This suggests that as the world continues to grapple with this multifaceted crisis, educational technology researchers are heavily engaged in addressing educational concerns associated with it (and remote teaching, particularly).

Grade level references in titles further suggested that educational technology research is being conducted at all levels but that it is most prominent at the higher education or post-secondary level and reduces in frequency as grade levels go down, with high school or secondary terms being more prominent than elementary or primary terms, with “higher education” (3.5%) being referenced twice as frequently as “K-12” (1.7%). This is noteworthy as it suggests that research findings associated with educational technology are currently mainly focused on older (and even adult) students and that if results are applied to understanding learners generally, then the needs of adolescents and younger children may currently be relatively underrepresented.

What Were Trending #Edtech Topics and Tools on Twitter in 2021?

Twitter is a valuable source of information about trends in a field because it allows researchers and practitioners to share relevant resources, studies, and musings and categorize posts via descriptive hashtags. The #edtech hashtag continued to be very popular during 2021, and we collected all original tweets (ignoring retweets) that included the #edtech hashtag for the year. This included 433,078 original tweets posted by 40,767 users, averaging 36,090 tweets per month ( SD  = 2974).

Because users can include multiple hashtags on a tweet, we aggregated the frequencies of additional (co-occurring) hashtags to determine the intended audiences (e.g., #teachers, #k12) and content topics (e.g., #elearning, #ai) of tweets. Some of the most popular additional hashtags of each type are presented in Table  2 . To better understand results, we also calculated the representation of each additional hashtag in the overall dataset (e.g., 2% of all #edtech tweets also included the #teachers hashtag) and the diversity of authorship (i.e., the number of users divided by the number of tweets). This diversity score was helpful for understanding how some hashtags were used by relatively few accounts for purposes such as product promotion. For example, the #byjus hashtag, which refers to an educational technology company founded in India, was tweeted 19,546 times. Still, the diversity score was only 3%, revealing that though this was a very popular hashtag in terms of tweet counts, it was being included by relatively few accounts at very high frequencies, such as via focused marketing campaigns.

Notably, several community or affinity space hashtags (Carpenter & Krutka, 2014 ; Rosenberg et al., 2016 ) were among the most common included with #edtech, such as #edchat, #edutwitter, and #teachertwitter. In particular, 13.9% of #edtech tweets also were tagged as #educhat, and 25.7% of #educhat tweets were also tagged as #edtech, revealing relatively high synchronicity between these two spaces. Furthermore, regarding institutional level, #k12 ( n  = 1712) and #highered ( n  = 1770) exhibited similar user counts, as did #school ( n  = 1284) and #highereducation ( n  = 1161), but, interestingly, the #k12 and #school hashtags exhibited nearly twice as many tweets as their #highered and #highereducation counterparts. This suggests that although the communities tweeting about topics for each group may be of similar size, the K-12 community was much more active than the higher education community.

Regarding topics, #elearning, #onlinelearning, #remotelearning, #distancelearning, #virtuallearning, and #blendedlearning were represented at a relatively high rate (in 16.1% of tweets), perhaps reflecting ongoing interest associated with #covid19. Other prominent topical hashtags included emerging technologies, such as #ai ( n  = 2112), #vr ( n  = 917), #ar ( n  = 679), and #blockchain ( n  = 545), as well as subject areas (e.g., #stem) and general descriptors (e.g., #innovation).

Furthermore, one of the primary reasons for tweeting is to share resources or media items. An analysis of these #edtech tweets revealed that 94.4% included either a link to an external site or an embedded media resource, such as an image or video. Regarding external links, prominent domains included (a) news sites, such as edsurge.com , edtechmagazine.com , or edutopia.org , (b) other social media, such as linkedin.com , instagram.com , or facebook.com , (c) multimedia resources, such as youtube.com , anchor.fm, or podcasts.apple.com , and (d) productivity and management tools, such as docs.google.com , forms.gle, or eventbrite.com (cf., Table  3 ).

Twitter communications in 2021 regarding #edtech included chatter about a variety of topics and resources. Shadows of #COVID-19 might be detected in the prevalence of this hashtag with others, like #remotelearning and #onlinelearning, but in many ways it seems that conversations continued to focus on issues of #education and #learning, as well as emerging topics like #ai, #vr, and #cybersecurity, suggesting some level of imperviousness to the pandemic.

What Were Trending Topics among Schools and School Districts on Facebook in 2021?

To examine trending educational technology topics on Facebook, we studied the posts by 14,481 schools and school districts on their public pages. First, one aspect of this analysis concerned the number of posts shared. In our last report, we documented how schools and districts posted more posts than in any other month during March, April, and May 2020—during the earliest and perhaps most tumultuous months of the COVID-19 pandemic, suggesting the importance of communication during this crisis period, as others have documented with Twitter data (Michela et al., 2022 ). Notably, in 2021, those months remained the most active; apart from those months, the numbers of posts by schools and districts in 2021 were roughly comparable to the numbers in 2019 and 2020 (see Fig.  1 ).

The Number of Posts on Facebook by Schools and School Districts

To understand which technologies were shared on these Facebook pages, we examined the domain names for all of the hyperlinks that were posted. Despite the myriad social and other changes experienced by schools from 2019 to 2021, link domains shared on Facebook exhibited remarkable consistency: Youtube, Google Docs, Google, and Google Drive—Google or tools created by Google—were the four most frequently shared for each of these years (Table  4 ). Note that the n represents the number of schools or districts sharing one or more links to these domains (of the 14,481 total school and school district pages). Thus, the 8278 indicates that 57.2% of schools and districts posted one or more links to YouTube over the 2021 year. These were followed by Zoom, which was also widely shared in 2020 (though not in 2019), and then Google Sites (which was shared frequently in 2020). The CDC and 2020 Census’s websites dropped from the list of the top ten most frequently shared domains in 2021, despite having been widely shared in 2020. Otherwise, the results are largely comparable between 2019, 2020, and 2021, indicating that schools and districts continued to use a core set of productivity tools despite the many disruptions and changes over this period.

We also examined the contents of the messages of schools’ and school districts’ posts. To do so, we considered the technologies identified by Weller ( 2020 ) in his history of the past 25 years of educational technology, as in our report for last year. Specifically, we searched the contents of the messages posted by schools and districts for the inclusion of the terms that correspond to technologies Weller identified as being representative of a particular year. While the domains shared by schools and districts demonstrated remarkable consistency, the contents of the messages posted by schools and districts varied substantially, especially when considering the changes from 2019 to 2020 and from 2020 to 2021. To illustrate, consider mentions of “e-learning,” which Weller identified as the focal point of 1999. In 2019, 834 messages that mentioned “e-learning” were posted by schools and districts, but in 2020, the number increased around ten-fold to 8326 mentions. Though it may have been expected for mentions of “e-learning” to remain somewhat constant during 2021, instead we saw a marked downturn to 1899 (or a 78% drop). This trend—a sizable increase in how often certain technologies were mentioned in 2020 relative to 2019 that was not sustained in 2021—was also found for mentions of “learning management systems,” “video,” and “Second Life and virtual worlds,” among others. Indeed, the only noteworthy increase in mentions of these technologies from 2020 to 2021 was for “artificial intelligence”.

Summary and Discussion

By triangulating the 2021 snapshots of each of these three data sources—Scopus, Twitter, and Facebook—we can begin to see a state of the educational technology field pressing into the future. Results on specific terms or topics may be useful for individual researchers and practitioners to see the representation of their areas of interest. Still, some common takeaways that emerge from all three sources include the following.

First, we found an emphasis on “e-learning”—particularly in Twitter and Facebook posts—as well as “blended learning” (Twitter) and “online learning” (journal articles). Notably, COVID-19 (and related terms) were also frequently mentioned. These findings align with how mentions of “e-learning” spiked during the 2020 year when the effects of the COVID-19 pandemic on education were especially disruptive, but their ongoing presence also suggests that interest in these topics will likely extend outside and beyond the context of the pandemic.

Second, we note a keen interest in emergent technologies like artificial intelligence and virtual reality, particularly on the part of researchers (as evidenced by how frequently these terms were mentioned in journal articles published in 2021). At the same time, we note that this interest has not yet crystallized into the sustained adoption and use of these emergent technologies—a point bolstered by the relatively limited mention of these technologies in the Facebook posts of schools and school districts. Thus, we think we as a field must wait and see whether interest in these technologies is lasting or transient.

Last, we found an ever-increasing reliance on several corporate entities for productivity and sharing. This was especially the case for Google and tools created by Google: YouTube, Google Docs, and Google Drive, in particular. Indeed, such tools are such an established part of our work (and educational) context that we might hardly think of them as tools. Furthermore, tools created by Google and several other corporations—including social media platforms themselves—were also prevalent in the content of the tweets we analyzed. While we do not believe it is a bad decision on the part of individuals or educational institutions to use these and other tools, there are also some potential downsides to their use that we think invite critical questions (Burchfield et al., 2021; Krutka et al., 2021 ).

As a result of these common takeaways, we will now conclude with three questions for educational technology researchers and practitioners to consider.

Pandemic Bump Vs. Ubiquity

First, many have wondered whether changes in educational technology catalyzed by the pandemic will yield sustained, ubiquitous changes to the field, or if adjustments represent only a short-term bump of interest—as may be the case with emergency remote teaching tools and strategies used in the early days of the pandemic (Hodges et al., 2020 ). One of the takeaways from our Facebook analysis was that while some productivity technologies appeared to have remained consistently used on the basis of our domain analysis (e.g., Google Docs), mentions of many specific technologies in the messages of the posts by schools and districts appeared to have been more transitory in nature, such as in the cases of “e-learning” and “learning management systems.” This suggests at least two possible interpretations. One is that these technologies were used in transient response to an unprecedented period of emergency remote instruction—though tools associated with remote teaching and learning continue to be used, their use was primarily a temporary, emergency measure. Another is that these tools were mentioned less because they have become a more ubiquitous but less visible tool used by teachers and learners. Learning management systems may still, of course, be widely used, but schools and districts may be sharing about their role less through their public social media platforms because they may already be familiar to students and their parents. While we cannot say why there was a dramatic increase followed by a decrease in the use of many educational technologies over the period from 2019 through 2021, our analysis indicates that many tools are, at least, being communicated about much less over the past year than in the preceding year when the pandemic began in the U.S.

Technocentrism Vs. Focusing on Learners and Improving Educational Systems

Second, though emerging technologies are obviously an essential component of our field, one of the perennial challenges we must grapple with is our relationship to these technologies. Are we technocentric, as Papert ( 1987 , 1990 ) warned, or do we focus on learning and improvement? In our results, we notice that technologies such as artificial intelligence, virtual reality, and augmented reality were very frequently referenced in comparison to most other modalities or topics of research. As processing and graphical rendering capabilities continue to become more compact and inexpensive via headsets, smartphones, and haptic devices, we would expect these technologies to continue to receive ongoing attention. Though there are certainly valuable learning improvement opportunities associated with such technologies (Glaser & Schmidt, 2021 ), we might also justifiably wonder whether the volume of attention that these technologies are currently receiving in the literature is concomitant to their actual (or even hypothetical) large-scale learning benefits—or whether current fascination with such technologies represents a repeat of other historical emphases that may not have panned out in the form of systemic educational improvement, such as in the case of MUVEs (cf., Nelson & Ketelhut, 2007 ).

Limited Broader Impacts on Larger Social Issues

Finally, to reiterate our critiques from previous years (Kimmons, 2020 ; Kimmons et al., 2021 ), we continued to see a dearth of references to important social issues in scholarly article titles, including references to social matters upon which educational technology should be expected to have a strong voice. For instance, terms relating to universal design ( n  = 0), accessibility ( n  = 4), privacy ( n  = 8), ethics ( n  = 12), security ( n  = 8), equity ( n  = 6), justice ( n  = 1), and (digital and participatory) divides ( n  = 1) were all very uncommon. Though “ethics” was the most common of these terms, it only was represented in 1-in-200 article titles, and though current “practices with student data represent cause for concern, as student behaviors are increasingly tracked, analyzed, and studied to draw conclusions about learning, attitudes, and future behaviors” (Kimmons, 2021 , para. 2; cf., Rosenberg et al., 2021 ) and proctoring software becomes increasingly ubiquitous (Kimmons & Veletsianos, 2021 ), “privacy” was only mentioned in 1-in-333 article titles and “proctor*” was only in 1-in-600 titles. In our current pandemic context, we have often heard educational technologists lament the fact that decision-makers and those in power may not seek our guidance in addressing issues related to the pandemic that would clearly benefit from our expertise. And yet, the absence of other socially-relevant topics from our research suggests that we may be challenged to leverage our work toward addressing matters of larger social or educational importance ourselves. A focus on the social matters and the social context around educational technology use, then, remains an opportunity for research and development by the educational technology community in the years ahead. This seems especially salient as our data suggests that the field is heavily influenced by big technology corporations like Google and Facebook that historically have been critiqued for violating ethical expectations of privacy and failing to support social good. As educational technology researchers and practitioners, we are primed with the position and expertise necessary to shape the future of ethical technology use in education. Hopefully, we can step up to this challenge.

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Kimmons, R., Rosenberg, J.M. Trends and Topics in Educational Technology, 2022 Edition. TechTrends 66 , 134–140 (2022). https://doi.org/10.1007/s11528-022-00713-0

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• Originality
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Finally, you should also assess whether this project could lead to potential career opportunities such as internships or job offers down the line. Make sure that you are researching something that is relevant enough so that it can benefit your professional development in some way. Additionally, consider how each research topic aligns with your career goals and interests; researching something that you are passionate about can help keep motivation high throughout the process.

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First, you should assess whether or not the research topic is within your area of competence. Of course, when you start out, you are not expected to be the world’s leading expert, but do should at least have some foundational knowledge.

Time commitment

When considering a research topic, you should think about how much time will be required for completion. Depending on your field of study, some topics may require more time than others due to their complexity or scope.

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Resources needed

It’s also critically important to consider what type of resources are necessary in order to conduct the research successfully. This includes physical materials such as lab equipment and chemicals but can also include intangible items like access to certain databases or software programs which may be necessary depending on the nature of your work. Additionally, if there are costs associated with obtaining these materials then this must also be factored into your evaluation process.

Potential risks

It’s important to consider the inherent potential risks for each potential research topic. These can include ethical risks (challenges getting ethical approval), data risks (not being able to access the data you’ll need), technical risks relating to the equipment you’ll use and funding risks (not securing the necessary financial back to undertake the research).

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An IERI – International Educational Research Institute Journal

• Open access
• Published: 02 August 2022

The influence of ICT use and related attitudes on students’ math and science performance: multilevel analyses of the last decade’s PISA surveys

• Matthew Courtney   ORCID: orcid.org/0000-0002-3253-8353 1 ,
• Mehmet Karakus   ORCID: orcid.org/0000-0002-3628-9809 2 ,
• Zara Ersozlu   ORCID: orcid.org/0000-0002-9120-2921 3 &
• Kaidar Nurumov 4  

Large-scale Assessments in Education volume  10 , Article number:  8 ( 2022 ) Cite this article

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This study analyzed the latest four PISA surveys, 2009, 2012, 2015, and 2018, to explore the association between students’ ICT-related use and math and science performance. Using ICT Engagement Theory as a theoretical framework and a three-level hierarchical linear modeling approach, while controlling for confounding effects, ICT-related independent variables of interest were added to the models at the student, school, and country levels. The series of models revealed that, in general, an increase in ICT availability and ICT use both inside and outside school had a negative association with learning outcomes, while students’ positive attitude toward ICT demonstrated a strong positive relationship. However, students’ perceived autonomy related to ICT use had the strongest association with academic performance, which is consistent with the changing nature of the modern learning environments. Findings revealed that virtually all forms of student ICT use, both inside and outside of school and whether subject related or not, had no substantive positive relationship with student performance in math or science. Conversely, higher student attitude toward, confidence in, belief in utility of, and autonomous use of ICT was associated with higher math and science performance for each of the four years of the study. Incidentally, we also found that while country GDP per capita had no consistent association with student performance, a school’s provision of extra-curricula activities did. Recommendations for educational leaders, teachers, and parents are offered.

Introduction

The use of Information and Communication Technology (ICT) have been a hot topic in education research since the beginning of the 1990s. ICT usage in vocational training, primary and secondary education is rapidly growing all around the world, but it remains unequally distributed across countries (OECD, 34 ). Schools are looking for new ways to integrate ICT skills into their policies and curriculum to foster the teaching and learning process in the context of “twenty-first-century skills” (Anderson, 1 ).

There is a rich research collection investigating the vital role that school ICT infrastructure and student ICT-related behaviour plays in students’ academic development, with much of this research country-specific, based on Programme for International Student Assessment (PISA) data, and focusing on only one cycle of PISA with some selection of variables (Biagi & Loi, 7 ; Bulut & Cutumisu, 9 ; Carrasco & Torrecilla, 9 ; Erdogdu & Erdogdu, 9 ; Hu et al., 21 ; Luu & Freeman, 24 ; Petko et al., 34 ; Wittwer et al., 51 ). We believe that researching the trends throughout the last decade of PISA cycles and making use of all key ICT-related variables can provide a more holistic picture of how school ICT infrastructure, ICT use and availability, and attitudes toward ICT is associated with academic performance over time. Therefore, the current study aims to explore the relationship between of ICT infrastructure, ICT use and availability inside and outside school, and students’ attitudes toward ICT and students’ math and science abilities measured in all the PISA surveys within the last decade (2009, 2012, 2015, and 2018).

Theoretical framework

This study uses Self-Determination Theory (SDT) to explain the associations between ICT-related variables and students’ academic performance. We bring together a set of environmental factors, individual differences, ICT use and availability inside and outside school, and attitudes toward ICT to explain the differences in students’ math and science performance. SDT asserts that self-motivation and determination are the main drivers of an individual’s learning (Deci & Ryan, 9 ). Competence (mastery and control over outcomes), relatedness (the drive to communicate with others), and autonomy (the desire to make their own choices) are the three basic facets in SDT used to explain mastery in learning (Deci & Ryan, 9 ). Based on SDT, Goldhammer et al. ( 9 ) introduced the ICT engagement concept with the dimensions of perceived autonomy related to ICT use, perceived ICT competence, ICT interest, and ICT as a topic in social interaction. Goldhammer et al. ( 9 ) assert that it is not only the use and availability of ICT inside and outside school but the underlying attitudes toward ICT that predict students’ academic achievement. Based on SDT, ICT Engagement Theory suggests that students’ interest, positive social interactions, autonomy, and competence related to ICT increase their intrinsic motivation, enabling them to challenge themselves with self-driven technology use, which can generate conditions conducive to optimal academic performance (Goldhammer et al., 9 ). Based on ICT Engagement Theory (Cristoph et al., 9 ; Kunina-Habenicht & Goldhammer, 24 ), student attitudes toward ICT were partially covered in the 2009 and 2012 cycles of PISA, while they were more closely reflected in the 2015 and 2018 cycles in the “ICT Familiarity Questionnaire” (OECD, 24 ).

In addition to our focus on student-related ICT variables, we also explore the role of background and ICT-related variables on student science and math performance. Though researched rarely (see Hu et al, 21 ), we explore the association between GDP per capita and Math and Science performance for each of the four cycles. Under SDT, it is important to consider the role of such contextual effects (Deci & Ryan, 9 ) and report on the results to educational stakeholders (Skyrabin et al., 34 ).

Considering that some schools can be considered digital frontrunners (Novak et al., 24 ) we are also interested in the role of school ICT infrastructure for the study period. Specifically, while controlling for important covariates (Zhang & Liu, 21 ) we look at the role of the number of available computers per student and the proportion of available computers connected to the internet in schools on the math and science performance of schools.

Students’ attitudes toward ICT

The empirical evidence suggests that students’ positive attitudes toward ICT are positively associated with their mathematics and science performance (Petko et al., 34 ; Tourón et al., 34 ). Areepattamannil and Santos ( 2 ) found that students who perceived themselves as autonomous and competent in ICT use develop positive views and feelings towards science, such as self-efficacy, enjoyment, and interest in science. Numerous studies have supported the notion that students’ mathematics and science achievement is associated with autonomous use of ICT (Hu et al., 21 ; Juhaňák et al., 9 ; Kunina-Habenicht & Goldhammer, 24 ; Meng et al., 21 ), interest in ICT use (Christoph et al., 9 ; Hu et al., 21 ; Koğar, 21 ; Kunina-Habenicht & Goldhammer, 24 ; Meng et al., 21 ), perceived self-confidence in ICT use (Guzeller & Akin, 9 ; Luu & Freeman, 24 ), and perceived self- competence in ICT use (Hu et al., 21 ; Koğar, 21 ; Kunina-Habenicht & Goldhammer, 24 ; Luu & Freeman, 24 ; Papanastasiou et al., 21 ; Srijamdee & Pholphirul, 44 ).

Although most of the studies reported positive relations between those attitudes and mathematics and science performance, Meng et al. ( 21 ) and Juhaňák et al. ( 9 ) reported controversial results for some of those attitudes. Meng et al. ( 21 ) found that the association between interest in ICT and mathematics and science performance was positive for the Chinese students while negative for the German students. Meng et al. ( 21 ) also reported a negative relationship between perceived self-competence and mathematics and science performance for the Chinese students, while there is no relation for the German students. In addition, Juhaňák et al. ( 9 ) found no associations between mathematics and science achievement with either interest in ICT or perceived self-competence (Czech students). On the other hand, most of the studies found negative relations between ICT use in social interaction and mathematics and science performance (Hu et al., 21 ; Juhaňák et al., 9 ; Meng et al., 21 ). Conversely, Martínez-Abad, Gamazo, and Rodríguez-Conde ( 9 ) reported positive associations between ICT use in social interaction and mathematics and science achievement on a sample of Spanish students. Given the conflicting results pertaining to students’ attitude toward ICT and academic performance, more substantial research in this area is in order.

ICT use and availability inside and outside of school

Research has suggested that ICT can add value to the learning process (UNESCO, 44 ). ICT use in educational settings with academic purposes has been shown to be useful in improving students’ performance in science (Erdogdu & Erdogdu, 9 ; Luu & Freeman, 24 ; Skryabin et al., 34 ) and mathematics (Carrasco & Torrecilla, 9 ; Erdogdu & Erdogdu, 9 ; Skryabin et al., 34 ).

The research on the impact of technology on learning outcomes, especially in mathematics and science, revealed the importance of technology use in education (Luu & Freeman, 24 ; Rutten et al., 24 ; Tamim et al., 21 ; Wittwer & Senkbeil, 51 ). Further, several meta-analysis studies suggested that ICT use in education has a small but positive impact on student performance (Bayraktar, 5 ; Cheung & Slavin, 9 ; Torgerson & Zhu, 51 ). However, a substantive number of research studies using large-scale international databases investigated how forms of ICT availability, use, and engagement has a positive association with student performance in mathematics and science (i.e., databases such as PISA, the Trends in International Mathematics and Science Study, TIMSS; and the Progress in International Reading Literacy Study, PIRLS). Importantly, the majority of these studies suggested that increased use of ICT at school had a negative association with mathematics and science performance (Bulut & Cutumisu, 9 ; Erdogdu & Erdogdu, 9 ; Hu et al., 21 ; Petko et al., 34 ; Skryabin et al., 34 ; Wittwer & Senkbeil, 51 ). The summary of the findings of a number of these key studies is now provided.

Using the PISA 2012 data, Petko et al. ( 34 ) investigated the role of the frequency of educational technology use on student achievement. They found that while ICT use at home for school purposes had a positive relationship with achievement, ICT use for entertainment purposes and the magnitude of use at school had a negative relationship with achievement. They also found that students’ positive attitudes towards educational technology were associated with higher test scores in most countries. They concluded that the moderate use of educational technology could be related to higher achievement, though both low and intensive use of educational technology in school appears to have a negative association. To explain this finding, the authors inferred that students’ lower academic achievement could be the result of ineffective pedagogy while they used technology and low-quality educational software that is used in the teaching process. However, these results of the study were not conclusive and there were a limited number of control variables used in the analysis.

Skryabin et al. ( 34 ) investigated how country-level ICT development and individual ICT usage was related to 4th- and 8th-grade student achievement in reading, mathematics, and science based on the data from TIMSS 2011, PIRLS 2011, and PISA 2012. The analysis revealed that country-level ICT development was a significant positive predictor for individual academic performance in all three subjects for both 4th- and 8th-grade students. After controlling for students’ gender and socioeconomic status, they found that country-level ICT development and student ICT use at home had a positive relationship with students’ academic performance; however, the ICT rate of change (measured by country’s recent shift in the ICT development index; International Telecomunications Union, 2012) had a negative association with students’ academic performance, but this link was not always significant for all subjects.

Early research by Wittwer and Senkbeil ( 51 ) investigated the role of using computers at home and school on student academic performance (based on PISA 2003 data). Their results suggested that, for the majority of students, the use of the computer at home or at school had no substantial influence on their academic achievement. However, more recently, Hu et al. ( 21 ) conducted research on how national ICT skills affected students’ academic performance (using PISA 2015 data). They found that ICT skills had a positive relationship with student academic performance and that ICT availability at school also had a positive relationship with students’ academic performance. In addition, the researchers found that student use of ICT for academic purposes had a positive relationship with student performance, whereas student use of ICT for entertainment purposes had a negative relationship. However, the study did not control for school SES and only focused on one year so could not draw conclusions across multiple cycles.

Wainer et al. ( 56 ) analysed the 2001 Brazilian Basic Education Evaluation System (SAEB) achievement exam for 4th-, 8th-, and 11th-grade students in mathematics and reading (Portuguese). The results suggested that the frequency of computer use had (1) a negative association with test results, and (2) a particularly high negative association with the test results of younger and lower-ability students. The researchers also identified that having internet access had a negative relationship with the academic performance of younger students, whilst this relationship was positive for older students. More recent research has explored the association between internet availability at school and home and student academic performance. Erdogdu and Erdogdu ( 9 ) explored the associations between access to ICT, student background, and school/home environment and students’ academic performance based on PISA 2012 data. While controlling for parental education level and socio-economic conditions (e.g., students’ having their own room), findings suggested that internet availability at home and at school was positively associated with students’ academic performance. Though, the specific relationships between availability and types of ICT use across the last decade have yet to be explored. Moreover, the nuanced associations between outside-of-school ICT use for leisure and social interaction for all countries has yet to be examined comprehensively in the literature.

Carrasco and Torrecilla ( 9 ), drawing upon PISA 2006 data, researched how computer access and use affected students’ academic performance. They found that computer access and use had a positive association with student performance. The researchers found that having a computer at home had a significant positive association with students’ reading and mathematics performance. Furthermore, Bulut, and Cutumisu ( 9 ) examined whether the use and availability of ICT at home and school was related to students’ academic success in the PISA 2012 mathematics and science-based assessments in Finland and Turkey. In both countries, they found that the use of ICT for mathematics lessons had a negative association with mathematics success; however, the general use of ICT at school had no substantive relationship with student performance in both mathematics and science. Finally, findings suggested that the use of ICT for entertainment had a positive association with students’ academic successes in Turkey while at the same time a negative association with students’ academic performances in Finland. Though, nuanced relationships for outside-of-school ICT use for leisure and social interaction for all countries has yet to be examined comprehensively. In another related study, Luu and Freeman ( 24 ) analysed the relationship between ICT use and scientific literacy across Canada and Australia based on PISA 2006 data. Their results suggested that students who browse the Internet more frequently and those who were more confident with basic ICT tasks earned higher scientific literacy scores. Though, more recent work in this area appears to be lacking.

Controversial findings in the associations between ICT related variables and students’ academic performance may have stemmed from the variety of PISA results across different nationalities, cycles, subjects, the combination of the variables chosen by the researchers, or the statistical approaches adopted by the researchers. For instance, ICT availability and use had a positive relationship with mathematics and science performance of Turkish students in PISA 2012 data, while it has either negative or no association with the performance of the Finnish student sample in the same study Bulut & Cutumisu 10 ). Similarly, using PISA 2015 data, Meng et al. ( 21 ) observed negative associations between mathematics and science performance of the Chinese and German students and their self-competence and interest in ICT, as opposed to the PISA results of the other countries. Juhaňák et al. ( 9 ) and Luu and Freeman ( 24 ) took into account the moderation effect of the frequency of ICT use on academic performance and found divergent results regarding the subgroups of students who used ICT at low, moderate, and high levels. They found that very low and very high usage of ICT had a negative association with academic performance. Biagi and Loi ( 7 ) found a positive association between ICT use for gaming and students’ academic performance. Petko et al. ( 34 ) argued that the controversial positive relationship could have resulted from an artefact of the method of analysis that Biagi and Loi ( 7 ) used. Rodrigues and Biagi’s ( 21 ) findings varied substantially by the combinations of type of school, frequency of ICT use, and ESCS (student economic, social, and cultural status) regarding the subgroups of the chosen variables. Through the econometric specification method they adopted, they regressed the students' performance on the different frequencies of ICT uses, while controlling for other variables that could be simultaneously associated with the dependent and independent variables. They found that low-frequency ICT users with mid to high ESCS benefit the most from an increased ICT use at school. They also reported that the positive association between ICT use at home for schoolwork and students’ science performance is stronger than those with low ESCS in private schools. In the current study, rather than comparing specific countries or testing any moderation or mediation effects, we use all the ICT related predictors of students’ mathematics and science performance using the complete data sets from the latest four PISA cycles to provide a comprehensive view of the subject matter.

The rationale for the current study

There is an increasing trend in the amount of research based on PISA data with interest in ICT skills and how these skills affect our students’ performances and other related constructs. Based on the rich research evidence, it was evident that ICT use can have a positive (small to moderate generally) association with students’ academic successes whilst it does depend on students’ purpose of using ICT, attitudes toward ICT, and the availability of ICT at both home and school.

Of the PISA studies reviewed, common independent variables pertained to ICT availability and use at school and home, with the strength of relationship between these variables and student academic performance sometimes dependent on the student sample and year of study. To date, little research has focused on the role of student competence in, attitude towards, interest in, and autonomous use of ICT. Moreover, to date, many studies have focussed on examining the role of ICT using a single PISA (and other single cycle large-scale assessment datasets such as TIMSS) and by taking a limited number of covariates into account (Luu & Freeman, 24 ; Erdogdu & Erdogdu, 9 ; Meng et al., 21 ; Odell, Galovan, & Cutumisu, 34 ; for TIMSS and PIRLS, see, for example, Grilli et al., 9 ). It should be noted that the cross-sectional nature of the PISA surveys makes longitudinal research impossible: i.e., the same cohort of students are not tracked longitudinally across time. However, for each cycle, attempts are made to ensure that samples are representative of the student group of interest, 15-year-olds, and questions pertaining to ICT are repeated opening the possibility for reasonable comparisons to be made across administrations.

We could only identify one example of research that focussed on five cycles of PISA. Zhang and Liu ( 21 ) investigated the role of ICT use on student performances for PISA cycles spanning 2000 to 2012. Research based on multiple PISA cycles over time provides a more holistic approach to highlighting and identifying the general situation of ICT use and attitude and its role in student learning. Therefore, the current research focuses on the last decade on PISA administrations and makes use of all ICT-related variables. Therefore, this study aims to explore the relationship between (1) ICT use and ICT related attitudes and (2) students’ math and science abilities measured in all the PISA surveys within the last decade. Besides, this study accounts for a wide range of covariates while undertaking the analyses at the student, school, and country levels. This was done to adjust for the confounding of associations of variables possibly related to both ICT-related use and students’ math and science performance. To note, Zhang and Liu ( 21 ) analysed the PISA surveys between 2000 and 2012 with a similar research question. However, in the 2015 and 2018 PISA cycles, several essential variables were added to the ICT surveys. To this point, in their scoping review, Odell, Galovan, and Cutumisu ( 34 ) noted that ICT as a topic in social interactions, interest in ICT, and autonomy in using ICT—variables added to the ICT survey in the latest two cycles—have been less studied concepts in the relevant literature. The current study makes further use of data from these two more recent cycles with the intention to provide updated and more comprehensive insights into the role of ICT use on student academic performance. Accordingly, the following three research questions are proposed for the current study:

RQ1: Can reasonable comparisons between ICT-related variables and control variables be made year-to-year for PISA 2009, 2012, 2015, and 2018? If not, what type of variable transformations might be usefully be applied to ensure this?

RQ2: What proportion of the variance in Math and Science can be attributed to within-school, between-school, and between-country effects?

RQ3: While controlling for student-, school-, and country-level confounding factors, what forms of student ICT-related attitude, accessibility, and school ICT-related infrastructure are associated with student performance in PISA Science and Math across PISA cycles?

Methodology

Participants.

The data for the current study was compiled from the previous four PISA cycles, which were made available from the OECD website. PISA is an international survey that has been conducted every three years since 2000. PISA aims to assess 15-year-old students’ science, math, and reading achievement scores, their various attitudes, behaviors, demographics, and other relevant contextual data from their parents and schools. For each of the four cycles, 2009, 2012, 2015, and 2018, both student and school data were merged. Each country had the option to have students and schools complete questions that measured the student- and school-level utility of, familiarity with, and attitude toward ICT. Because this survey was not obligatory, different numbers of countries opted to be involved in the ICT survey year-to-year. Accounting for this missing data, and after removing schools with fewer than ten students (Lai & Kwok, 9 ), total student sample sizes across the four cycles amounted to 247,352, 243.060, 194,399, and 212,652, respectively. The total number of schools was 9,123, 9,923, 7,726, and 8,261, respectively, while the total number of countries was 44, 43, 45, and 49, respectively. On average, there were 27.1, 24.5, 25.2, and 25.7 students in each school, respectively; and an average of 207.3, 230.8, 171.7, and 168.6 schools were sampled from each country, respectively.

In this study, a series of three-level models were used to examine the relationship between ICT-related variables and students’ academic performance. The plausible values of students’ math and science achievement scores were used as dependent variables in the models. The control and independent variables used at the country, school, and student levels are described below.

Country-level variables

There are inequalities in computer and internet use between countries, and this has been found to be related to countries’ socio-economic characteristics (Montagnier & Wirthmann, 24 ). As a prominent indicator of a country’s socio-economic level, each country’s GDP per capita score was taken from World Bank ( 34 ) and included in the model as an independent variable at the country-level. Therefore, in the current study, GDP per capita was considered an important independent variable of interest.

School-level variables

School-level ICT development indices were used as independent variables and several educational variables related to school infrastructure were also included as control variables at the between-school level.

For school-level ICT development, we included the ratio of available computers per student at modal grade (RATCMP1 in 2015 and 2018; RATCMP15 in 2012; IRATCMP in 2009), and the proportion of available computers that are connected to the Internet (RATCMP2 in 2015 and 2018; COMPWEB in 2009 and 2012; 0 = no computers in school online, 1 = all computers in school online).

We included the following six control variables: (1) “Shortage of educational material” (EDUSHORT in 2018 and 2015), (2) “Quality of educational resources” (SCMATEDU in 2012 and 2009), (3) School-level economic, social, and cultural status (ESCS) (aggregated from students’ ESCS scores), (4) School type (SCHLTYPE; 1 = Private; 2 = Public), (5) Creative extra-curricular activities (EXCURACT in 2009; CREACTIV in 2012, 2015, 2018, and (6) Shortage of educational staff (STAFFSHORT in 2015 and 2018; TCSHORT in 2009 and 2012).

Student-level variables

Like at the school-level, multiple independent and control variables of interest were included in all models.

For control variables, students’ economic, social, and cultural status (ESCS) and gender (1 = female; 2 = male) were used. ESCS is a composite score computed by three indices (OECD, 24 ): home possessions including books at home (HOMEPOS), highest parental education (PARED), and highest parental occupation (HISEI).

The independent variables related to ICT use can be classified into three categories: ICT use outside school , ICT use in school , and students’ attitudes toward ICT .

ICT availability at home (ICTHOME in all cycles), ICT use outside of school [leisure] (ENTUSE in all cycles), use of ICT outside of school [for schoolwork activities] (HOMESCH in all cycles), subject-related ICT use outside of lessons (ICTOUTSIDE in only 2018 PISA), and ICT as a topic in social interaction (SOIAICT in only 2015 and 2018 cycles) were the variables related to “ ICT use outside school .”

ICT availability at school (ICTSCH in all cycles), use of ICT at school in general (USESCH in all cycles), and subject-related ICT use during lessons (ICTCLASS only in 2018 PISA) were the variables related to “ ICT use in school .”

Self-confidence in ICT high-level tasks (HIGHCONF only in 2009 PISA), attitude towards computers (ATTCOMP only in 2009 PISA), limitations of a computer as a tool for school learning (ICTATTNEG only in 2012 PISA), attitudes towards computer as a tool for school learning (ICTATTPOS only in 2012 PISA), interest in ICT (INTICT only in 2015 and 2018 cycles), perceived ICT competence (COMPICT only in 2015 and 2018 cycles), and perceived autonomy related to ICT use (AUTICT only in 2015 and 2018 cycles) were the variables pertaining to “ studies attitudes toward ICI ” Footnote 1

Data adjustments

Dichotomous variables were dummy coded as follows: school type (SCHLTYPE: private = 1, public = 2) and student gender (GENDER: female = 1, male = 2). The variance for (1) GDP per capita, (2) the ratio of computers to students (RATCMP1), ICT available at home (ICTHOME), and ICT available in school (ICTSCH) was not consistent across the four cycles. For this reason, these variables were each also standardized prior to MLM analyses (see Table 1 ). In addition, the variable specifying the proportion of computers connected to the Internet (COMPWEB: none = 0, all = 1) was highly negatively skewed each cycle, so normalization procedures were undertaken in accordance with Courtney and Chang ( 9 ) (see Table 1 for selected descriptive statistics) prior to analysis. Decisions concerning the centering of predictor variables were made in accordance with Brincks et al. ( 8 ) and Lüdtke et al., 21 ). Specifically, we group mean center variables at the individual or school level when (1) student perception of the school environment was measured (e.g., perceived ICT use and availability inside schools, and (2) in the special case when the predictor has been computed by averaging the responses for all cases in each group (herein, ESCS). Further, because the school-level variables, STAFFSHORT, SCMATEDU, and EDUSHORT pertain to school principal perception (likely bound by comparative in-country perceptions), country-mean centering was applied to these variables.

To note, it was decided that the coefficients reported in the final linear mixed-effects models would be unstandardized. This decision was made so that the size of the coefficients would reflect the commonly understood metric in PISA, i.e., with the mean of approximately 500 and SDs of 100. While this is not exactly the case (see Table 1 , means of all PVs), means and standard deviations are approximately the same. It should also be noted that the Supplementary Materials (Additional file 1 : Table A1) provide definitions for each of the variables included in the study.

Use of sample weights

To ensure that each of the participating countries made an equal contribution to the study and to make the results of the study more generalizable internationally, we decided to make use of “senate weights” for all models. Because of missing data, the resultant sum of all student senate weights did not reach 5000. Therefore, the student senate weights for each country were multiplied by a constant such that the resultant sum of all student senate weight for the respective country came to 5000. The constant for each country was estimated in accordance with Eq.  1 :

where N is the total number of students included in the final analyses for each country after accounting for missing data.

The analysis was undertaken with the assistance of the open-source software, R (R Core Team, 44 ). The means and standard deviations for all variables are reported based on the observed sample data. The null and linear mixed effects modes made use of the lme4 (linear mixed-effects) package (Bates et al., 4 ) and lmer function. Analyses accounted for the three-level hierarchical structure of the data with students nested schools and schools nested in countries. All multilevel modeling analyses incorporated normalized weights so that the contribution from each of the countries in the analysis could be considered equal, regardless of their population or sample size (for PISA 2009, W_FSTUWT; for 2012, SENWGT_STU; for 2015 and 2018, SENWT were used). This way results of the study could be considered applicable to all participating countries. For each cycle, an initial exploration of the intra-class correlations (ICCs) for students’ Math and Science was followed by analyses of the aforementioned country-, school-, and student-level variables as fixed effects.

In accordance with Wu ( 44 ), analyses for each year and associated subjects were run with all available plausible values (PV1-5 for 2009–2012, and PV1-10 for 2015–2018). After implementing optimization algorithms in accordance with Nash and Ravi ( 34 ) and Bates et al. ( 3 ), all models converged successfully. All models used the maximum likelihood (ML) estimation.

Based on these results, mean coefficients, t values, and p values for each year-subject combination were then calculated for the models for all four years. With the trend toward more strict assessments of statistical significance (Benjamin et al., 6 ), and the large sample sizes associated with the PISA studies, a threshold of p  < 0.001 and b  = 2.00 (unstandardized shift in achievement/scale scores) was deemed as substantive at the student and school levels, while a threshold of p  < 0.05 was deemed of interest at the country level. Given the inclusion of multiple control variables in the models, we set the minimum association at 2 scale score points, though recognize that other researcher may propose different substantive limits depending on their study.

RQ1 asks whether or not reasonable comparisons between ICT-related variables and control variables can be made year-to-year. Results suggest that, after standardizing the three variables, namely RATCMP1, ICTHOME, and ICTSCH, the variance in each variable does not change substantially year-to-year. Therefore, it is argued that reasonable comparisons can be made across the four administrations (see Table 1 ).

RQ2 asks what proportion of the variance in Math and Science can be attributed to within-school, between-school, and between-country effects. The null models were run for both math and science achievement scores, using the available plausible values for each analysis. This was done to examine the extent to which student achievement differed significantly between schools and countries. Table 2 shows the intercepts, residuals, and intraclass correlation coefficients (ICCs) at school and country levels. For Math, country-level ICCs were quite stable across all cycles ranging from 0.199 to 0.214, while school-level ICCs dropped from levels slightly higher than 0.300 to slightly higher than 0.200 for the latter two cycles. Similarly, for science, country-level ICCs were quite stable with values ranging from 0.155 to 0.183 across all cycles while school-level ICCs dropped from approximately 0.300 and 0.310 in the first two cycles to approximately 0.220 and 0.210 for the last two cycles.

RQ3 asks, what forms of student ICT-related attitude, accessibility, and school ICT-related infrastructure are associated with student performance in PISA Science and Math across PISA cycles. After establishing substantive school- and country-level effects in RQ2, a series of three-level linear mixed-effect models, inclusive of the independent variables at the student-, school-, and country-levels, were run. A review of the final models in Tables 3 and 4 reveal that the independent variables explained up to 9.5% of the residual variance at the student-level, 61.8% of the residual variance at the school-level, and 34.8% of the residual variance at the country-level variance.

At the country-level, the GDP per capita of the countries involved in the ICT survey only had a statistically significant relationship with math and science achievement in 2012 ( b  = 13.21, p  < 0.01; b  = 10.98, respectively, p  < 0.05).

For both mathematics and science performance, results revealed that the overall variance explained at the school level tended to increase in the last two cycles (2015 and 2018). For math, variance explained at the school level grew from 50.7% in 2009 to 60.9% in 2018. Similarly, for science, variance explained grew from 50.5% in 2009 to 61.8% in 2018, with the variance explained due to school type appearing to become more substantive for both subjects.

Results revealed that the overall variance explained in science performance, at the within-school (student) level, increased from 6.5% in 2009 to 9.5% in 2018. It appears that the inclusion of variables in 2015 and 2018 pertaining to students’ perceived interest, competence, and autonomy in ICT provided substantive explanatory power for science performance. However, in comparison, the level of variance explained for math performance remained more constant across PISA cycles.

Tables 3 and 4 reveal that the direction of the relationships between the variables and the coefficients at all three levels appear to be quite consistent over the four cycles for both math and science ability. At the student level, two covariates, ESCS, and gender have strong positive association with both math and science achievement across all cycles. The ESCS effect indicates that the economic, social, and cultural advantages have a substantial relationship with students’ math and science achievement levels. The results for the gender variable means that, with females as the reference group, males have higher academic performance consistently across all the cycles.

ICT availability both at home and at school, and ICT use both inside and outside school—no matter the purpose of the students; for general, leisure, for schoolwork activities, or social interaction—was virtually always associated with either neutral or lower math and science performance for all cycles (with the single exception being Science, 2018, “ICT use outside of school, leisure”). For student use of ICT outside of school, substantive associations ( b  > 2.00; p  < 0.001) were quite consistently negative across all cycles with no instances of substantive positive associations. Similarly, for ICT use inside school, relationships were generally negative or neutral with no substantive positive relationships for either students’ math or science performance.

Students’ positive attitudes and beliefs toward ICT use have a substantive positive relationship with both their math and science performance for all cycles. The findings indicate that the more successful students have higher self-confidence in ICT high-level tasks, have more positive attitudes towards computers, more strongly believe in the usefulness of computers as a tool for school learning, are more interested in ICT, and perceive themselves more competent and autonomous in ICT use. In 2009 PISA, self-confidence in ICT had the highest relationship ( b math  = 5.94, b science  = 6.44, p  < 0.001) followed by positive attitudes toward computers ( b math  = 5.35, b science  = 5.25, p  < 0.001).

In the 2012 cycle, “attitudes towards computers: limitations of the computer as a tool for school learning” had the largest ICT-related relationship ( b math  = -10.30, b science  = -11.82, p  < 0.001). The scale measured the degree to which students “think that using computers for learning is troublesome and using the internet resources as a learning tool is not useful and suitable”, and this variable appeared to be associated with lower math and science performance. Conversely, this result also somewhat suggested that those “who believe that computers and Internet are useful tools for school learning” have higher achievement scores (2012; b math  = 2.07, b science  = 4.26, p  < 0.001).

In the 2015 and 2018 cycles, students’ perceived autonomy had the strongest association with academic performance ( b math(2015)  = 9.43, b math(2018)  = 8.93, b science(2015)  = 11.90, b science(2018)  = 10.20, p  < 0.001), reflecting the changing nature of the current educational settings in the way that students are more inclined to exert influence over their learning environments in order to increase their knowledge and abilities (Pellegrino, 24 ). Autonomy was followed by students’ interest in ICT ( b math(2015)  = 2.82, b math(2018)  = 3.65, b science(2015)  = 3.72, b science(2018)  = 5.06, p  < 0.001) and their perceived ICT competence ( b math(2015)  = 2.30, b math(2018)  = 2.63, p  < 0.01; and b science(2015)  = 2.71, b science(2018)  = 3.86, p  < 0.001).

In terms of ICT infrastructure, the number of available computers per student in the school appeared to have no substantive association with math and science performance for any cycle. However, the proportion of available computers connected to the net appeared to have generally positive associations (see exception for 2015, Math) with math and science performance ( b math(2009)  = 3.85, p  < 0.001; b math(2015)  = − 1.22, p  < 0.05; b math(2018)  = 2.96, p  < 0.05; b science(2009)  = 5.39, b science(2018)  = 4.26, p  < 0.001).

Incidentally, and as expected, at the school level, ESCS maintained the most substantive confounding association with student math and science performance for all cycles with coefficients for math between 63.67 to 74.61 ( p  < 0.001) and coefficients for science ranging between 66.80 to 71.98 ( p  < 0.001). Also incidentally, it is noted that a schools’ level of provision of extra-curricular activities appears to have an substantive, consistent, and positive associations with student math and science performance for all cycles. Finally, parenthetically, after accounting for the role of school socio-economic advantage and provision of extra-curricular activities, counterintuitively, school designation as a public institution appears to afford an advantage.

This study aimed to explore the role of student engagement with ICT technologies and the role of school ICT infrastructure on students’ math and science abilities for the last four PISA cycles (2009, 2012, 2015, and 2018). The results of this study drew upon multiple ICT-related PISA variables to provide insights into the changing role of ICT infrastructure and behavior on students’ academic performances in mathematics and science. Although studies using the PISA data from different countries revealed different patterns of relationships between ICT related variables and students’ math and science performance (Odell, Galovan, & Cutumisu, 34 ), the current study provides an overall view, taking all the participating countries into account across all PISA cycles spanning the last decade.

Country- and school- level effects

At the country-level, GDP per capita of the countries involved in the ICT survey only had an association with math and science achievement scores in 2012. Although the country-level ICCs suggested substantial differences in science and math achievement in the current study, GDP could not consistently explain the achievement gap between countries. Another variable, such as the “national ICT development level” that was not included in this study, could have provided some explanatory power for the achievement gap between countries, as explored by Skryabin et al. ( 34 ). As the number of participating countries increase in international large-scale assessment studies, more extensive work could be undertaken in this area.

At the school levels, counterintuitively, the number of available computers per student appeared to have no substantive association with school-level math and science performance for any cycle. This result concurs with early PISA studies on the topic. For example, Fuch and Ludger ( 9 ) found that, after controlling for family background and general school infrastructure, the availability of computers at schools had no statistically significant association with student academic performance. The authors posit that the relationship between school access to computers and performance may be more U-shaped. Therefore, more specific research into possible non-linear relationship is certainly in order here. However, the proportion of available school computers connected to the internet did have an expected positive relationship for both math and science in 2009 and 2018. Therefore school connectivity may be important, though this is not conclusive. Certainly, further international research into the role of school internet speed and student accessibility to websites (not necessarily used for learning) beyond simple proportion of computers connected should be explored in the future so to provide more pertinent insight of the digital divide in schools internationally (for a discussion, see Valadez & Duran, 21 ).

Within-school effects of ICT use and availability

In this section the current findings associated with the student-level effects of ICT use both (1) outside of school lessons, and (2) in school are discussed in contrast with the research literature. For convenience, the discussion is provided in the order of fixed effects presented in the Tables 3 and 4 .

In terms of within-school effects, there is a negative association between ICT availability at home and students’ math and science performance, as supported by previous studies (Hu et al., 21 ; Juhaňák et al., 9 ; Tan & Hew, 24 ). While some studies found a positive association between these two variables (Delen & Bulut, 9 ; Papanastasiou et al., 21 ; Srijamdee & Pholphirul, 44 ), others such as Juhaňák et al. ( 9 ) suggested no association. Also to note is that Bulut and Cutumisu ( 10 ) found positive relations for Turkish students but no relations for Finnish students. Considered broadly, the results here call into question the utility of unlimited availability of ICT materials at home and the possibility of distractive effects. It appears that unrestrained home access may have substantive detrimental relationship with adolescent academic learning.

ICT use outside of school for entertainment is associated with lower math and science performance in the current study, which is in line with the previous research findings (Bulut & Cutumisu, 10 , for Finnish students; Petko et al., 34 ; Skryabin et al., 34 , for math only; Juhaňák et al., 9 ; Luu & Freeman, 24 ; Rodrigues & Biagi, 21 , high-intensity users; Kunina-Habenicht & Goldhammer, 24 ). Therefore, these findings in the current study support the idea that, the frequency of use of ICT for entertainment, though outside of school, can place students at a disadvantage academically when student performance is contrasted with counterparts inside schools.

ICT use outside of school for schoolwork activities is negatively associated with math and science achievement in the current study, corroborating the findings of the previous studies (Carrasco & Torrecilla, 9 ; Skryabin et al., 34 ; Rodrigues & Biagi, 21 , medium and high users; Kunina-Habenicht & Goldhammer, 24 ; Hu et al., 21 ; Petko et al., 34 , only for science; Juhaňák et al., 9 , only for science). The findings here are somewhat troublesome given that the focus here is students’ frequency of computer use at home for school-related purposes. While counter-intuitive, it may be that use of such devices may involve a higher potential for distraction for the study period—the potential for distraction for which adolescent students may not manage well. However, we note that these effects are generally quite small ( b math(2012)  = − 0.40, p  < 0.01; b sci(2009)  = − 5.69, p  < 0.001) so further research is needed on this topic.

ICT as a topic in social interaction is also negatively associated with student math and science performance, further confirming previous findings (Carrasco & Torrecilla, 9 ; Rodrigues & Biagi, 21 ; Skryabin et al., 34 ). This finding comes as no surprise given that the index reflects the level of ICT use for interpersonal communication.

Finally, in terms of ICT-use outside of school lessons, students subject-related ICT use outside of lessons, defined as the extent to which students use UCT for specific subject-related tasks was also negatively associated with academic performance. This pattern is also revealing and confronting as even student ICT use focused on school work appears to also have a detrimental association with academic performance.

At this juncture, we turn to the role of ICT use in school itself for the four PISA cycles.

Findings in this study also reveal a negative association between ICT availability at school and students’ math and science performance, as supported by research by Koğar ( 21 ). Therefore, overall, and for the age-group of interest, ICT availability at school, akin to that at home, may also have a prominent distracting effect. Therefore, consistent negative associations for home and school use for both math and science across all PISA cycles may reveal the need to manage and constrain adolescent engagement with ICT devices and content.

ICT use at school, both in general and subject-related use during lessons , was associated with lower math and science performance for all cycles, confirming the results of previous studies (Erdogdu & Erdogdu, 9 ; Hu et al., 21 ; Juhaňák et al., 9 ; Luu & Freeman, 24 ; Petko et al., 34 ; Skryabin et al., 34 ; Bulut & Cutumisu, 10 ; Kunina-Habenicht et al., 24 ). Given the results above pertaining to ICT use and availability at school, it is understandable that involvement in ICT tasks at school might also be disruptive to student learning and development. However, here, year-by-year confirmation that student subject-related use is also associated with poor academic performance is quite confronting. This suggests that the integration of ICT for classroom activities may be associated with more damage than good.

Odell, Cutumisu, & Gierl ( 21 ) concluded in their scoping review of the secondary analyses of the PISA data that moderate use of ICT, rather than high or no use of it, may be positively associated with students’ math and science performance. However, our research here points to the consistent finding that ICT availability both at home and at school and ICT use both inside and outside school may be distractive for most students, decreasing their achievement levels. Even if they make use of ICT at school for subject-related purposes, it might be distractive and reduce their academic performance in science and math, subjects requiring focus and concentration to improve (Hu et al., 21 ). One explanation for this may be provided by Kunina-Habenicht and Goldhammer ( 24 ) who argue that more frequent use of ICT at school can be linked with remedial purposes for lower-performing students. Rodrigues and Biagi’s ( 21 ) findings are supportive of this contention by pointing out that high performers in math and science are the ones who use ICT at lower levels inside and outside school while the low performers are the ones who use ICT from medium to high levels.

Within-school effects of attitudes toward ICT

The most significant finding of this study is related to the role of the more recently fielded attitudinal variables in 2015 and 2016. Students’ positive attitudes and beliefs toward ICT use have a substantive positive influence on both their math and science performance for all cycles. More specifically, self-confidence in ICT high-level tasks, positive attitudes toward computers, belief in the usefulness of computers and the Internet as a tool for school learning, interest in ICT, perceived ICT competence, and perceived autonomy in ICT use appear to have a positive influence on students’ math and science performance. Previous studies have also found that successful students in math and science have more positive attitudes toward computers (Petko et al., 34 ; Tourón et al., 34 ), are more confident in ICT use (Guzeller & Akin, 9 ; Luu & Freeman, 24 ), are more interested in ICT use (Christoph et al., 9 ; Hu et al., 21 ; Meng et al., 21 ; Koğar, 21 ; Kunina-Habenicht & Goldhammer, 24 ), and feel more competent (Hu et al., 21 ; Koğar, 21 ; Kunina-Habenicht & Goldhammer, 24 ; Luu & Freeman, 24 ; Papanastasiou et al., 21 ; Srijamdee & Pholphirul, 44 ) and autonomous in using ICT (Hu et al., 21 ; Juhaňák et al., 9 ; Kunina-Habenicht & Goldhammer, 24 ; Meng et al., 21 ).

The findings of this study corroborate the assumptions of the self-determination theory and the ICT engagement concept (Deci & Ryan, 9 ; Goldhammer et al., 9 ), suggesting that academically successful students have a higher content-specific inner motivation related to ICT (ICT interest), more positive beliefs about their ICT knowledge and skills (ICT competence), and a feeling of self-directedness and control in ICT-related activities (autonomy). Given these relationships, it may be that there exists a cluster of student attributes associated with positive beliefs and attitudes around learning in ICT and in general. More work could be done to explore this. It should also be noted that the current findings posit that student enjoyment of social interaction around ICT has a negative influence on students’ math and science performance, confirming the findings of the previous studies (Hu et al., 21 ; Juhaňák et al., 9 ; Kunina-Habenicht & Goldhammer, 24 ; Meng et al., 21 ). In addition, it may be that lower performing students use ICT more often for social interaction to solve their school-related problems, such as requesting help from others instead of searching for written information, as was proposed by Kunina-Habenicht and Goldhammer ( 24 ).

Incidental findings

While students’ math and science performance in public schools was found to be lower than that in private schools, after controlling for the role of ESCS and the provision of extracurricular activities, school designation as a public institution appears to offer an advantage (Tourón et al., 34 ). This was a surprising result as it appears to be counter-intuitive. However, Zhang and Liu ( 21 ) findings also confirm the same pattern after controlling for ESCS. This finding appears to extend previous research that found no statistically significant relationship between private schooling and student performance in Australia (Nghiem et al., 21 ). Evidence in the current study of a consistent and growing reverse relationship (i.e., public school advantage, ceteris paribus ) for the past decade in PISA. On a speculative note, this pattern may be associated with the general, and perhaps inefficient, trend toward school privatization and socio-economic segregation (Lam et al., 21 ; Valenzuela et al., 24 ; Willms, 57 ). Finally, the provision of extra-curricular activities can be a critical complement to science and math performance that appears to consistently raise the learning bar and possibly ameliorate the role of socio-economic disadvantages (Willms, 57 ). Finally, our study adds to the growing body of literature on the role of gender for math and science performance. We note that boys tend to have a moderate advantage for math and more slight advantage for science, ceteris paribus.

The results of this study imply that the most substantial ICT-related predictor of students’ are an appropriate set of positive attitudes, competencies, and skills. In other words, the intensity or the quantity of ICT use itself may not make a difference, and the students may not realize the expected benefits if they do not use the ICT purposefully and consciously. These results are in line with the previous research findings suggesting that the quality of the ICT use is more predictive of students’ academic outcomes than the quantity (Lee & Wu, 24 ; Lei, 9 ; Petko et al., 34 ). Since ICT availability and ICT use have varying influences on students’ academic performance, educators and parents are recommended to be extra cautious in using ICT both inside and outside school. It can be helpful for educational leaders, teachers, and parents to invest more time in developing strategies for the students to effectively use educational technologies as a learning tool and to refrain from their distractive effects. The results also imply the importance of students’ positive attitudes and beliefs toward ICT and their interest in ICT for their math and science performance. Based on these results, teachers and parents are advised to nurture students’ positive attitudes and beliefs toward ICT to supplement learning and empower them to be self-competent and autonomous learners in order to improve their learning.

There are several limitations concerning the data used in this study, and they have implications for future studies. The cross-sectional nature of the PISA data set does not allow us to make direct causal inferences from the findings; instead, we intended to explore the associations between the selected variables. Other researchers can use experimental or longitudinal designs to better explore cause-and-effect relationships between those variables. The self-reported nature of the PISA data used in this study poses a methodological limitation that might provide an exaggerated or biased approximation of the ICT related attitudes and perceptions, and this might not give an accurate estimation of the ICT use. Other researchers can use different research designs and datasets that provide a more precise delineation of the ICT use and other ICT-related variables. Another limitation is that the complete set of items in the ICT questionnaire varied between different PISA cycles. It maybe that the items do not represent ICT-related behaviour in a comprehensive way in order to cover all aspects of the ICT related perceptions and attitudes or ICT use. In addition, all the results need to be interpreted in the context of the current research design (i.e., inclusion of specific country, school, and student-related variables). Therefore, researchers can use other data sets covering other ICT related variables such as teachers’ and parents’ perceptions and attitudes towards educational technologies, teacher support, or parental support in ICT use. Finally, future research that explores student accessibility to and attitude toward ICT during and after the recent schooling restrictions (associated with the pandemic) will also shed light on this field.

Availability of data and materials

The datasets analysed during the current study are available from the corresponding author on reasonable request.

All wording and meaning for all student- and school-level variables were equivalent across cycles. Table 1 provides further details.

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Dr. Matthew Courtney is an Associate Professor of Educational Assessment and Student Achievement. He has a broad interest in student and teacher development and enjoys applying quantitative methods to answer questions about education and learning. Dr Courtney has publications in peer-reviewed journals in the fields of assessment, higher education, cyber behavior and psychology, youth academic engagement, and quantitative research methods. He has developed extensive skills and experience in the application of IRT, multilevel modelling, VAM, and SEM models.

Dr. Mehmet Karakus is currently working as an Assistant Professor at the Research Centre for Global Learning, Coventry University, UK. Prior to that he worked at the Department of Higher Education, Graduate School of Education, Nazarbayev University, Kazakhstan. His main research interests are emotions in educational leadership, teacher psychology, equity and equality in education, quantitative methodology, multivariate analyses, and structural equation modeling in educational research.

Dr Ersozlu is a lecturer in Education (Maths Education) in the Faculty of Arts and Education. Dr Ersozlu’s research focuses on self-regulated learning (SRL), metacognition, reflective thinking, practice and teacher education. Her published papers have examined student’s metacognitive and cognitive strategy usages, how student’s metacognitive skills relate to their reflective thinking levels, the student teachers’ SRL behaviours, the problems in teacher education training, and self-regulated studying strategy used in instrumental learning.

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Courtney, M., Karakus, M., Ersozlu, Z. et al. The influence of ICT use and related attitudes on students’ math and science performance: multilevel analyses of the last decade’s PISA surveys. Large-scale Assess Educ 10 , 8 (2022). https://doi.org/10.1186/s40536-022-00128-6

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Enhancing the roles of information and communication technologies in doctoral research processes

• Sarah J. Stein   ORCID: orcid.org/0000-0003-0024-1675 1 &
• Kwong Nui Sim 2  

International Journal of Educational Technology in Higher Education volume  17 , Article number:  34 ( 2020 ) Cite this article

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While information and communication technologies (ICT) are prominent in educational practices at most levels of formal learning, there is relatively little known about the skills and understandings that underlie their effective and efficient use in research higher degree settings. This project aimed to identify doctoral supervisors’ and students’ perceptions of their roles in using ICT. Data were gathered through participative drawing and individual discussion sessions. Participants included 11 students and two supervisors from two New Zealand universities. Focus of the thematic analysis was on the views expressed by students about their ideas, practices and beliefs, in relation to their drawings. The major finding was that individuals hold assumptions and expectations about ICT and their use; they make judgements and take action based on those expectations and assumptions. Knowing about ICT and knowing about research processes separately form only part of the work of doctoral study. Just as supervision cannot be considered independently of the research project and the student involved, ICT skills and the use of ICT cannot be considered in the absence of the people and the project. What is more important in terms of facilitating the doctoral research process is students getting their “flow” right. This indicates a need to provide explicit support to enable students to embed ICT within their own research processes.

Background/context

Information and communication technologies (ICT) can bring either joy or challenge to well-versed academic practices, and either create barriers to learning and development or be the answer to needs. While some grasp and pursue opportunities to make use of various ICT for study, research and teaching, others struggle. Despite documented and anecdotal positive urges to adopt ICT to increase and improve efficiency and effectiveness, staff and students struggle experience ICT as needless and difficult-to-use interruptions. There is often little need seen to change practices by introducing ICT into ways of working. Exploring these views and experiences was the focus of this project. Being empathetic to views such as those expressed by Castañeda and Selwyn ( 2018 ), we did not approach this investigation from a position that assumes that ICT are natural and needed solutions to problems related to improving and facilitating effective learning, teaching and research. Rather, we took a more neutral stance, wishing to explore the experiences of those involved, namely, students and staff, through discussion with them about their ICT practices and views, and with a specific focus on doctoral study and supervision.

Doctoral supervision and the role, place and nature of the doctorate are receiving increasing attention in higher education research literature. A wide range of topics have been covered from, for example, the importance and types of support for students throughout candidature (e.g., Zhou & Okahana, 2019 ); to the teaching and supervision aspects of doctoral supervision (e.g., Åkerlind & McAlpine, 2017 ; Cotterall, 2011 ; Lee, 2008 ).

With advancements in, accessibility to, and development of, ICT within education settings has come a plethora of research into online and blended learning. These studies often highlight the capacity of ICT for facilitating teaching, learning and administrative activity within educational institutions and systems (e.g., Marshall & Shepherd, 2016 ). They cover numerous areas of importance from theoretical, practical, and philosophical angles and include the perspectives and needs of learners, educators and institutions (e.g., Nichols, Anderson, Campbell, & Thompson, 2014 ).

There are also studies on student use of ICT, though not necessarily doctoral students, and these cover a wide range of topics including specific ICT skills (e.g., Stensaker, Maassen, Borgan, Oftebro, & Karseth, 2007 ). Where postgraduate research students are concerned, some studies on ICT skill development and support provide some insights about students (e.g., Dowling & Wilson, 2017 ), and institutional ICT systems (Aghaee et al., 2016 ).

Notable about the many of these studies cited above is the use of self-reporting tools as mechanisms for gathering data about student use and views about ICT. While self-reports are valuable ways to collect such data about self-efficacy, they do have limits. In online learning environments, the role of self-efficacy, for example, is still being contested. It has been argued that learners from a variety of disciplines and learning settings will tend to overestimate claims about their performance and/or knowledge and skills (e.g., Mahmood, 2016 ).

All these studies help to ‘map the territory’ of ICT, their use at individual and institutional levels and related practices. Much advice and guidance can be gleaned from the literature as well, although relatively little for the specific integration of ICT within the doctoral research and supervision environment. Based on the literature that is available though, all indications are that (doctoral) students adopt educational practices incorporating limited ICT use, even though the use of ICT has grown enormously in the last 10 to 20 years. With the current interest in ensuring success of students and completion of doctoral degrees being closely related to high quality supervision, there is a need to improve supervision practices and within that, advance understandings about how to support students in their use of ICT for their doctoral research.

This project

This project aimed to explore doctoral student and supervisor views and use of ICT within the doctoral process. The intention was to bring to light perceptions that could give clues as to how to make practical modifications to the content and scope of professional development support for supervisors and students, in order to help them to make best use of ICT. In addition, consideration was given to the way data would be collected to ensure that more than just the self-reported perspectives of the participants were included.

An interpretivist research approach (Erickson, 2012 ) framed this study to support a focus on understanding the world from the perspectives of those who live it. Thus, the approach was well-suited to exploring perceptions about the use of ICT in our context.

Thus, this study did not commence with any hypotheses related to the influence of ICT in doctoral research in mind. Instead, as the interpretive frame of the research implies, this study investigated ways in which participants expressed their experiences of engaging and integrating ICT in support of their doctoral research processes. The data tapped into the participants’ (PhD students and doctoral supervisors) perspectives, as they expressed them. The research approach thus defined and shaped all aspects of the data gathering, analyses and presentation. In this way, alignment was ensured among the ontological, epistemological and practical implementation of the research project.

The study took place in two New Zealand universities where participants were either employees or students. Both universities are research-intensive, with histories of producing high-level research across many disciplines. Both institutions have clear and well-formulated policies and practices governing doctoral study - PhD and professional doctorate - and these include supporting that study through supervision. A specialised unit in each institution manages the administration of the doctoral degree. Couching “supervision” as essentially a (specialised) teaching activity, each unit also provides or coordinates professional development for staff in the art of supervision, and for students in the skills and processes of undertaking doctoral degree study.

Participants

Participants included doctoral students and supervisors from the two universities. As a result of an invitation to all students and supervisors, in total, 11 students and two supervisors responded. The students were PhD students at varying levels of completion. There was a mix of part time and full-time students from a variety of discipline backgrounds including health sciences, sciences, commerce and humanities. The supervisors were experienced and were from humanities and sciences.

Data sources

Data were collected using a 3-tier participative drawing process (Wetton & McWhirter, 1998 ). This strategy involved a series of two or three interview/discussions, along with participant-made drawings, which formed the focus of the interview/discussions.

This strategy generated two sources of data - interview transcripts and participant drawings – and involved the following (3-tier) phases:

Initial semi-structured interview/discussion to ascertain information about participants’ backgrounds and other details they saw relevant to share. In addition, they were asked about their use of ICT generally as well as within the doctoral process. It was a chance for the researchers to gain some understanding of participants’ views and practices in relation to ICT and their doctoral/supervision journeys.

Participant drawing . The participants were asked to make a drawing in their own time and before the second interview/discussion. Guidelines for the drawing suggested that they think of a way to illustrate their research process first, then to add onto the drawing any ICT (such as devices, websites, programmes, applications) that they make use of in the process.

Follow-up interview/discussion . During this phase, each participant was asked to explain the drawing’s features and how it made sense in terms of the project he or she was undertaking. This included discussion about how their supervision was working, how they worked with supervisors, and how the ICT they had included in the drawing worked within the process. They were also asked about elements that were not in the drawing, for example, certain ICT or activities that might have appeared in a typical account of a doctoral research process but were not included.

All interview/discussions were audio recorded and transcriptions of the recordings were returned to the participants for checking. The drawings were scanned and stored electronically.

In line with the interpretive approach that framed and governed our study, the data were analysed shortly after being gathered. Analysis of the data contributed to the development of ideas about participants’ perceptions, and these were refined progressively across the instances that researchers met with participants. Perceptions were thus checked, rechecked and refined against each data set.

This iterative and inductive approach (Thomas, 2006 ) involved thematic analysis (Silverman, 2001 ) and the capture of major and common ideas (Mayring, 2000 ) expressed by participants about how ICT are perceived and used in doctoral research processes. This approach helped to operationalise a process of co-construction between researchers and participants. Through checking, rechecking, refining and confirming, the researchers were able to articulate their understanding of participant perceptions that matched participants’ expressed thoughts.

The outcome of the analysis process was four assertions concerning ways the students perceived and understood ICT within doctoral study. Because there were only two supervisor participants, the data from the supervisors served to support the assertions we were more confidently able to make about student perceptions.

Research approach, quality assurance conditions and context

Despite the (what might be argued, small) number of volunteer participants who showed interest in, and committed themselves to, this study (i.e., no drop-outs or selection being made from a pool), it is worth noting that the researchers worked with each participant over an extended period of time (prolonged engagement), focused on investigating and gathering identifiable, as well as documentable, aspects of the participants’ ICT understandings and practices (persistent observation), and employed analysis techniques that incorporated peer debriefing, member checking, and fair presentation of assertions (Guba & Lincoln, 1989 ).

The aim was to unlock and identify views of reality held by the participants. The empirical evidence was used to help develop commentary and critique of the phenomenon which was the focus of the study (i.e., ICT use), including what the phenomenon is and how it occurs/is enacted/revealed in a particular context (viz., in doctoral research). This was, therefore, a different kind of study from one that might commence with a hypothesis, which would be concerned more with objectivity, explanation and testable propositions. In short, the methods employed in the current study fitted the intention to solve a “puzzle” about a phenomenon in relation to a particular context.

As this study involved human participants, ethical approval was gained through the institutional processes. This approval (University of Otago Human Ethics Committee reference number D17/414 and Victoria University of Wellington, Ethics Committee reference number 0000023415) enabled data collection methods described in the previous section to be carried out for any doctoral students and supervisors who volunteered to participate in this study. Ethical consent, use and care of the data as well as the ethical treatment of students and staff as participants were integral to the research design, planning and implementation of the whole study.

Findings and discussion

The four assertions are now presented. Each assertion is described and quotations from the interview/discussions along with examples of drawings from the student participants are used to illustrate aspects of each assertion.

Assertion 1: ICT are impartial tools; it does not matter how ICT are used, because the endpoint, that is, thesis completion, is the justification. ICT and people are separate and separated entities.

Students talked about how they worked on their thesis document and on the process of the study they were undertaking. Comments focused on various ICT being used and often on skills needed in order to use them. Some students expressed the view that ICT were tools, separate from the project and the person involved, to be used to achieve an endpoint. For example,

So long as it's formatted – it shouldn't matter - that's their [editors’] responsibility, not mine.

There’s probably a bit more about Zoom [web conferencing application] I could learn but again for me unless it’s a problem, I’m not going to go looking for it… not just for the sake of it at the moment.

Motivation to achieve an outcome was a focus of comments that support this assertion. For many participants, the aim to complete the study and write a thesis was, naturally, a large driver for how they were managing their study. Time was precious, and they would do what they had to do to reach their goal. To be motivated to learn about a new ICT, there needed to be a purpose that sharply focussed on achieving that end.

If the technologies are suddenly not available] I’m happy to sit down with a typewriter and learn it… If I’m not driven, I won’t bother.

This focus is illustrated in Fig.  1 . The drawing shows clearly identified components that make up major elements within the stages of producing the research for the thesis. ICT are listed in relation to those components.

ICT and people are separate and separated entities

Supervisors too, tended to focus on thesis production rather than on the process of producing a thesis that includes the use of ICT (i.e., as opposed to their very clear and explicit focus on the research process). An example illustrating this is:

Generally, people think the standard of the people getting or earning a PhD is that this person should be an independent researcher. [But no] After all, we only examine a particular thesis [and] there are lots of inputs from supports and supervision from supervisors.

In summary, this assertion focusses strongly on the experience of doctoral study being about getting the project done within a research journey that gives minimal regard to the affordances of ICT. ICT are framed as necessary but also fraught, especially due to the effort and time that draw attention away from the primary goal.

Assertion 2: ICT are tools or mechanisms that prompt active thought on practices with respect to planning and managing thesis writing and project execution. ICT and individuals work alongside each other.

Views that expressed notions of there being a close interactive relationship between students and ICT came through in several of the discussions with the participants. The focus on achieving goals and endpoints was strong, but the expression of how to achieve those goals, capitalising upon the affordances that ICT present, was different from the way views were expressed in relation to Assertion 1.

On a simple level, this student describes the checking he did when weighing up the merits of a piece of software to meet his needs.

I normally do a trial version… have a play with it. And if I think they are useful then I might try it on a project. And if then I feel it’s definitely worth investing… then I’ll go buy it.

Others simply liked to explore, to see whether there was potential in any ICT they encountered, as in,

Sometimes I just like playing with stuff to see what they can do and then if they tick my boxes then I keep them and if they don't, I move on. So it's more kind of ‘search and discover’ than kind of looking for something, you know.

Describing a deeper level of activity, a degree of critique and active reflection were indicated by another student when he said,

…we tried an electronic version of putting together a programme for a New Zealand conference and I was surprised how long it took us. Whereas in the past I’ve worked with [colleagues] and we’ve just moved pieces of paper around on the floor for abstracts and we were done really quickly.

These sentiments are well-captured in Fig.  2 . Here, the focus is on experimenting with ICT rather than the research process. The process of working things out to suit the individual is foregrounded.

ICT and individuals work alongside each other

Whereas Assertion 1-type expressions presented effort in a generally negative light, Assertion 2-type expressions couched effort as an assumed part of learning something new. There was a sense expressed in comments that there will be a way to manage the “problem” to be solved, which then generated the necessary motivation to engage effort. For example,

You just know what you know when you start off; when you're unsure about what you need to do. There's a bit of a barrier in front of you. It feels a bit intimidating and overwhelming, and then you get into it and it just works. And you just kind of put all the pieces together and get something out at the end.

There was a sense that supervisors’ perspectives of ICT might support this assertion too. For instance,

[ICT are] integral to everything now – there's no such thing as doing it without [them] anymore – these are the tools with which we do all the things we do.

In summary, this assertion captures the views of students who engage actively in making decisions about which, how and why they incorporate ICT into doctoral research practices.

Assertion 3: Knowing about ICT is only part of the thinking; what is more important is getting the “flow” right. ICT and the individual are in a complementary partnership.

Perhaps prompted by the nature of the drawing task, which was to illustrate how ICT fitted within the whole process of doctoral study, several students described the challenges to bringing everything together into one process made up of many parts, sections and subsections. One participant focussed on her “workflow” in order to manage the multiple documents, tasks and schedule involved in her doctoral research journey.

What systems do I use, what's my workflow? So, I actually spent some weeks looking at … ideas from other PhD students about their workflows and how they manage it.

Similar to Assertion 2-type comments, ‘getting one’s flow right’ involved exploration and an amount of reflective decision-making. For example,

So I did a play around with that [ICT] and found it was quite useful … So I’m trying to be quite disciplined about when I’ve got a document, entering it at the time, reading an article, throw in heaps of tags rather than not …And I simply keep a note, cross referencing to the actual articles. I like to have the articles and for some key ones I like to make a note. So, if it’s a seminal paper that I know I’ll be referring back to.

Thus, students talked about how hard they worked to set up routines and processes to enable them to manage time and their research projects. As in the above excerpts, they referred to categorising documents, searching for resources, undertaking analysis, managing data, and producing the thesis itself.

In working out one’s system or flow, this student highlighted the need to know about the affordances of ICT and how others had made use of them.

…you do need to know a bit about each of the individual … capabilities of the different systems to know what's even possible… but alongside that you're kind of reading other people's ideas of how they did it, and you think that bit might work for me oh, but that bit won't… so then you can kind of mix and match a bit.

The drawing in Fig.  3 highlights the “flow”. Absent of all words, this illustration draws attention to the movement of ideas, thoughts, processes and actions, from a number of different points but all ultimately converging or contributing to the one path.

ICT and the individual are in a complementary partnership

There was a hint that at least one of the supervisors saw the need for a workflow in this same vein: “So long as [the students are] happy with what they’re using – they should use ‘a’ system,”

In summary, this assertion highlights that what is important with respect to ICT and the doctoral process is how it all comes together within one’s flow. That flow incorporates active effort on the part of the individual in finding ICT and practices that suit the individual’s approaches as well as their project demands.

Assertion 4: ICT are not neutral; there is a two-way interaction between technologies as artefacts and the use of them to achieve ends. ICT and the person are intricately linked through multiple active, practical, goal-oriented connections.

This assertion draws attention to the nature of technology as a phenomenon; that technology is not an impartial tool that has no influence on the way humans act and react. This assertion presents ICT as an artefact of technological design activity; as a source of improving efforts to achieve an endpoint; but also as an influencer and even determiner of the thinking and practices of the person interacting with the ICT (e.g., Baird, 2002 ).

On what could be argued a superficial level, this student noted some active connection between the person and the software application, beyond simple use, when he commented:

I think it goes both ways, the product has to be intuitive and you’ve got to have a little bit of inclination to try out different things.

Others went beyond the superficial to describe more in-depth relationships between themselves and the ICT they were using. When discussing her use of software to help her manage her project and her time, this student talked about how the ICT she was using supported and enhanced her thinking.

Using the application] really changed the way I started to think about [my research]. I started to be less worried about the big overwhelming long term stuff that was out there and just think, okay, this week, what am I going to do this week, how am I going to be really efficient and targeted, and I think that really helped me.

Following is another example of how ICT helped solve a problem while simultaneously having an influence on behaviour; in this instance with organising notes, ideas and documents.

“… and it's the same with my note-taking because [the programme] that I use has a similar sort of functionality that it can search text that you've written but also search notes and PDF docs and those kind of things, so it means that when you've had a random thought and put it somewhere you can find it again. Which is huge for me, so I guess that … the power of the search engine is probably the thing that drove me to become paperless, so it helps me to organize myself much better. … filing paper is a skill that I have not mastered whereas filing digital stuff is not as important because you can always just find it again.

Figure  4 illustrates this intricately intertwined interactivity among person, purpose, project, ICT and outcomes.

ICT and the person are intricately linked through multiple active, practical, goal-oriented connections

While we did not find strong evidence for supervisors’ thoughts about this integrated and embedded notion of ICT, one supervisor did note “I could probably build them into my system, but I just never have”.

In summary, Assertion 4 highlights the integral role that ICT can be perceived to play in doctoral research processes. This is more than the working-alongside connection illustrated by Assertion 2 and the complementary partnership characterised by Assertion 3.

Assertions 1 and 2 highlight that individuals hold assumptions about, and have expectations of, ICT use; and those expectations and assumptions influence and determine their judgements about ICT and their use of ICT. The assertions point to connections between perceptions and practices. Assertion 1 describes a perception that ICT are separate from the person and the task-at-hand, while Assertion 2 presents a perception in which the person and the ICT are working alongside each other in harmony or at least in a loose partnership. Both assertions focus on endpoints, but the endpoints vary according to the perception of where ICT fit into the journey towards their achievement. For Assertion 1-type expressions, there is one major endpoint. For Assertion 2-type expressions, there are multiple, shorter-term endpoints that build towards achieving the major goal of completing the thesis.

Building on Assertions 1 and 2 are Assertions 3 and 4, which highlight what may be argued as more complex levels of perceiving and working with ICT. Both assertions give some focus to inter-connections, where people and ICT partner or collaborate. Assertion 3 depICT a perception that is about complementarity; where ICT affordances are seen as worthwhile when they support and enhance the work of the individual in ways that make sense to that individual. Assertion 4 builds on Assertion 3 by bringing to light the relationship in which the person alters and changes thinking or practices because of the influence that ICT affordances can have. No evidence was found to support a possible additional claim that as well as ICT causing individuals to alter and modify thinking and behaviours due to their existence, ICT, in turn, are perceived to be able to alter their ways of responding to the people who use them. This is not out of the realms of possibility of course, with ICT increasingly being designed and built to be able to respond to users’ needs.

It is also worth mentioning that the ‘types’ of ICT and the extent of their use by the participants was not the focus of this study. However, the findings suggested that the participants’ ICT use, regardless of their PhD phase and broad discipline background, might have reflected their inability to realise the advantages of learning how to use current ICT-related devices, tools, and applications to enhance the process of undertaking their doctoral research. The evidence that emerged in this study indicated that participants’ perspectives of ICT determined their adoption practices in general (i.e., as illustrated through the four assertions). The boarder higher education context including the specific institution and supervisors, might have neglected the explicit support of PhD students’ ICT capability development in this process.

In addition, while there is no similar study being found thus far, the insights gained from this study are actually similar to the findings in the research studies into the role of ICT in undergraduate education (Butson & Sim, 2013 ; Sim & Butson, 2013 , 2014 ). Results in those studies, demonstrated students’ low levels of ICT use, may be an indication that digital devices and digital tools do not play a significant role in daily study practices. Researchers such as Esposito, Sangrà & Maina ( 2013 ) also show that the PhD students’ learning to become researchers in the digital age is much more complex than is often suggested (e.g., the skills of Prenksy ( 2001 ) “digital natives”). Becoming a researcher involves developing a complex set of knowledge, intellectual abilities, techniques and professional standards. The Researcher Development Framework (Careers Research and Advisory Centre (CRAC), 2010 ) illustrates one useful attempt at mapping out that complexity. It could be that both students’ and supervisors’ adoption of ICT for academic purposes has been overshadowed or taken for granted as a consequence of their advanced academic level.

Implications

The four assertions can be used to provide some guidance to those supporting and participating in doctoral research processes. Students and supervisors do possess a vast array of skills, knowledge and abilities. They have a variety of experiences as well as varying reasons and levels of motivation. Their skills and capacity to make use of ICT to support their roles in the research process vary as well. The assertions that have emerged from this study will inform the planning for support activities to enhance supervisors’ and students’ professional development, whatever their background and needs.

Depending on the perceptions held about ICT and the relationship between ICT and the person in the context of the task and its goals (i.e., the doctoral study) within the doctoral research process as depicted in the four assertions, ICT tend to be seen as a challenge, a change or an opportunity. In the context of ICT use, doctoral students and supervisors may:

assume that if they do not already know how to use something it is not worth learning or exploring as that learning brings with it risk to quality, efficiency and effectiveness of the doctoral research process; and/or.

assume that students will work out the place that ICT play within the research process for themselves.

The findings of this study suggest the need to.

challenge existing ICT knowledge and skill, and to support acceptance of the need to change practices;

teach technological thinking, to enable choice and decision making about ICT;

embed ICT into practices in meaningful ways to suit individual and project needs;

highlight (explicit) responsibilities about thinking and planning skills with respect to making the best use of ICT, to ensure efficiency and effectiveness;

realise that the research process is as much about how it happens as what happens;

recast assumptions about the doctoral research process to embed ICT within it;

reflect on the meaning of effectiveness and efficiency in the context of doctoral research; and the effects of ICT in supporting and facilitating them;

understand that there is a link among ICT thinking and practice: using ICT can enhance or raise ideas that were never thought of before.

This study explored perceptions of doctoral supervisors and students of the role and place of ICT in supervision and study. It generated four assertions characterising those perceptions the relationships among people, ICT and the task-at-hand, that is, the supervised research process. As Castañeda and Selwyn ( 2018 ) argue, it is important that we have an active commitment to ‘think otherwise’ about how ICT might be better implemented across higher education settings” (p. 8). We should not assume that ICT are not important enough to let them fade into the background as they become normalised, without questioning the interrelationships that are happening between the person and the ICT. In the doctoral research setting, as one example of a higher education context, ICT do have a role to play. They cannot and should not be ignored. But seeing ICT in relationship to the person and to the setting is essential.

This project has provided insights into the doctoral students and supervisors’ perceptions of the roles played by ICT during doctoral research process. There are complex human factors, including assumptions, attitudes and conceptions about academic practices, influencing and determining perspectives as well as how ICT are incorporated into doctoral research process, behaviours and practices. Just as Kandiko and Kinchin ( 2012 ) argue that supervision cannot be looked at in the absence of the research work in which it occurs, we argue that doctoral students’ understanding and use of ICT cannot be considered independently of their research work; and that work includes relationships with their project, their supervisors, within the context of the institution, and with the ICT they do and could engage with.

Directly associated with the outcomes of this study, future studies and further exploration could focus on:

ICT use by larger and more diverse groups of doctoral students from a range of fields within discipline areas at institutions outside New Zealand;

building on the findings in order to determine how intensity of ICT use might change for students across the course of their candidature, and in relation to the nature of their research projects;

the role of supervisors, academic departments, and institutions in supporting and enhancing students’ practices and beliefs about ICT in research processes;

the ways in which supervisors engage ICT in their daily academic practices, with a view to exploring how, or if, their ICT use is an influence on PhD students’ beliefs and behaviours in using ICT.

Studying ICT in these directions could offer fresh perspectives and opportunities to think differently and reveal an active way of understanding the role of ICT in doctoral education.

Availability of data and materials

These are not available for open access as their access is bound by the ethical agreement approved by the two institutions and made with the participants in the study.

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Acknowledgements

We thank the students and supervisors who shared their reflections and willingly engaged with us in this project.

We acknowledge the support of Ako Aotearoa, The National Centre for Tertiary Teaching Excellence, New Zealand through its Regional Hub Project Fund (RHPF), and the support of our institutions, University of Otago and Victoria University of Wellington.

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Stein, S.J., Sim, K.N. Enhancing the roles of information and communication technologies in doctoral research processes. Int J Educ Technol High Educ 17 , 34 (2020). https://doi.org/10.1186/s41239-020-00212-3

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80 Information Technology Research Topics

FacebookXEmailWhatsAppRedditPinterestLinkedInWelcome to our blog post, where we invite students at all academic levels to discover compelling research topics in Information Technology (IT). If you are a student embarking on a journey to write your thesis or dissertation in Information Technology (IT), you have arrived at the perfect destination. Welcome to our comprehensive research topics list, […]

Welcome to our blog post, where we invite students at all academic levels to discover compelling research topics in Information Technology (IT). If you are a student embarking on a journey to write your thesis or dissertation in Information Technology (IT), you have arrived at the perfect destination. Welcome to our comprehensive research topics list, designed to aid you in selecting a compelling and innovative research topic that aligns with your academic pursuits and aspirations in Information Technology (IT).

Information technology is a dynamic field that offers many possibilities, from artificial intelligence and data science to cybersecurity and virtual reality. We aim to guide you in selecting thought-provoking research topics that align with your interests and expertise. Through rigorous research, you can contribute to groundbreaking advancements that shape the digital landscape and improve lives. Join us on this exciting journey as we explore new horizons in IT research and unleash your intellectual potential. We can create a future where innovation knows no bounds and technology makes positive change. Let the quest begin!

A List Of Potential Research Topics In Information Technology:

• The role of Internet of Things (IoT) in smart cities: a review of deployment and impact.
• Leveraging deep learning techniques for emotion recognition in human-computer interaction.
• A review of deep learning techniques in medical image analysis for disease diagnosis.
• Ethical implications of artificial intelligence in autonomous vehicles.
• Cybersecurity in healthcare: challenges and solutions for protecting patient data.
• Leveraging machine learning algorithms for personalized healthcare recommendations.
• Investigating the impact of IoT on energy efficiency in smart buildings.
• Leveraging machine learning for early detection of cybersecurity threats.
• Evaluating the challenges and opportunities of IoT in smart transportation systems.
• Evaluating the impact of social media algorithms on user behaviour and content consumption.
• Assessing the challenges of cybersecurity in cloud computing environments.
• Digital transformation and remote workforce management: lessons learned from the COVID-19 pandemic.
• Assessing the cybersecurity preparedness of UK healthcare institutions: a case study of NHS trusts.
• Assessing the challenges of data governance in the era of big data.
• Blockchain technology in supply chain management: a systematic review of applications and challenges.
• A review of data privacy and security concerns in online social networks.
• Reviewing the ethical implications of artificial intelligence in autonomous vehicles.
• Understanding the role of artificial intelligence in renewable energy management.
• Understanding the role of AI in detecting and preventing cybersecurity threats in critical infrastructures.
• Evaluating the impact of open banking on financial innovation and consumer protection in the UK.
• Understanding the role of artificial intelligence in enhancing customer experience in the UK retail sector.
• Exploring the role of artificial intelligence in personalized education and e-learning platforms.
• Evaluating the implications of deepfake technology on digital forensics.
• Understanding the role of AI in improving accessibility for people with disabilities.
• Anomaly detection in Internet of Things (IoT) networks using machine learning algorithms.
• Understanding the role of virtual reality in mental health therapy and treatment.
• Exploring the potential of blockchain in enhancing data integrity in electronic health records (EHRS).
• Investigating the use of natural language processing for sentiment analysis in social media.
• Leveraging data analytics for predictive maintenance in the aviation industry.
• Evaluating the ethical implications of AI-powered decision-making systems.
• Investigating the use of machine learning for medical image analysis and diagnosis.
• Investigating the use of AI in enhancing cybersecurity incident response.
• Improving data privacy in cloud computing through homomorphic encryption.
• The evolution of e-learning platforms: a review of user experience and effectiveness.
• The impact of telemedicine and virtual consultations on healthcare accessibility post-covid.
• Enhancing user experience in mobile applications through user-centric design and AI.
• The impact of remote work technologies on work-life balance: a systematic review.
• Digital health passports: privacy, security, and ethical considerations in a post-pandemic world.
• Exploring the potential of quantum computing for solving complex computational problems.
• Evaluating the impact of GDPR compliance on data privacy in UK organizations.
• Understanding the role of AI in detecting and preventing financial fraud.
• Exploring the integration of augmented reality in e-commerce shopping experiences.
• Assessing the ethical implications of AI in autonomous weapons systems.
• Evaluating the use of AI in predicting and preventing traffic accidents.
• Analyzing the impact of virtual reality on online shopping behaviour.
• Big data analytics for predictive maintenance in the industrial Internet of things (IoT).
• Optimizing network performance in 5g networks: a software-defined networking (sdn) approach.
• Enhancing privacy in smart homes through federated learning.
• Evaluating the ethical considerations of facial recognition technology in surveillance systems.
• E-commerce and supply chain resilience: adapting to the “New Normal” post-covid.
• Investigating the use of blockchain for supply chain traceability in the fashion industry.
• Enhancing cyber-physical security in smart cities using machine learning techniques.
• Exploring the challenges and opportunities of 5g network deployment in the UK.
• Analyzing the adoption and implementation of cloud computing in the UK’s small and medium-sized enterprises (SMEs).
• The role of artificial intelligence in healthcare delivery and disease surveillance post-covid.
• Understanding the potential of blockchain in digital identity management.
• Evaluating the effectiveness of AI in detecting and preventing cyberbullying.
• Exploring the potential of edge computing for real-time data processing in IoT applications.
• Assessing the impact of biometric authentication on mobile banking security.
• Assessing the adoption of digital health technologies in UK hospitals for remote patient monitoring.
• Investigating the use of AI-powered chatbots in customer service and support.
• Evaluating the potential of blockchain in intellectual property rights management.
• Blockchain-based decentralized identity management for enhanced user privacy.
• Cybersecurity challenges and strategies in the era of increased digitalization post-covid.
• Analyzing the role of data analytics in optimizing public transportation systems in London.
• Assessing the role of data analytics in personalized healthcare diagnosis and treatment.
• Enhancing e-commerce security through multi-factor authentication.
• Recent advancements in natural language processing and understanding: a comprehensive review.
• Understanding the challenges of cybersecurity in remote work environments.
• Investigating the use of blockchain in supply chain management to ensure transparency and traceability.
• Understanding the role of social media in influencing political discourse in the UK: a case study of general elections.
• Exploring the role of contactless technologies in enhancing customer experience post-covid.
• E-learning and online education: assessing the long-term implications of COVID-19 on educational systems.
• Leveraging data science techniques for predictive maintenance in industrial manufacturing.
• Enhancing cybersecurity in the Internet of Medical Things (IoMT) devices.
• A comprehensive review of cybersecurity threats and solutions in cloud computing environments.
• Reviewing the role of augmented reality and virtual reality in immersive user experiences.
• The future of events and conferences: hybrid and virtual models in a post-COVID era.
• Understanding the role of AI in enhancing personalized marketing and advertising.
• Exploring the challenges and opportunities of IoT in smart agriculture.

In conclusion, Information Technology offers an expansive landscape of research possibilities, catering to students pursuing dissertation research across different degree levels. From exploring cutting-edge AI and blockchain technology advancements to delving into the transformative impact of remote work and e-learning post-COVID, these research topics reflect the dynamic nature of Information Technology (IT) in our rapidly evolving digital age. As you embark on your academic journey, choose a research topic that aligns with your passions, interests, and career aspirations. Let your dissertation research be the catalyst for change and progress in the exciting realm of IT, empowering you to make a lasting impact on the global stage.

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Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

Stella timotheou.

1 CYENS Center of Excellence & Cyprus University of Technology (Cyprus Interaction Lab), Cyprus, CYENS Center of Excellence & Cyprus University of Technology, Nicosia-Limassol, Cyprus

Ourania Miliou

Yiannis dimitriadis.

2 Universidad de Valladolid (UVA), Spain, Valladolid, Spain

Sara Villagrá Sobrino

Nikoleta giannoutsou, romina cachia.

3 JRC - Joint Research Centre of the European Commission, Seville, Spain

Alejandra Martínez Monés

Andri ioannou, associated data.

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Digital technologies have brought changes to the nature and scope of education and led education systems worldwide to adopt strategies and policies for ICT integration. The latter brought about issues regarding the quality of teaching and learning with ICTs, especially concerning the understanding, adaptation, and design of the education systems in accordance with current technological trends. These issues were emphasized during the recent COVID-19 pandemic that accelerated the use of digital technologies in education, generating questions regarding digitalization in schools. Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses. Such results have engendered the need for schools to learn and build upon the experience to enhance their digital capacity and preparedness, increase their digitalization levels, and achieve a successful digital transformation. Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem, there is a need to show how these impacts are interconnected and identify the factors that can encourage an effective and efficient change in the school environments. For this purpose, we conducted a non-systematic literature review. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors that affect the schools’ digital capacity and digital transformation. The findings suggest that ICT integration in schools impacts more than just students’ performance; it affects several other school-related aspects and stakeholders, too. Furthermore, various factors affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the digital transformation process. The study results shed light on how ICTs can positively contribute to the digital transformation of schools and which factors should be considered for schools to achieve effective and efficient change.

Introduction

Digital technologies have brought changes to the nature and scope of education. Versatile and disruptive technological innovations, such as smart devices, the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR) and virtual reality (VR), blockchain, and software applications have opened up new opportunities for advancing teaching and learning (Gaol & Prasolova-Førland, 2021 ; OECD, 2021 ). Hence, in recent years, education systems worldwide have increased their investment in the integration of information and communication technology (ICT) (Fernández-Gutiérrez et al., 2020 ; Lawrence & Tar, 2018 ) and prioritized their educational agendas to adapt strategies or policies around ICT integration (European Commission, 2019 ). The latter brought about issues regarding the quality of teaching and learning with ICTs (Bates, 2015 ), especially concerning the understanding, adaptation, and design of education systems in accordance with current technological trends (Balyer & Öz, 2018 ). Studies have shown that despite the investment made in the integration of technology in schools, the results have not been promising, and the intended outcomes have not yet been achieved (Delgado et al., 2015 ; Lawrence & Tar, 2018 ). These issues were exacerbated during the COVID-19 pandemic, which forced teaching across education levels to move online (Daniel, 2020 ). Online teaching accelerated the use of digital technologies generating questions regarding the process, the nature, the extent, and the effectiveness of digitalization in schools (Cachia et al., 2021 ; König et al., 2020 ). Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses (Blaskó et al., 2021 ; Di Pietro et al, 2020 ). Such results have engendered the need for schools to learn and build upon the experience in order to enhance their digital capacity (European Commission, 2020 ) and increase their digitalization levels (Costa et al., 2021 ). Digitalization offers possibilities for fundamental improvement in schools (OECD, 2021 ; Rott & Marouane, 2018 ) and touches many aspects of a school’s development (Delcker & Ifenthaler, 2021 ) . However, it is a complex process that requires large-scale transformative changes beyond the technical aspects of technology and infrastructure (Pettersson, 2021 ). Namely, digitalization refers to “ a series of deep and coordinated culture, workforce, and technology shifts and operating models ” (Brooks & McCormack, 2020 , p. 3) that brings cultural, organizational, and operational change through the integration of digital technologies (JISC, 2020 ). A successful digital transformation requires that schools increase their digital capacity levels, establishing the necessary “ culture, policies, infrastructure as well as digital competence of students and staff to support the effective integration of technology in teaching and learning practices ” (Costa et al, 2021 , p.163).

Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem (Eng, 2005 ), there is a need to show how the different elements of the impact are interconnected and to identify the factors that can encourage an effective and efficient change in the school environment. To address the issues outlined above, we formulated the following research questions:

a) What is the impact of digital technologies on education?

b) Which factors might affect a school’s digital capacity and transformation?

In the present investigation, we conducted a non-systematic literature review of publications pertaining to the impact of digital technologies on education and the factors that affect a school’s digital capacity and transformation. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors which affect the schools’ digital capacity and digital transformation.

Methodology

The non-systematic literature review presented herein covers the main theories and research published over the past 17 years on the topic. It is based on meta-analyses and review papers found in scholarly, peer-reviewed content databases and other key studies and reports related to the concepts studied (e.g., digitalization, digital capacity) from professional and international bodies (e.g., the OECD). We searched the Scopus database, which indexes various online journals in the education sector with an international scope, to collect peer-reviewed academic papers. Furthermore, we used an all-inclusive Google Scholar search to include relevant key terms or to include studies found in the reference list of the peer-reviewed papers, and other key studies and reports related to the concepts studied by professional and international bodies. Lastly, we gathered sources from the Publications Office of the European Union ( https://op.europa.eu/en/home ); namely, documents that refer to policies related to digital transformation in education.

Regarding search terms, we first searched resources on the impact of digital technologies on education by performing the following search queries: “impact” OR “effects” AND “digital technologies” AND “education”, “impact” OR “effects” AND “ICT” AND “education”. We further refined our results by adding the terms “meta-analysis” and “review” or by adjusting the search options based on the features of each database to avoid collecting individual studies that would provide limited contributions to a particular domain. We relied on meta-analyses and review studies as these consider the findings of multiple studies to offer a more comprehensive view of the research in a given area (Schuele & Justice, 2006 ). Specifically, meta-analysis studies provided quantitative evidence based on statistically verifiable results regarding the impact of educational interventions that integrate digital technologies in school classrooms (Higgins et al., 2012 ; Tolani-Brown et al., 2011 ).

However, quantitative data does not offer explanations for the challenges or difficulties experienced during ICT integration in learning and teaching (Tolani-Brown et al., 2011 ). To fill this gap, we analyzed literature reviews and gathered in-depth qualitative evidence of the benefits and implications of technology integration in schools. In the analysis presented herein, we also included policy documents and reports from professional and international bodies and governmental reports, which offered useful explanations of the key concepts of this study and provided recent evidence on digital capacity and transformation in education along with policy recommendations. The inclusion and exclusion criteria that were considered in this study are presented in Table ​ Table1 1 .

Inclusion and exclusion criteria for the selection of resources on the impact of digital technologies on education

To ensure a reliable extraction of information from each study and assist the research synthesis we selected the study characteristics of interest (impact) and constructed coding forms. First, an overview of the synthesis was provided by the principal investigator who described the processes of coding, data entry, and data management. The coders followed the same set of instructions but worked independently. To ensure a common understanding of the process between coders, a sample of ten studies was tested. The results were compared, and the discrepancies were identified and resolved. Additionally, to ensure an efficient coding process, all coders participated in group meetings to discuss additions, deletions, and modifications (Stock, 1994 ). Due to the methodological diversity of the studied documents we began to synthesize the literature review findings based on similar study designs. Specifically, most of the meta-analysis studies were grouped in one category due to the quantitative nature of the measured impact. These studies tended to refer to student achievement (Hattie et al., 2014 ). Then, we organized the themes of the qualitative studies in several impact categories. Lastly, we synthesized both review and meta-analysis data across the categories. In order to establish a collective understanding of the concept of impact, we referred to a previous impact study by Balanskat ( 2009 ) which investigated the impact of technology in primary schools. In this context, the impact had a more specific ICT-related meaning and was described as “ a significant influence or effect of ICT on the measured or perceived quality of (parts of) education ” (Balanskat, 2009 , p. 9). In the study presented herein, the main impacts are in relation to learning and learners, teaching, and teachers, as well as other key stakeholders who are directly or indirectly connected to the school unit.

The study’s results identified multiple dimensions of the impact of digital technologies on students’ knowledge, skills, and attitudes; on equality, inclusion, and social integration; on teachers’ professional and teaching practices; and on other school-related aspects and stakeholders. The data analysis indicated various factors that might affect the schools’ digital capacity and transformation, such as digital competencies, the teachers’ personal characteristics and professional development, as well as the school’s leadership and management, administration, infrastructure, etc. The impacts and factors found in the literature review are presented below.

Impacts of digital technologies on students’ knowledge, skills, attitudes, and emotions

The impact of ICT use on students’ knowledge, skills, and attitudes has been investigated early in the literature. Eng ( 2005 ) found a small positive effect between ICT use and students' learning. Specifically, the author reported that access to computer-assisted instruction (CAI) programs in simulation or tutorial modes—used to supplement rather than substitute instruction – could enhance student learning. The author reported studies showing that teachers acknowledged the benefits of ICT on pupils with special educational needs; however, the impact of ICT on students' attainment was unclear. Balanskat et al. ( 2006 ) found a statistically significant positive association between ICT use and higher student achievement in primary and secondary education. The authors also reported improvements in the performance of low-achieving pupils. The use of ICT resulted in further positive gains for students, namely increased attention, engagement, motivation, communication and process skills, teamwork, and gains related to their behaviour towards learning. Evidence from qualitative studies showed that teachers, students, and parents recognized the positive impact of ICT on students' learning regardless of their competence level (strong/weak students). Punie et al. ( 2006 ) documented studies that showed positive results of ICT-based learning for supporting low-achieving pupils and young people with complex lives outside the education system. Liao et al. ( 2007 ) reported moderate positive effects of computer application instruction (CAI, computer simulations, and web-based learning) over traditional instruction on primary school student's achievement. Similarly, Tamim et al. ( 2011 ) reported small to moderate positive effects between the use of computer technology (CAI, ICT, simulations, computer-based instruction, digital and hypermedia) and student achievement in formal face-to-face classrooms compared to classrooms that did not use technology. Jewitt et al., ( 2011 ) found that the use of learning platforms (LPs) (virtual learning environments, management information systems, communication technologies, and information- and resource-sharing technologies) in schools allowed primary and secondary students to access a wider variety of quality learning resources, engage in independent and personalized learning, and conduct self- and peer-review; LPs also provide opportunities for teacher assessment and feedback. Similar findings were reported by Fu ( 2013 ), who documented a list of benefits and opportunities of ICT use. According to the author, the use of ICTs helps students access digital information and course content effectively and efficiently, supports student-centered and self-directed learning, as well as the development of a creative learning environment where more opportunities for critical thinking skills are offered, and promotes collaborative learning in a distance-learning environment. Higgins et al. ( 2012 ) found consistent but small positive associations between the use of technology and learning outcomes of school-age learners (5–18-year-olds) in studies linking the provision and use of technology with attainment. Additionally, Chauhan ( 2017 ) reported a medium positive effect of technology on the learning effectiveness of primary school students compared to students who followed traditional learning instruction.

The rise of mobile technologies and hardware devices instigated investigations into their impact on teaching and learning. Sung et al. ( 2016 ) reported a moderate effect on students' performance from the use of mobile devices in the classroom compared to the use of desktop computers or the non-use of mobile devices. Schmid et al. ( 2014 ) reported medium–low to low positive effects of technology integration (e.g., CAI, ICTs) in the classroom on students' achievement and attitude compared to not using technology or using technology to varying degrees. Tamim et al. ( 2015 ) found a low statistically significant effect of the use of tablets and other smart devices in educational contexts on students' achievement outcomes. The authors suggested that tablets offered additional advantages to students; namely, they reported improvements in students’ notetaking, organizational and communication skills, and creativity. Zheng et al. ( 2016 ) reported a small positive effect of one-to-one laptop programs on students’ academic achievement across subject areas. Additional reported benefits included student-centered, individualized, and project-based learning enhanced learner engagement and enthusiasm. Additionally, the authors found that students using one-to-one laptop programs tended to use technology more frequently than in non-laptop classrooms, and as a result, they developed a range of skills (e.g., information skills, media skills, technology skills, organizational skills). Haßler et al. ( 2016 ) found that most interventions that included the use of tablets across the curriculum reported positive learning outcomes. However, from 23 studies, five reported no differences, and two reported a negative effect on students' learning outcomes. Similar results were indicated by Kalati and Kim ( 2022 ) who investigated the effect of touchscreen technologies on young students’ learning. Specifically, from 53 studies, 34 advocated positive effects of touchscreen devices on children’s learning, 17 obtained mixed findings and two studies reported negative effects.

More recently, approaches that refer to the impact of gamification with the use of digital technologies on teaching and learning were also explored. A review by Pan et al. ( 2022 ) that examined the role of learning games in fostering mathematics education in K-12 settings, reported that gameplay improved students’ performance. Integration of digital games in teaching was also found as a promising pedagogical practice in STEM education that could lead to increased learning gains (Martinez et al., 2022 ; Wang et al., 2022 ). However, although Talan et al. ( 2020 ) reported a medium effect of the use of educational games (both digital and non-digital) on academic achievement, the effect of non-digital games was higher.

Over the last two years, the effects of more advanced technologies on teaching and learning were also investigated. Garzón and Acevedo ( 2019 ) found that AR applications had a medium effect on students' learning outcomes compared to traditional lectures. Similarly, Garzón et al. ( 2020 ) showed that AR had a medium impact on students' learning gains. VR applications integrated into various subjects were also found to have a moderate effect on students’ learning compared to control conditions (traditional classes, e.g., lectures, textbooks, and multimedia use, e.g., images, videos, animation, CAI) (Chen et al., 2022b ). Villena-Taranilla et al. ( 2022 ) noted the moderate effect of VR technologies on students’ learning when these were applied in STEM disciplines. In the same meta-analysis, Villena-Taranilla et al. ( 2022 ) highlighted the role of immersive VR, since its effect on students’ learning was greater (at a high level) across educational levels (K-6) compared to semi-immersive and non-immersive integrations. In another meta-analysis study, the effect size of the immersive VR was small and significantly differentiated across educational levels (Coban et al., 2022 ). The impact of AI on education was investigated by Su and Yang ( 2022 ) and Su et al. ( 2022 ), who showed that this technology significantly improved students’ understanding of AI computer science and machine learning concepts.

It is worth noting that the vast majority of studies referred to learning gains in specific subjects. Specifically, several studies examined the impact of digital technologies on students’ literacy skills and reported positive effects on language learning (Balanskat et al., 2006 ; Grgurović et al., 2013 ; Friedel et al., 2013 ; Zheng et al., 2016 ; Chen et al., 2022b ; Savva et al., 2022 ). Also, several studies documented positive effects on specific language learning areas, namely foreign language learning (Kao, 2014 ), writing (Higgins et al., 2012 ; Wen & Walters, 2022 ; Zheng et al., 2016 ), as well as reading and comprehension (Cheung & Slavin, 2011 ; Liao et al., 2007 ; Schwabe et al., 2022 ). ICTs were also found to have a positive impact on students' performance in STEM (science, technology, engineering, and mathematics) disciplines (Arztmann et al., 2022 ; Bado, 2022 ; Villena-Taranilla et al., 2022 ; Wang et al., 2022 ). Specifically, a number of studies reported positive impacts on students’ achievement in mathematics (Balanskat et al., 2006 ; Hillmayr et al., 2020 ; Li & Ma, 2010 ; Pan et al., 2022 ; Ran et al., 2022 ; Verschaffel et al., 2019 ; Zheng et al., 2016 ). Furthermore, studies documented positive effects of ICTs on science learning (Balanskat et al., 2006 ; Liao et al., 2007 ; Zheng et al., 2016 ; Hillmayr et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ; Lei et al., 2022a ). Çelik ( 2022 ) also noted that computer simulations can help students understand learning concepts related to science. Furthermore, some studies documented that the use of ICTs had a positive impact on students’ achievement in other subjects, such as geography, history, music, and arts (Chauhan, 2017 ; Condie & Munro, 2007 ), and design and technology (Balanskat et al., 2006 ).

More specific positive learning gains were reported in a number of skills, e.g., problem-solving skills and pattern exploration skills (Higgins et al., 2012 ), metacognitive learning outcomes (Verschaffel et al., 2019 ), literacy skills, computational thinking skills, emotion control skills, and collaborative inquiry skills (Lu et al., 2022 ; Su & Yang, 2022 ; Su et al., 2022 ). Additionally, several investigations have reported benefits from the use of ICT on students’ creativity (Fielding & Murcia, 2022 ; Liu et al., 2022 ; Quah & Ng, 2022 ). Lastly, digital technologies were also found to be beneficial for enhancing students’ lifelong learning skills (Haleem et al., 2022 ).

Apart from gaining knowledge and skills, studies also reported improvement in motivation and interest in mathematics (Higgins et. al., 2019 ; Fadda et al., 2022 ) and increased positive achievement emotions towards several subjects during interventions using educational games (Lei et al., 2022a ). Chen et al. ( 2022a ) also reported a small but positive effect of digital health approaches in bullying and cyberbullying interventions with K-12 students, demonstrating that technology-based approaches can help reduce bullying and related consequences by providing emotional support, empowerment, and change of attitude. In their meta-review study, Su et al. ( 2022 ) also documented that AI technologies effectively strengthened students’ attitudes towards learning. In another meta-analysis, Arztmann et al. ( 2022 ) reported positive effects of digital games on motivation and behaviour towards STEM subjects.

Impacts of digital technologies on equality, inclusion and social integration

Although most of the reviewed studies focused on the impact of ICTs on students’ knowledge, skills, and attitudes, reports were also made on other aspects in the school context, such as equality, inclusion, and social integration. Condie and Munro ( 2007 ) documented research interventions investigating how ICT can support pupils with additional or special educational needs. While those interventions were relatively small scale and mostly based on qualitative data, their findings indicated that the use of ICTs enabled the development of communication, participation, and self-esteem. A recent meta-analysis (Baragash et al., 2022 ) with 119 participants with different disabilities, reported a significant overall effect size of AR on their functional skills acquisition. Koh’s meta-analysis ( 2022 ) also revealed that students with intellectual and developmental disabilities improved their competence and performance when they used digital games in the lessons.

Istenic Starcic and Bagon ( 2014 ) found that the role of ICT in inclusion and the design of pedagogical and technological interventions was not sufficiently explored in educational interventions with people with special needs; however, some benefits of ICT use were found in students’ social integration. The issue of gender and technology use was mentioned in a small number of studies. Zheng et al. ( 2016 ) reported a statistically significant positive interaction between one-to-one laptop programs and gender. Specifically, the results showed that girls and boys alike benefitted from the laptop program, but the effect on girls’ achievement was smaller than that on boys’. Along the same lines, Arztmann et al. ( 2022 ) reported no difference in the impact of game-based learning between boys and girls, arguing that boys and girls equally benefited from game-based interventions in STEM domains. However, results from a systematic review by Cussó-Calabuig et al. ( 2018 ) found limited and low-quality evidence on the effects of intensive use of computers on gender differences in computer anxiety, self-efficacy, and self-confidence. Based on their view, intensive use of computers can reduce gender differences in some areas and not in others, depending on contextual and implementation factors.

Impacts of digital technologies on teachers’ professional and teaching practices

Various research studies have explored the impact of ICT on teachers’ instructional practices and student assessment. Friedel et al. ( 2013 ) found that the use of mobile devices by students enabled teachers to successfully deliver content (e.g., mobile serious games), provide scaffolding, and facilitate synchronous collaborative learning. The integration of digital games in teaching and learning activities also gave teachers the opportunity to study and apply various pedagogical practices (Bado, 2022 ). Specifically, Bado ( 2022 ) found that teachers who implemented instructional activities in three stages (pre-game, game, and post-game) maximized students’ learning outcomes and engagement. For instance, during the pre-game stage, teachers focused on lectures and gameplay training, at the game stage teachers provided scaffolding on content, addressed technical issues, and managed the classroom activities. During the post-game stage, teachers organized activities for debriefing to ensure that the gameplay had indeed enhanced students’ learning outcomes.

Furthermore, ICT can increase efficiency in lesson planning and preparation by offering possibilities for a more collaborative approach among teachers. The sharing of curriculum plans and the analysis of students’ data led to clearer target settings and improvements in reporting to parents (Balanskat et al., 2006 ).

Additionally, the use and application of digital technologies in teaching and learning were found to enhance teachers’ digital competence. Balanskat et al. ( 2006 ) documented studies that revealed that the use of digital technologies in education had a positive effect on teachers’ basic ICT skills. The greatest impact was found on teachers with enough experience in integrating ICTs in their teaching and/or who had recently participated in development courses for the pedagogical use of technologies in teaching. Punie et al. ( 2006 ) reported that the provision of fully equipped multimedia portable computers and the development of online teacher communities had positive impacts on teachers’ confidence and competence in the use of ICTs.

Moreover, online assessment via ICTs benefits instruction. In particular, online assessments support the digitalization of students’ work and related logistics, allow teachers to gather immediate feedback and readjust to new objectives, and support the improvement of the technical quality of tests by providing more accurate results. Additionally, the capabilities of ICTs (e.g., interactive media, simulations) create new potential methods of testing specific skills, such as problem-solving and problem-processing skills, meta-cognitive skills, creativity and communication skills, and the ability to work productively in groups (Punie et al., 2006 ).

Impacts of digital technologies on other school-related aspects and stakeholders

There is evidence that the effective use of ICTs and the data transmission offered by broadband connections help improve administration (Balanskat et al., 2006 ). Specifically, ICTs have been found to provide better management systems to schools that have data gathering procedures in place. Condie and Munro ( 2007 ) reported impacts from the use of ICTs in schools in the following areas: attendance monitoring, assessment records, reporting to parents, financial management, creation of repositories for learning resources, and sharing of information amongst staff. Such data can be used strategically for self-evaluation and monitoring purposes which in turn can result in school improvements. Additionally, they reported that online access to other people with similar roles helped to reduce headteachers’ isolation by offering them opportunities to share insights into the use of ICT in learning and teaching and how it could be used to support school improvement. Furthermore, ICTs provided more efficient and successful examination management procedures, namely less time-consuming reporting processes compared to paper-based examinations and smooth communications between schools and examination authorities through electronic data exchange (Punie et al., 2006 ).

Zheng et al. ( 2016 ) reported that the use of ICTs improved home-school relationships. Additionally, Escueta et al. ( 2017 ) reported several ICT programs that had improved the flow of information from the school to parents. Particularly, they documented that the use of ICTs (learning management systems, emails, dedicated websites, mobile phones) allowed for personalized and customized information exchange between schools and parents, such as attendance records, upcoming class assignments, school events, and students’ grades, which generated positive results on students’ learning outcomes and attainment. Such information exchange between schools and families prompted parents to encourage their children to put more effort into their schoolwork.

The above findings suggest that the impact of ICT integration in schools goes beyond students’ performance in school subjects. Specifically, it affects a number of school-related aspects, such as equality and social integration, professional and teaching practices, and diverse stakeholders. In Table ​ Table2, 2 , we summarize the different impacts of digital technologies on school stakeholders based on the literature review, while in Table ​ Table3 3 we organized the tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript.

The impact of digital technologies on schools’ stakeholders based on the literature review

Tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript

Additionally, based on the results of the literature review, there are many types of digital technologies with different affordances (see, for example, studies on VR vs Immersive VR), which evolve over time (e.g. starting from CAIs in 2005 to Augmented and Virtual reality 2020). Furthermore, these technologies are linked to different pedagogies and policy initiatives, which are critical factors in the study of impact. Table ​ Table3 3 summarizes the different tools and practices that have been used to examine the impact of digital technologies on education since 2005 based on the review results.

Factors that affect the integration of digital technologies

Although the analysis of the literature review demonstrated different impacts of the use of digital technology on education, several authors highlighted the importance of various factors, besides the technology itself, that affect this impact. For example, Liao et al. ( 2007 ) suggested that future studies should carefully investigate which factors contribute to positive outcomes by clarifying the exact relationship between computer applications and learning. Additionally, Haßler et al., ( 2016 ) suggested that the neutral findings regarding the impact of tablets on students learning outcomes in some of the studies included in their review should encourage educators, school leaders, and school officials to further investigate the potential of such devices in teaching and learning. Several other researchers suggested that a number of variables play a significant role in the impact of ICTs on students’ learning that could be attributed to the school context, teaching practices and professional development, the curriculum, and learners’ characteristics (Underwood, 2009 ; Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Tang et al., 2022 ).

Digital competencies

One of the most common challenges reported in studies that utilized digital tools in the classroom was the lack of students’ skills on how to use them. Fu ( 2013 ) found that students’ lack of technical skills is a barrier to the effective use of ICT in the classroom. Tamim et al. ( 2015 ) reported that students faced challenges when using tablets and smart mobile devices, associated with the technical issues or expertise needed for their use and the distracting nature of the devices and highlighted the need for teachers’ professional development. Higgins et al. ( 2012 ) reported that skills training about the use of digital technologies is essential for learners to fully exploit the benefits of instruction.

Delgado et al. ( 2015 ), meanwhile, reported studies that showed a strong positive association between teachers’ computer skills and students’ use of computers. Teachers’ lack of ICT skills and familiarization with technologies can become a constraint to the effective use of technology in the classroom (Balanskat et al., 2006 ; Delgado et al., 2015 ).

It is worth noting that the way teachers are introduced to ICTs affects the impact of digital technologies on education. Previous studies have shown that teachers may avoid using digital technologies due to limited digital skills (Balanskat, 2006 ), or they prefer applying “safe” technologies, namely technologies that their own teachers used and with which they are familiar (Condie & Munro, 2007 ). In this regard, the provision of digital skills training and exposure to new digital tools might encourage teachers to apply various technologies in their lessons (Condie & Munro, 2007 ). Apart from digital competence, technical support in the school setting has also been shown to affect teachers’ use of technology in their classrooms (Delgado et al., 2015 ). Ferrari et al. ( 2011 ) found that while teachers’ use of ICT is high, 75% stated that they needed more institutional support and a shift in the mindset of educational actors to achieve more innovative teaching practices. The provision of support can reduce time and effort as well as cognitive constraints, which could cause limited ICT integration in the school lessons by teachers (Escueta et al., 2017 ).

Teachers’ personal characteristics, training approaches, and professional development

Teachers’ personal characteristics and professional development affect the impact of digital technologies on education. Specifically, Cheok and Wong ( 2015 ) found that teachers’ personal characteristics (e.g., anxiety, self-efficacy) are associated with their satisfaction and engagement with technology. Bingimlas ( 2009 ) reported that lack of confidence, resistance to change, and negative attitudes in using new technologies in teaching are significant determinants of teachers’ levels of engagement in ICT. The same author reported that the provision of technical support, motivation support (e.g., awards, sufficient time for planning), and training on how technologies can benefit teaching and learning can eliminate the above barriers to ICT integration. Archer et al. ( 2014 ) found that comfort levels in using technology are an important predictor of technology integration and argued that it is essential to provide teachers with appropriate training and ongoing support until they are comfortable with using ICTs in the classroom. Hillmayr et al. ( 2020 ) documented that training teachers on ICT had an important effecton students’ learning.

According to Balanskat et al. ( 2006 ), the impact of ICTs on students’ learning is highly dependent on the teachers’ capacity to efficiently exploit their application for pedagogical purposes. Results obtained from the Teaching and Learning International Survey (TALIS) (OECD, 2021 ) revealed that although schools are open to innovative practices and have the capacity to adopt them, only 39% of teachers in the European Union reported that they are well or very well prepared to use digital technologies for teaching. Li and Ma ( 2010 ) and Hardman ( 2019 ) showed that the positive effect of technology on students’ achievement depends on the pedagogical practices used by teachers. Schmid et al. ( 2014 ) reported that learning was best supported when students were engaged in active, meaningful activities with the use of technological tools that provided cognitive support. Tamim et al. ( 2015 ) compared two different pedagogical uses of tablets and found a significant moderate effect when the devices were used in a student-centered context and approach rather than within teacher-led environments. Similarly, Garzón and Acevedo ( 2019 ) and Garzón et al. ( 2020 ) reported that the positive results from the integration of AR applications could be attributed to the existence of different variables which could influence AR interventions (e.g., pedagogical approach, learning environment, and duration of the intervention). Additionally, Garzón et al. ( 2020 ) suggested that the pedagogical resources that teachers used to complement their lectures and the pedagogical approaches they applied were crucial to the effective integration of AR on students’ learning gains. Garzón and Acevedo ( 2019 ) also emphasized that the success of a technology-enhanced intervention is based on both the technology per se and its characteristics and on the pedagogical strategies teachers choose to implement. For instance, their results indicated that the collaborative learning approach had the highest impact on students’ learning gains among other approaches (e.g., inquiry-based learning, situated learning, or project-based learning). Ran et al. ( 2022 ) also found that the use of technology to design collaborative and communicative environments showed the largest moderator effects among the other approaches.

Hattie ( 2008 ) reported that the effective use of computers is associated with training teachers in using computers as a teaching and learning tool. Zheng et al. ( 2016 ) noted that in addition to the strategies teachers adopt in teaching, ongoing professional development is also vital in ensuring the success of technology implementation programs. Sung et al. ( 2016 ) found that research on the use of mobile devices to support learning tends to report that the insufficient preparation of teachers is a major obstacle in implementing effective mobile learning programs in schools. Friedel et al. ( 2013 ) found that providing training and support to teachers increased the positive impact of the interventions on students’ learning gains. Trucano ( 2005 ) argued that positive impacts occur when digital technologies are used to enhance teachers’ existing pedagogical philosophies. Higgins et al. ( 2012 ) found that the types of technologies used and how they are used could also affect students’ learning. The authors suggested that training and professional development of teachers that focuses on the effective pedagogical use of technology to support teaching and learning is an important component of successful instructional approaches (Higgins et al., 2012 ). Archer et al. ( 2014 ) found that studies that reported ICT interventions during which teachers received training and support had moderate positive effects on students’ learning outcomes, which were significantly higher than studies where little or no detail about training and support was mentioned. Fu ( 2013 ) reported that the lack of teachers’ knowledge and skills on the technical and instructional aspects of ICT use in the classroom, in-service training, pedagogy support, technical and financial support, as well as the lack of teachers’ motivation and encouragement to integrate ICT on their teaching were significant barriers to the integration of ICT in education.

School leadership and management

Management and leadership are important cornerstones in the digital transformation process (Pihir et al., 2018 ). Zheng et al. ( 2016 ) documented leadership among the factors positively affecting the successful implementation of technology integration in schools. Strong leadership, strategic planning, and systematic integration of digital technologies are prerequisites for the digital transformation of education systems (Ređep, 2021 ). Management and leadership play a significant role in formulating policies that are translated into practice and ensure that developments in ICT become embedded into the life of the school and in the experiences of staff and pupils (Condie & Munro, 2007 ). Policy support and leadership must include the provision of an overall vision for the use of digital technologies in education, guidance for students and parents, logistical support, as well as teacher training (Conrads et al., 2017 ). Unless there is a commitment throughout the school, with accountability for progress at key points, it is unlikely for ICT integration to be sustained or become part of the culture (Condie & Munro, 2007 ). To achieve this, principals need to adopt and promote a whole-institution strategy and build a strong mutual support system that enables the school’s technological maturity (European Commission, 2019 ). In this context, school culture plays an essential role in shaping the mindsets and beliefs of school actors towards successful technology integration. Condie and Munro ( 2007 ) emphasized the importance of the principal’s enthusiasm and work as a source of inspiration for the school staff and the students to cultivate a culture of innovation and establish sustainable digital change. Specifically, school leaders need to create conditions in which the school staff is empowered to experiment and take risks with technology (Elkordy & Lovinelli, 2020 ).

In order for leaders to achieve the above, it is important to develop capacities for learning and leading, advocating professional learning, and creating support systems and structures (European Commission, 2019 ). Digital technology integration in education systems can be challenging and leadership needs guidance to achieve it. Such guidance can be introduced through the adoption of new methods and techniques in strategic planning for the integration of digital technologies (Ređep, 2021 ). Even though the role of leaders is vital, the relevant training offered to them has so far been inadequate. Specifically, only a third of the education systems in Europe have put in place national strategies that explicitly refer to the training of school principals (European Commission, 2019 , p. 16).

Connectivity, infrastructure, and government and other support

The effective integration of digital technologies across levels of education presupposes the development of infrastructure, the provision of digital content, and the selection of proper resources (Voogt et al., 2013 ). Particularly, a high-quality broadband connection in the school increases the quality and quantity of educational activities. There is evidence that ICT increases and formalizes cooperative planning between teachers and cooperation with managers, which in turn has a positive impact on teaching practices (Balanskat et al., 2006 ). Additionally, ICT resources, including software and hardware, increase the likelihood of teachers integrating technology into the curriculum to enhance their teaching practices (Delgado et al., 2015 ). For example, Zheng et al. ( 2016 ) found that the use of one-on-one laptop programs resulted in positive changes in teaching and learning, which would not have been accomplished without the infrastructure and technical support provided to teachers. Delgado et al. ( 2015 ) reported that limited access to technology (insufficient computers, peripherals, and software) and lack of technical support are important barriers to ICT integration. Access to infrastructure refers not only to the availability of technology in a school but also to the provision of a proper amount and the right types of technology in locations where teachers and students can use them. Effective technical support is a central element of the whole-school strategy for ICT (Underwood, 2009 ). Bingimlas ( 2009 ) reported that lack of technical support in the classroom and whole-school resources (e.g., failing to connect to the Internet, printers not printing, malfunctioning computers, and working on old computers) are significant barriers that discourage the use of ICT by teachers. Moreover, poor quality and inadequate hardware maintenance, and unsuitable educational software may discourage teachers from using ICTs (Balanskat et al., 2006 ; Bingimlas, 2009 ).

Government support can also impact the integration of ICTs in teaching. Specifically, Balanskat et al. ( 2006 ) reported that government interventions and training programs increased teachers’ enthusiasm and positive attitudes towards ICT and led to the routine use of embedded ICT.

Lastly, another important factor affecting digital transformation is the development and quality assurance of digital learning resources. Such resources can be support textbooks and related materials or resources that focus on specific subjects or parts of the curriculum. Policies on the provision of digital learning resources are essential for schools and can be achieved through various actions. For example, some countries are financing web portals that become repositories, enabling teachers to share resources or create their own. Additionally, they may offer e-learning opportunities or other services linked to digital education. In other cases, specific agencies of projects have also been set up to develop digital resources (Eurydice, 2019 ).

Administration and digital data management

The digital transformation of schools involves organizational improvements at the level of internal workflows, communication between the different stakeholders, and potential for collaboration. Vuorikari et al. ( 2020 ) presented evidence that digital technologies supported the automation of administrative practices in schools and reduced the administration’s workload. There is evidence that digital data affects the production of knowledge about schools and has the power to transform how schooling takes place. Specifically, Sellar ( 2015 ) reported that data infrastructure in education is developing due to the demand for “ information about student outcomes, teacher quality, school performance, and adult skills, associated with policy efforts to increase human capital and productivity practices ” (p. 771). In this regard, practices, such as datafication which refers to the “ translation of information about all kinds of things and processes into quantified formats” have become essential for decision-making based on accountability reports about the school’s quality. The data could be turned into deep insights about education or training incorporating ICTs. For example, measuring students’ online engagement with the learning material and drawing meaningful conclusions can allow teachers to improve their educational interventions (Vuorikari et al., 2020 ).

Students’ socioeconomic background and family support

Research show that the active engagement of parents in the school and their support for the school’s work can make a difference to their children’s attitudes towards learning and, as a result, their achievement (Hattie, 2008 ). In recent years, digital technologies have been used for more effective communication between school and family (Escueta et al., 2017 ). The European Commission ( 2020 ) presented data from a Eurostat survey regarding the use of computers by students during the pandemic. The data showed that younger pupils needed additional support and guidance from parents and the challenges were greater for families in which parents had lower levels of education and little to no digital skills.

In this regard, the socio-economic background of the learners and their socio-cultural environment also affect educational achievements (Punie et al., 2006 ). Trucano documented that the use of computers at home positively influenced students’ confidence and resulted in more frequent use at school, compared to students who had no home access (Trucano, 2005 ). In this sense, the socio-economic background affects the access to computers at home (OECD, 2015 ) which in turn influences the experience of ICT, an important factor for school achievement (Punie et al., 2006 ; Underwood, 2009 ). Furthermore, parents from different socio-economic backgrounds may have different abilities and availability to support their children in their learning process (Di Pietro et al., 2020 ).

Schools’ socioeconomic context and emergency situations

The socio-economic context of the school is closely related to a school’s digital transformation. For example, schools in disadvantaged, rural, or deprived areas are likely to lack the digital capacity and infrastructure required to adapt to the use of digital technologies during emergency periods, such as the COVID-19 pandemic (Di Pietro et al., 2020 ). Data collected from school principals confirmed that in several countries, there is a rural/urban divide in connectivity (OECD, 2015 ).

Emergency periods also affect the digitalization of schools. The COVID-19 pandemic led to the closure of schools and forced them to seek appropriate and connective ways to keep working on the curriculum (Di Pietro et al., 2020 ). The sudden large-scale shift to distance and online teaching and learning also presented challenges around quality and equity in education, such as the risk of increased inequalities in learning, digital, and social, as well as teachers facing difficulties coping with this demanding situation (European Commission, 2020 ).

Looking at the findings of the above studies, we can conclude that the impact of digital technologies on education is influenced by various actors and touches many aspects of the school ecosystem. Figure  1 summarizes the factors affecting the digital technologies’ impact on school stakeholders based on the findings from the literature review.

Factors that affect the impact of ICTs on education

The findings revealed that the use of digital technologies in education affects a variety of actors within a school’s ecosystem. First, we observed that as technologies evolve, so does the interest of the research community to apply them to school settings. Figure  2 summarizes the trends identified in current research around the impact of digital technologies on schools’ digital capacity and transformation as found in the present study. Starting as early as 2005, when computers, simulations, and interactive boards were the most commonly applied tools in school interventions (e.g., Eng, 2005 ; Liao et al., 2007 ; Moran et al., 2008 ; Tamim et al., 2011 ), moving towards the use of learning platforms (Jewitt et al., 2011 ), then to the use of mobile devices and digital games (e.g., Tamim et al., 2015 ; Sung et al., 2016 ; Talan et al., 2020 ), as well as e-books (e.g., Savva et al., 2022 ), to the more recent advanced technologies, such as AR and VR applications (e.g., Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ), or robotics and AI (e.g., Su & Yang, 2022 ; Su et al., 2022 ). As this evolution shows, digital technologies are a concept in flux with different affordances and characteristics. Additionally, from an instructional perspective, there has been a growing interest in different modes and models of content delivery such as online, blended, and hybrid modes (e.g., Cheok & Wong, 2015 ; Kazu & Yalçin, 2022 ; Ulum, 2022 ). This is an indication that the value of technologies to support teaching and learning as well as other school-related practices is increasingly recognized by the research and school community. The impact results from the literature review indicate that ICT integration on students’ learning outcomes has effects that are small (Coban et al., 2022 ; Eng, 2005 ; Higgins et al., 2012 ; Schmid et al., 2014 ; Tamim et al., 2015 ; Zheng et al., 2016 ) to moderate (Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Liao et al., 2007 ; Sung et al., 2016 ; Talan et al., 2020 ; Wen & Walters, 2022 ). That said, a number of recent studies have reported high effect sizes (e.g., Kazu & Yalçin, 2022 ).

Current work and trends in the study of the impact of digital technologies on schools’ digital capacity

Based on these findings, several authors have suggested that the impact of technology on education depends on several variables and not on the technology per se (Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Lei et al., 2022a ). While the impact of ICTs on student achievement has been thoroughly investigated by researchers, other aspects related to school life that are also affected by ICTs, such as equality, inclusion, and social integration have received less attention. Further analysis of the literature review has revealed a greater investment in ICT interventions to support learning and teaching in the core subjects of literacy and STEM disciplines, especially mathematics, and science. These were the most common subjects studied in the reviewed papers often drawing on national testing results, while studies that investigated other subject areas, such as social studies, were limited (Chauhan, 2017 ; Condie & Munro, 2007 ). As such, research is still lacking impact studies that focus on the effects of ICTs on a range of curriculum subjects.

The qualitative research provided additional information about the impact of digital technologies on education, documenting positive effects and giving more details about implications, recommendations, and future research directions. Specifically, the findings regarding the role of ICTs in supporting learning highlight the importance of teachers’ instructional practice and the learning context in the use of technologies and consequently their impact on instruction (Çelik, 2022 ; Schmid et al., 2014 ; Tamim et al., 2015 ). The review also provided useful insights regarding the various factors that affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the transformation process. Specifically, these factors include a) digital competencies; b) teachers’ personal characteristics and professional development; c) school leadership and management; d) connectivity, infrastructure, and government support; e) administration and data management practices; f) students’ socio-economic background and family support and g) the socioeconomic context of the school and emergency situations. It is worth noting that we observed factors that affect the integration of ICTs in education but may also be affected by it. For example, the frequent use of ICTs and the use of laptops by students for instructional purposes positively affect the development of digital competencies (Zheng et al., 2016 ) and at the same time, the digital competencies affect the use of ICTs (Fu, 2013 ; Higgins et al., 2012 ). As a result, the impact of digital technologies should be explored more as an enabler of desirable and new practices and not merely as a catalyst that improves the output of the education process i.e. namely student attainment.

Conclusions

Digital technologies offer immense potential for fundamental improvement in schools. However, investment in ICT infrastructure and professional development to improve school education are yet to provide fruitful results. Digital transformation is a complex process that requires large-scale transformative changes that presuppose digital capacity and preparedness. To achieve such changes, all actors within the school’s ecosystem need to share a common vision regarding the integration of ICTs in education and work towards achieving this goal. Our literature review, which synthesized quantitative and qualitative data from a list of meta-analyses and review studies, provided useful insights into the impact of ICTs on different school stakeholders and showed that the impact of digital technologies touches upon many different aspects of school life, which are often overlooked when the focus is on student achievement as the final output of education. Furthermore, the concept of digital technologies is a concept in flux as technologies are not only different among them calling for different uses in the educational practice but they also change through time. Additionally, we opened a forum for discussion regarding the factors that affect a school’s digital capacity and transformation. We hope that our study will inform policy, practice, and research and result in a paradigm shift towards more holistic approaches in impact and assessment studies.

Study limitations and future directions

We presented a review of the study of digital technologies' impact on education and factors influencing schools’ digital capacity and transformation. The study results were based on a non-systematic literature review grounded on the acquisition of documentation in specific databases. Future studies should investigate more databases to corroborate and enhance our results. Moreover, search queries could be enhanced with key terms that could provide additional insights about the integration of ICTs in education, such as “policies and strategies for ICT integration in education”. Also, the study drew information from meta-analyses and literature reviews to acquire evidence about the effects of ICT integration in schools. Such evidence was mostly based on the general conclusions of the studies. It is worth mentioning that, we located individual studies which showed different, such as negative or neutral results. Thus, further insights are needed about the impact of ICTs on education and the factors influencing the impact. Furthermore, the nature of the studies included in meta-analyses and reviews is different as they are based on different research methodologies and data gathering processes. For instance, in a meta-analysis, the impact among the studies investigated is measured in a particular way, depending on policy or research targets (e.g., results from national examinations, pre-/post-tests). Meanwhile, in literature reviews, qualitative studies offer additional insights and detail based on self-reports and research opinions on several different aspects and stakeholders who could affect and be affected by ICT integration. As a result, it was challenging to draw causal relationships between so many interrelating variables.

Despite the challenges mentioned above, this study envisaged examining school units as ecosystems that consist of several actors by bringing together several variables from different research epistemologies to provide an understanding of the integration of ICTs. However, the use of other tools and methodologies and models for evaluation of the impact of digital technologies on education could give more detailed data and more accurate results. For instance, self-reflection tools, like SELFIE—developed on the DigCompOrg framework- (Kampylis et al., 2015 ; Bocconi & Lightfoot, 2021 ) can help capture a school’s digital capacity and better assess the impact of ICTs on education. Furthermore, the development of a theory of change could be a good approach for documenting the impact of digital technologies on education. Specifically, theories of change are models used for the evaluation of interventions and their impact; they are developed to describe how interventions will work and give the desired outcomes (Mayne, 2015 ). Theory of change as a methodological approach has also been used by researchers to develop models for evaluation in the field of education (e.g., Aromatario et al., 2019 ; Chapman & Sammons, 2013 ; De Silva et al., 2014 ).

We also propose that future studies aim at similar investigations by applying more holistic approaches for impact assessment that can provide in-depth data about the impact of digital technologies on education. For instance, future studies could focus on different research questions about the technologies that are used during the interventions or the way the implementation takes place (e.g., What methodologies are used for documenting impact? How are experimental studies implemented? How can teachers be taken into account and trained on the technology and its functions? What are the elements of an appropriate and successful implementation? How is the whole intervention designed? On which learning theories is the technology implementation based?).

Future research could also focus on assessing the impact of digital technologies on various other subjects since there is a scarcity of research related to particular subjects, such as geography, history, arts, music, and design and technology. More research should also be done about the impact of ICTs on skills, emotions, and attitudes, and on equality, inclusion, social interaction, and special needs education. There is also a need for more research about the impact of ICTs on administration, management, digitalization, and home-school relationships. Additionally, although new forms of teaching and learning with the use of ICTs (e.g., blended, hybrid, and online learning) have initiated several investigations in mainstream classrooms, only a few studies have measured their impact on students’ learning. Additionally, our review did not document any study about the impact of flipped classrooms on K-12 education. Regarding teaching and learning approaches, it is worth noting that studies referred to STEM or STEAM did not investigate the impact of STEM/STEAM as an interdisciplinary approach to learning but only investigated the impact of ICTs on learning in each domain as a separate subject (science, technology, engineering, arts, mathematics). Hence, we propose future research to also investigate the impact of the STEM/STEAM approach on education. The impact of emerging technologies on education, such as AR, VR, robotics, and AI has also been investigated recently, but more work needs to be done.

Finally, we propose that future studies could focus on the way in which specific factors, e.g., infrastructure and government support, school leadership and management, students’ and teachers’ digital competencies, approaches teachers utilize in the teaching and learning (e.g., blended, online and hybrid learning, flipped classrooms, STEM/STEAM approach, project-based learning, inquiry-based learning), affect the impact of digital technologies on education. We hope that future studies will give detailed insights into the concept of schools’ digital transformation through further investigation of impacts and factors which influence digital capacity and transformation based on the results and the recommendations of the present study.

Acknowledgements

This project has received funding under Grant Agreement No Ref Ares (2021) 339036 7483039 as well as funding from the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy. The UVa co-authors would like also to acknowledge funding from the European Regional Development Fund and the National Research Agency of the Spanish Ministry of Science and Innovation, under project grant PID2020-112584RB-C32.

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Home > Griffith Sciences > School of Information and Communication Technology > Research > Current ICT Research Projects

Current ICT Research Projects

School of Information and Communication Technology

• Learning and teaching

Be at the forefront of the latest technological advancements with a research degree at Griffith.

Explore the range of research projects available with the School of ICT in areas of computer vision and signal processing, software engineering and software quality, cyber security and network security, autonomous systems, machine learning, data analytics and big data.

For more information about the project, please contact the listed supervisor.

Computer Vision and Signal Processing

Extraction and Modelling of Power Line Corridor

Supervisors:  Dr. Mohammad Awrangjeb and Professor Bela Stantic

Description: The speedy development in electricity infrastructure due to urge in domestic and business usage as well as its importance in national economy requires a safe and secure maintenance of power line corridors (PLC) to ensure the efficient and uninterrupted power supply of electricity to consumers. The monitoring of PLC primarily includes two of the following aspects: electrical components such as power lines and pylons and surrounding objects, such as vegetation. For reliable transmission, the stability of power lines and pylons and monitoring of vegetation near PLC is important.

As power lines are comprised of very thin conductors, thus detailed information is required for accurate mapping. Airborne light detection and ranging (LiDAR) has been proven a powerful tool to overcome these challenges to enable more efficient inspection in recent years. Active airborne LiDAR systems directly capture the 3D information of power infrastructure and surrounding objects. Nevertheless,

PLCs are built with multi-loop, multi-phase structures (bundle conductors) and exists in intricate environments (e.g., mountains and forests), thus raises challenges to process airborne point cloud data for extraction and modelling of individual PLC objects.

This study aims to overcome these challenges by providing an automated and more robust solutions for PLC mapping. This research incorporates three main objectives; (i) power lines extraction, pylons and vegetation extraction, (ii) reconstruction of power lines and pylons using for 3D modelling, (iii) vegetation monitoring from airborne LiDAR data.

Related publications

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. (Google Scholar Metrics (GSM) -Rank: 10 in Remote Sensing, GSM makes only 20 top cited in each area combining both conference and journal articles.)

DICTA 2019 (Australian in Core 2018)

Building Extraction from LiDAR point cloud data

Description: Building extraction with individual roof parts and other components such as chimneys and dormers is important for building reconstruction and 3D modelling. Using Light Detection and Ranging (LiDAR) point-cloud data the task is more complex and difficult because of the unknown semantic characteristics and inharmonious behaviour of the LiDAR input data. Most of the existing state-of-the-art methods fail to detect small true roof planes with exact boundaries due to outliers, occlusions, complex building structures, and other inconsistent nature of LiDAR data thus, accurate building detection, reconstruction, and 3D modelling a challenging and complex task. Studies have been conducted over the last two decades on individual building extraction and reconstruction using LiDAR data. The main objective of this PhD thesis is to extract buildings and individual roof parts effectively using LiDAR data for the purpose of 3D reconstruction and modelling of buildings.

Dey, E. K., Awrangjeb, M., & Stantic, B. (2019, July). An Unsupervised Outlier Detection Method For 3D Point Cloud Data. In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 2495-2498). IEEE.

Dey, E. K., Awrangjeb, M., & Stantic, B. (2020). Outlier detection and robust plane fitting for building roof extraction from LiDAR data. International Journal of Remote Sensing, 41(16), 6325-6354.

Dey, E. K. and Awrangjeb, M., "A Robust Performance Evaluation Metric for Extracted Building Boundaries From Remote Sensing Data," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 4030-4043, 2020, doi: 10.1109/JSTARS.2020.3006258.

Continual Learning on Dynamic Data Stream

Supervisors:  A/Prof. Alan Wee-Chung Liew

Description: Continual learning (CL) or lifelong learning is the ability of a model to learn continually from a stream of data. The idea of CL is to mimic human’s ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. With CL, we want to use the data that is coming to update the model autonomously based on the new activity. Data are typically discarded after use, and there is no opportunity to re-use the data for model retraining. Continual learning is a challenge for deep neural network models since the continual acquisition of incrementally available information from non-stationary data distributions generally leads to catastrophic forgetting or interference. Other challenges in CL includes adapting to emerging and disappearing concepts, adapting to concept drift, adapting to nonstationary noise, dealing with highly imbalance classes, etc. This project aims to develop novel (supervised and unsupervised) machine learning algorithms that overcome these challenges.

T.T. Nguyen, M.T. Dang, V.A. Luong, A.W.C. Liew, T.C. Liang, J. McCall, “Multi-Label Classification via Incremental Clustering on Evolving Data Stream”, Pattern Recognition, Vol. 95: 96-113, 2019.

T.T. Nguyen, T.T.T. Nguyen, V.A. Luong, N.Q.V. Hung, A.W.C. Liew, B, Stantic, “Multi-label classification via labels correlation and first order feature dependence on data stream”, Pattern Recognition, Vol. 90: 35-51, 2019.

T.T.T. Nguyen, T.T. Nguyen, A.W.C. Liew, S.L. Wang, “Variational Inference based Bayes Online Classifiers with Concept Drift Adaptation”, Pattern Recognition, Vol. 81: 280-293, 2018.

Efficient object detection for low-powered devices

Supervisors:  Dr. Gervase Tuxworth

Description: Recognising objects in images is an important task for many applications including security, autonomous navigation and image tagging and markup. Recently the field has been dominated by convolutional neural networks, with some networks reaching sizes of over 100 million parameters. These networks are typically run on specialised hardware that consumes a high amount of power, but when considering applications running on light-weight low-cost hardware, these solutions may not be suitable. This project seeks to find solutions to allow for accurate object detection on low powered devices.

Shaikh D, Manoonpong P, Tuxworth G, Bodenhagen L. Multi-sensory guidance of goal-oriented behaviour of legged robots. Proceedings of the 20th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2017.

Fine-grained image classification

Description: Fine-grained image classification is a challenge in computer vision, which aims at identifying the correct object in a dataset where there is both low between-class variance (different objects appear visually similar) and high intra-class variance (objects of the same class appear different). This work looks at implementing new models and techniques within convolutional neural networks to improve performance in these challenging datasets.

Park YJ, Tuxworth G, Zhou J. Insect Classification Using Squeeze-and-Excitation and Attention Modules - a Benchmark Study. IEEE International Conference on Image Processing, 2019.

Spectral-spatial-temporal processing of hyperspectral videos

Supervisors: A/Prof. Jun Zhou

Description: Hyperspectral videos contains rich spectral, spatial, and temporal information. Traditional methods treat these domains separately to undertake video analysis tasks, ignoring the intrinsic relationship embedded in the cross-modal data space. In this project, we propose to develop joint spectral-spatial-temporal processing methods to fully explore the abundant information embedded in hyperspectral videos. Fundamental theories and methods will be developed based on physics and statistical models and will be powered by the latest deep learning approaches. A number of applications in environment, agriculture, and medicine will be used to showcase the usefulness of the methods.

Fengchao Xiong, Jun Zhou, and Yuntao Qian. Material based object tracking in hyperspectral videos, IEEE Transactions on Image Processing, Vol 29, No. 1, pages 3719-3733, 2020.

Suhad Lateef Al-khafaji, Jun Zhou, Ali Zia and Alan Wee-Chung Liew. Spectral-spatial scale invariant feature transform for hyperspectral images. IEEE Transactions on Image Processing, Vol. 27, No. 2, pages 837-850, 2018.

Microscopic hyperspectral imaging

Description: Object detection and recognition is a fundamental task for microscopic imaging. It’s applications range from disease detection, cell recognition to microplastic classification. Traditional detection and recognition techniques are based on images captured in the visible light wavelength, limits the discrimination capability of systems deployed for complex microscopic imaging environment. Hyperspectral images contain light wavelength indexed reflectance from objects, therefore, enable the capability of material detection that is essential for many real-world tasks. This project provides unique opportunities to work with cross-disciplinary researchers in medical and environmental areas. The goal is to develop innovative technologies that can revolutionise the current microscopic imaging practice.

Chee Meng Ho, Qi Sun, Adrian Teo; David Wibowo, Yongsheng Gao, Jun Zhou, Yanyi Huang, Say Hwa Tan, and Chun-Xia Zhao. Development of a microfluidic droplet-based microbioreactor for microbial cultivation. ACS Biomaterials Science & Engineering, Vol. 6, No. 6, pages 3630-3637, 2020.

Yanyang Gu, Zongyuan Ge, Paul Bonnington, and Jun Zhou. Progressive transfer learning and adversarial domain adaptation for cross-domain skin disease classification. IEEE Journal of Biomedical and Health Informatics, Vol. 24, No. 5, pages 1379-1393, 2020.

Jie Liang, Jun Zhou, Lei Tong, Xiao Bai and Bin Wang. Material based salient object detection from hyperspectral images. Pattern Recognition, Vol. 76, Pages 476-490, 2018.

Software Engineering and Software Quality

Software correctness for Safe-Critical Systems

Supervisors: Professor Vladimir Estivill-Castro

Miguel Carrillo , Vladimir Estivill-Castro,  David A. Rosenblueth . Model-to-Model Transformations for Efficient Time-domain Verification of Concurrent Models by NuSMV Modules.  MODELSWARD 2020 : 287-298

Complexity Management in Enterprise Architecture

Supervisors: A/Prof. Peter Bernus

Description: The history of mankind can be characterised as a constant development of tools, technologies and systems of various kinds (agriculture, transport, communication, manufacturing, energy, etc.). These  (technical and socio-technical) systems of systems have evolved to be more and more complex and it has become increasingly difficult to manage and control their evolution.

This is a fundamental problem, because the mere survival of humankind became dependent on them.  Taming the complexity of large scale systems requires an interdisciplinary effort, that combines approaches rooted in Enterprise Architecture, AI & Cognitive Science, Systems Engineering, Management Science & Control Engineering, Cybernetics, and others.

Several interdisciplinary PhD projects are available to address the problem: How to direct the evolution and transformation of large scale systems?

Possible topics include:

• Improving the Resilience of Australia's Supply Chain,
• Architecting Energy Transformation,
• Modelling Smart Manufacturing (IoT, Industry 4.0, digital twin),
• Architecting Integrated Transport Systems, Smart Cities, Architectural Solutions to the Water Crisis,
• Agile command and control
• The limits of control (theory development),
• Self Aware Systems Architecture (theory development).

Bernus, P., Noran, Goranson, T. (2020). Toward a Science of Resilience, Supportability 4.0 and Agility. In Proc. IFAC World Congress (July 2020). IFAC Papers Online ISSN: 2405-8963

Turner, P., Bernus, P., Noran, O. (2018). Enterprise Thinking for Self-aware Systems. In S. Cavalieri, M. Macchi and L. Monostori (Eds) Proc Information Control Problems in Manufacturing  IFAC Papers Online ISSN: 2405-8963

Bernus, P., Goranson, T., Gotze, J., Jensen-Waud, A., Kandjani, H., Molina, A., Noran, O., Rabelo, R.J., Romero, D., Saha, P., Turner, P. (2016) Enterprise engineering and management at the crossroads.  Computers in Industry. 79 (2016):87-102.

Bernus, P., Noran, O., Molina, A. (2015). Enterprise Architecture: Twenty Years of the GERAM Framework. Annual Reviews in Control. 39(2015):83-93

Organisationally mandated assimilation processes of an enterprise-wide information system in a radiology practice in Australia

Supervisors: Dr. Bruce Rowlands

Description: The study aims to develop a theoretical framework that integrated elements of Lamb & Kling’s (2003) social actor model concentrating on the relationships among the radiology practitioners, the technology (an enterprise-wide Health Information System), and a larger social milieu surrounding its use.

Alireza Amrollahi and Bruce Rowlands. OSPM: a design methodology for open strategic planning. Information & Management, Vol. 55, No. 6, pages 667-685, 2018

Alireza Amrollahi and Bruce Rowlands. Collaborative open strategic planning: a method and case study. Information Technology & People, Vol. 30, No. 4, pages 832-852, 2017.

IT Risk Management Implementation

Description: Two important gaps exist in IT risk management (ITM) research. Firstly, there is insufficient research on the process IT individuals go through when implementing IT-RM frameworks for the first time. Secondly, there is an absence of literature that addresses how these factors and processes can be depicted in a model.

Neda Azizi, Bruce Rowlands and Shah Jahan Miah. IT risk management implementation as sociotechnical change: a process approach. 30 th Australasian Conference on Information Systems, paper 104, 2019.

Developing the concept of individual IT culture and its impact on IT risk management implementation, paper 178, 2019.

Helping airline pilots fly more safely: Creating, validating and verifying the consistency of dynamic procedures

Supervisors: Dr. Geraldine Torrisi , Dr. Guido Carim Junior , Prof. Vladimir Estivill-Castro

Description: Do you want to help airline pilots perform their flying safer? An airplane is a very complicated safety-critical system whose technology is the main interface to those operating it. However, when a particular failure occurs, pilots must consult emergency checklists, which are either presented as paper-based or in electronic format. Electronics checklists are commonly integrated as part of the avionics or part of the Flight bags (tablets issued by the aircraft manufacturer) as a pdf file or a rudimentary electronic version of the paper-based checklist with one of another extra feature (such as tracking the actions, e.g.). When the situation is more complicated than covered by the checklists, pilots must also judge the procedures’ instructions against their flying experience to handle the problem. Situations like multiple failures, false alarms, inoperative systems are not covered by these checklists, regardless of the format, and impose additional demands on the troubleshooting activity. The situations are dynamic, but the procedures are static.

Despite some artificial intelligence tools currently converting the natural language and artifacts (diagram) of paper-based checklists, there is a need to create, validate and verify the consistency of the dynamic procedures. Your contribution would be to ensuring the information on procedures and course of action is consistent, not contradictory, complete and adequate for the set of symptoms input by pilots. Maybe modelling with behaviour trees, or some other formal logic system (such as defeasible logic) lining it with AI and reasoning. The aim is to confirm procedures are polished and even updateable while retaining consistency. You may find that there may be other challenges. For instance, can some procedures be factored out, and be re-used as subroutines? Can the description of the procedure be also assisting the pilot with a model of the state of the flight?

This PhD research topic is part of a larger project reinventing the way pilots use the documents, manuals and checklist in the cockpit. The objective is to make their work more efficient and safer by providing an intelligent system that provides the information they need, when needed.

Guido C. Carim, Tarcisio A. Saurin and Sidney W.A. Dekker. How the cockpit manages anomalies: revisiting the dynamic fault management model for aviation. Cognition, Technology & Work, Vol. 22, pages 143–157, 2020.

Guido C. Carim, Tarcisio A. Saurin, Jop Havinga, Andrew Rae, Sidney W.A. Dekker, and Éder Henriqson. Using a procedure doesn’t mean following it: A cognitive systems approach to how a cockpit manages emergencies. Safety Science, Vol. 89, pages 147-157, 2016.

Learning Analytics Implementations in Australian Universities

Supervisors: Dr. David Tuffley

Description: Learning Analytics Implementations in Australian Universities: towards a model of success.

Clark, Jo-Anne & Tuffley, David. Learning Analytics implementations in universities: towards a model of success using multiple case studies. Proceedings of the 36 th International Conference on Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education, pages 82-92, 2019.

Developing high quality software systems through Behaviour Engineering

Supervisors: Dr. Larry Wen

Description: Behavior Engineering (BE), an innovative Software Engineering approach to develop software intensive systems, was firstly proposed by Professor Geoff Dromey in Griffith University. In the past two decades, various research and real industry cases studies have been explored to investigate its capability and received fruitful results. Different from other software engineering approaches, which try to make a software design to satisfy the software requirements, while BE is extracting a software design from the software requirements through a state-of-the-art translation and integration process. This approach can quickly identify defects in software requirements and produce a solution that guarantees to fulfil the requirements.  In the past 20 years, more than one hundred papers have been published. Many software tools have been developed and large-scale case studies have been performed. BE has also been applied in many software engineering areas including requirement engineering, software change management, software process improvement, and formal method. Even though much research has been conducted, and their results have proven the value of this approach, the potential of this approach has yet been fully appreciated. There are many different paths to extend this approach and many different areas that could adapt this approach. As an example, we are currently collaborating with a Chinese company to investigate BE in software acquisition.

Many of BE related publications can be found at BE website.

Cyber Security and Network Security

Using Machine Learning to Detect Cyber Attacks in Industrial Control Systems

Supervisors: A/Prof. Ernest Foo

Description: Industrial Control systems use SCADA protocols to control the electricity grid or water treatment plants or other critical infrastructure.  Many of these systems are being connected to the Internet and are vulnerable to cyber attacks.  This project will employ machine learning and artificial intelligence to automatically detect attacks against these systems and automate the best response for defense.

IEEE Transactions on Industrial Informatics, IEEE Transactions on Information Forensics and Security, Computers & Security

Automated Process Analysis for Intrusion Detection in Industry 4.0 Systems

Description: Next generation manufacturing systems use advanced robotic technologies and complex processes to function.  However many of these systems are connected to the Internet and are vulnerable to cyber attacks.  Stealthy cyber attacks are often difficult to detect.  This project will develop algorithms to monitor system processes for anomalies to automatically detect faults and cyber attacks.

IEEE Transactions on Industrial Informatics, IEEE Transactions on Information Forensics and Security, Computers & Security, IEEE Access

Cyber Security of Vehicle Communication Systems

Description: Driver-less vehicles and Intelligent Transport Systems need to use wireless communications to function with safety.  However these communications may be vulnerable to cyber attacks that allow attackers to manipulate traffic and cause accidents. This project will explore new ways to ensure efficient authentication to detect and prevent attacks against vehicle communication systems.

IEEE Transactions on Industrial Informatics, Vehicular Communications, IEEE Transactions on Vehicular Technology

Advanced Post-Quantum Cryptosystems

Supervisors: Dr. Qinyi Li

Description: Our daily digital life is protected by public-key cryptosystems like public-key encryption and digital signature systems. The security of most public-key cryptosystems have been deployed is ultimately based on the difficulties of solving number-theoretic problems (e.g., integer factoring problem and discrete logarithm problem) using classic computers. It turns out these number-theoretic problems can be efficiently solved by large-scale quantum computers which have been theorised about for decades. There has been substantial progress towards making quantum computing practical. To protect our communication in the long-term, we need a new generation of cryptosystems to defeat quantum computers. Cryptography based on decoding problems (e.g., decoding random linear codes) is a very promising candidate. In this project, you will explore the field of post-quantum cryptography and conduct research on one the two directions: 1) designing advanced post-quantum cryptosystems e.g., attributed-based encryption, functional encryption, fully homomorphic encryption, ring/group signatures and apply them to the real-world problems, e.g., fine-grained access control on encrypted data for cloud computing, efficient search and query on the encrypted database, smart contract and cryptocurrency 2) designing and implementing (in software or hardware) practical public-key encryption and digital signature systems with strong practical security (i.e., secure against various side-channel attacks) and high practicality (i.e., can be used for the Internet security protocols or computing-resource-restricted devices like IoT devices).

Xavier Boyen, Malika Izabachene, Qinyi Li (Corresponding Author): An Efficient Lattice CCA-Secure KEM in the Standard Model. The 12th International Conference on Security and Cryptography for Networks (SCN 2020). Accepted on 14 June, 2020.

Xavier Boyen, Qinyi Li (Corresponding Author): Direct CCA-Secure KEM and Deterministic PKE from Plain LWE. The 10th International Conference on Post-Quantum Cryptography (PQCrypto 2019). LNCS 11505, pp.116-130. Springer 2019.

Xavier Boyen, Qinyi Li (Corresponding Author): All-but-Many Lossy Trapdoor Functions from Lattices and Applications. The 37th International Cryptology Conference (Crypto 2017). LNCS 10403, pp. 298-331, Springer 2017.

Xavier Boyen, Qinyi Li (Corresponding Author): Towards Tightly Secure Lattice Short Signature and Id-Based Encryption. The 22nd International Conference on Theory and Applications of Cryptography and Information Security (AsiaCrypt 2016). LNCS 10032, pp. 404-434. Springer 2016.

Application of Machine Learning Intelligence in Wireless Networks

Supervisors: Dr. Wee Lum Tan

Description: There is great potential in applying machine learning techniques to design self-organising, self-aware, intelligent wireless networks. Machine learning enables network nodes to actively learn the state of the wireless environment, detect correlations in the data, and take actions to optimise network operations and make efficient use of the limited wireless spectrum resources.

The first project will develop methods to parse the massive amount of wireless network statistics/data (e.g. channel state information, signal strength, interference, noise, traffic load/patterns, etc.) in order to analyse and predict the context of the wireless environment. Using these data, we will develop machine learning-guided techniques to address a variety of challenges in wireless networks such as power control, user traffic scheduling, spectrum management, rate selection, etc.

A major challenge of machine learning is its vulnerability to adversarial attacks. Adversarial machine learning attacks in wireless networks can cause network nodes to make incorrect decisions or interfere with data transmissions. For example, network nodes can train a classifier on various wireless statistics and use it to predict future channel availability status and adapt their transmission decisions to the spectrum dynamics. An adversary can train its classifier to be functionally equivalent to the one at the transmitter, and launch attacks (e.g. sends jamming signals) when it predicts that the transmitter will transmit data to the receiver. These attacks can significantly affect network performance, e.g. reduced spectral efficiency and increased node energy consumption.

Therefore, a second project is to investigate the impact of different machine learning vulnerabilities in wireless networks and develop techniques to detect and mitigate these attacks in highly dynamic wireless networks.

Autonomous Systems

Using Adaptive Behaviour Found in Nature to Solve Dynamic Multi-objective Optimisation Problems

Supervisors: Dr. Marde Helbig

Description: Many real-world problems require obtaining an optimal trade-off solution for conflicting goals, for example, trying to minimise the electricity cost while maximising comfort in a room. Normally if you maximise comfort, through for example switching on the air-conditioning and switching on the lights in the room, you are also increasing the electricity cost. Therefore, these two goals conflict with one another. Furthermore, a change in the weather may lead to a different desired solution for the room. Another example is finding the optimal route when using a map application or a GPS when driving from one point to another, by minimising the time required and minimising the cost (such as distance travelled or reducing toll fees and thereby avoiding the motor way). However, minimising the cost may lead to a longer travel time being required. In addition, an accident on the route may change the most optimal solution to not being valid anymore. This research investigates using Computational Intelligence algorithms to solve these types of problems, referred to as dynamic multi-objective optimisation problems. Computational Intelligence algorithms have a population of entities, where each entity represents a possible solution in the search space. These algorithms are based on adaptive behaviour found in nature, such as the flying formation of a flock of birds searching for food, pheromones used by ants when foresting for food, genetic material such DNA, etc.

M. Helbig, Heiner Zille, Mahrokh Javadi and Sanaz Mostaghim. Performance of Dynamic Algorithms on the Dynamic Distance Minimization Problem, In Proceedings of the International Genetic and Evolutionary Computation Conference (GECCO) Companion, p. 205-206, Prague, Czech Republic, 13-17 July 2019 (CORE Rank A).

M.  Helbig and   A.P. Engelbrecht. Benchmarks for dynamic multi-objective optimisation algorithms, ACM Computing Surveys, 46(3), September, 2014 (2014 impact factor: 3.373, WoS Rank: Q1).

M.  Helbig and A.P.  Engelbrecht. Performance measures for dynamic multi-objective optimisation, Information Sciences, 250:61-81, November, 2013 (2013 impact factor: 3.643, WoS Rank: Q1).

Estivill-Castro V. (2019) Game Theory Formulation for Ethical Decision Making. In: Aldinhas Ferreira M., Silva Sequeira J., Singh Virk G., Tokhi M., E. Kadar E. (eds) Robotics and Well-Being. Intelligent Systems, Control and Automation: Science and Engineering, vol 95. Springer, Cham.

Multi-agent systems to Model the Human Immunology response to viruses like COVID or to Cancer

David F. Nettleton , Vladimir Estivill-Castro,  Enrique Hernández Jiménez . Multi-agent Modeling Simulation of In-vitro T-cells for Immunologic Alternatives to Cancer Treatment.  ICAART (1) 2020 : 169-178

Intelligent optimisation and deep learning guided protein structure prediction

Supervisors: Professor Abdul Sattar

Explainable AI: reasoning with learning

Learning based search for hard combinatorial optimisation problems

Supervisors: A/Prof. Kaile Su

Description: This Project aims to advance local search technologies to address new challenges for solving hard combinatorial optimization problems in data mining, image processing, and deep neural network. This Project expects to propose new efficient local search strategies, to investigate the mechanism that integrates proposed local search strategies and machine learning for real-world applications, and to explore the local search approach to training deep neural networks. Expected outcomes of this Project include the novel paradigm for efficient local search, and the local search algorithms for solving real-world problems in data mining, image processing, and deep neural network

ChuanLuo ,  Shaowei Cai , Kaile Su,  Wenxuan Huang . CCEHC: An efficient local search algorithm for weighted partial maximum satisfiability.  Artificial Intelligence, Vol. 243 , pages 26-44, 2017.

Yi Fan ,  Nan Li ,  Chengqian Li ,  Zongjie Ma ,  Longin Jan Latecki , Kaile Su. Restart and Random Walk in Local Search for Maximum Vertex Weight Cliques with Evaluations in Clustering Aggregation.  International Joint Conference on Artificial Intelligence, pages 622-630, 2017.

Explainable AI through rule-based machine learning

Supervisors: Dr. Zhe Wang and Prof. Kewen Wang

Description: As existing deep learning systems often behave in a black-box manner and thus are incapable to provide human-understandable explanations for their predictions, which limits their wide application in decision critical applications. This project focuses on the automated construction of rule-based knowledge bases to support machine reasoning and explaining the predictions made.

Pouya Ghiasnezhad Omran, Kewen Wang, and Zhe Wang. An Embedding-based Approach to Rule Learning in Knowledge Graphs. In: IEEE Transactions on Knowledge and Data Engineering (accepted for publication).

Pouya Ghiasnezhad Omran, Kewen Wang, and Zhe Wang. Scalable Rule Learning via Learning Representation. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18), pages 2149-2155, 2018.

Machine Learning, Data Analytics and Big Data

Privacy-Preserving Data-Mining

David F. Nettleton , Vladimir Estivill-Castro,  Julián Salas . Privacy in Multiple On-line Social Networks - Re-identification and Predictability.  Trans. Data Priv. 12(1) : 29-56 (2019)

Explanation and verification of machine learning models

Supervisors: Dr. Zhe Hou

Description: Machine learning is a subset of artificial intelligence that is focused on building mathematical models based on sample data, and making predictions without explicitly being programmed to perform the task. Machine learning has been used in data analytics for insurance, sports, tourism, marketing and many other areas. However, most existing machine learning algorithms often give excellent prediction results without telling the user how the decisions are made. This weakness results in trust issues from the user and limitations for adopting machine learning in some applications. To realise white-box machine learning, we propose to develop a new prediction model analysis method based on automated reasoning that systematically extracts logical explanations from prediction models and presents them in a way that users can easily understand. We will then leverage my previous experience in formal verification to convert prediction model into logical model and verify it against user specifications. Finally, we will develop new learning algorithms that can train correct-by-construction prediction models with respect to user specifications.

Optimisation-driven safe reinforcement learning for medical decision-making

Description: There is a tremendous gap between today’s AI systems and the requirements in mission-critical applications. Improving reliability, safety, and security of AI decision-making is of paramount importance. These challenges drove us to develop new AI decision-making techniques which are safer and more secure. Particularly, we propose to integrate formal verification and bio-inspired optimisation techniques into (deep) reinforcement learning (RL) in order to provide a higher level of safety and security guarantees. There are three main modules for the proposed work. The first module concerns the development of new reliable optimisation algorithms that are suitable to be used as the core for reinforcement learning. The second module is about designing an efficient safe reinforcement learning algorithm using PAT and reliability-based optimisation. The third module is an application of the previous two in the scenario of cyber-physical attacks. We propose to extend the previous two modules with an adversarial deep reinforcement learning approach to train a more secure system. Finally we will apply the developed techniques in medical decision-making case studies such as the usage of respirator for COVID-19 treatments and drug dosage analysis.

Automated Intelligence Analysis of Social Media Data for Causal Discovery

Supervisors: Dr. Saiful Islam

Description: The recent growth of social media data opens-up a potentiality for automated systems to collect, process and analyse user generated data on causality. Automated discovery of causality detects the relationship between a cause and the corresponding effect. The discovered causality related information can be directly applied to several applications including automatic question answering, security and prescriptive event analysis. For instance, can we conclude that “lack of communication” caused “a disruption in bus service in Gold Coast” from the tweet “A disruption in bus service in Gold Coast due to lack of communication between translink and event organizers” posted by a user in twitter? Automated discovery of causality in social media data is not straightforward. Rather, it is a very challenging problem due to the unstructured, informal, and diverse nature of social media data. In this project, we aim to tackle this issue by developing an autonomous intelligent system that will collect and process social media data, develop transfer-learning based artificial intelligence (AI) models and algorithms to detect text causality in social media data. Some of our preliminary works have already been accepted by the community and published in the top venues of data mining and AI fields.

Data Privacy for Machine Learning

Description: Machine learning (ML) allows computer systems to train themselves to improve their performance. It is pervasive and plays a key role in a wide range of applications. At a high level, ML consists of two phases. In the first phase, it applies a learning algorithm to a set of training data drawn from some unknown distribution to generate a model (hypothesis). In the second phase, the model can be used to explain new data (e.g., classify new data from the unknown distribution, or generate new data from a distribution that is close to the unknown distribution). In many applications of ML, sensitive data is needed and therefore data privacy becomes a concern. For example, when comes to Machine Learning As a Service, remote entities (usually untrusted) provide access to machine learning algorithms using the Internet to user’s data and return the results. User’s data might be completely exposed to the remote entities if security/privacy mechanisms are not imposed. Also, even with the best privacy on the training data, output (in cleartext form) of the second phase of ML may reveal information on training data. Therefore, with ML is being applied ubiquitously, a set of techniques that protect data privacy in ML is desirable and important. In this project, you will closely analyse the data privacy issues in the context of ML and explore advanced cryptographic and privacy techniques (e.g., fully homomorphic encryption, secure multi-party computation and differential privacy) to provide innovative and practical solutions.

Unified stream learning of medical data for continuous patient outcome monitoring, prognosis, and hospital resource allocation

Supervisors: A/Prof. Alan Wee-Chung Liew

Description: This project aims to develop novel stream learning algorithms for continuous patient outcome monitoring and prognosis by taking into account patient's data collected during hospital admission. The algorithms are expected to integrate high frequency time series data with patient's demographic data, lab test data, diagnosis data, prescription data, etc. as exemplified in MIMIC-III, for accurate patient outcome monitoring and prognosis. This will in turn used to inform hospital resource planning and allocation using for example, our highly efficient binary QP solver [1]. Practical issues such as data sparsity, noisy and missing data, data non-stationarity, data leakage, prediction bias, model explainability, etc. will be investigated.

B.S.Y. Lam, A.W.C. Liew, “A Fast Binary Quadratic Programming Solver based on Stochastic Neighborhood Search”, IEEE Trans on Pattern Analysis and Machine Intelligence, 2020. DOI: 10.1109/TPAMI.2020.3010811

Privacy Preserving Big Data Analytics in Cloud Environments

Supervisors: Dr. Hui Tian

Description: Along with the advances of computing and network technologies, applying AI and machine learning techniques to analyse various types of big data from heterogeneous sources has become a major form of data processing and analysis. However, privacy leakage in accessing, processing and analysing shared (published) data is a major concern that obstacles the development of big data analytics. There have been numerous example of shocking damages and losses - both political and financial - caused by privacy breaches in different scales.

In order to safeguard data sharing for the purpose of big data analytics required by our industry and business, in the project we will investigate effective models, methods and techniques for privacy protection in data publishing, processing and analysis. For data publishing, we will study both cryptographic and non-cryptographic techniques including block cypher, randomization and anonymization to achieve effective protection of different type of data. For data processing, we will study effective privacy-preserving computing techniques including secure multi-party computation (SMC) and differential privacy. We will apply them in a cloud environment on virtualized network and computing resources. For data analysis, we will embed privacy-preserving techniques into machine learning models to achieve secure machine learning on big data.

Project outcomes will benefit both researchers and practitioners in big data analytics, machine learning, cloud computing and social network analysis, and potentially result significant economic gain for Australia's network-centric industry and business.

Hui Tian, Wenwen Sheng, Hong Shen, Can Wang. Truth Finding by Reliability Estimation on Inconsistent Entities for Heterogeneous Data Sets. Knowledge-Based Systems, Jul. 2019. (CORE B, IF 5.921)

Hui Tian, Jingtian Liu and Hong Shen. Diffusion Wavelet-based Privacy Preserving in Social Networks. Computers & Electrical Engineering, Feb. 2018. (CORE B, IF 2.663)

Ruoxuan Wei, Hui Tian and Hong Shen. Improving k-Anonymity Based Privacy Preservation for Collaborative Filtering. Computers & Electrical Engineering, Mar. 2018. (CORE B, IF 2.663)

Effective and Efficient Recommender Systems via Social Networks

Supervisors: Dr. Can Wang

Description: This project aims at building a series of efficient recommender systems with high accuracy from social networks, such as Twitter, Facebook, Instagram, Netflix, and so on. The research questions may include how to quantify the coupling relationships in recommender systems from different levels, how to enhance the interpretability of recommender systems, how to involve the trend information and how to model trust in various recommendation problems, how to speed up the recommendation process but with acceptable accuracy, and etc.

Can Wang, Chi-Hung Chi, Zhong She, Longbing Cao, Bela Stantic. Coupled Clustering Ensemble by Exploring Data Interdependence. ACM Transactions on Knowledge Discovery from Data, Vol. 12, No. 6, Article 63, pages 1-38, 2018. [Impact Factor 2.538, Q1]

Can Wang, Xiangjun Dong, Fei Zhou, Longbing Cao, Chi-Hung Chi. Coupled Attribute Similarity Learning on Categorical Data. IEEE Transactions on Neural Networks and Learning Systems, Vol. 26, No. 4, pages 781-797, 2015. [Impact Factor: 11.683, Q1]

Zhe Liu ,  Lina Yao ,  Lei Bai ,  Xianzhi Wang ,  Can Wang . Spectrum-Guided Adversarial Disparity Learning. The 2020 ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Accepted by KDD 2020). [CORE Ranking: A*]

Ye Tao, Can Wang, Lina Yao, Weimin Li, Yonghong Yu. TRec: Sequential Recommender Based On Latent Item Trend Information. International Joint Conference on Neural Networks (IJCNN 2020), pp. 1-8, 2020. [CORE Ranking: A]

Yunwei Zhao, Can Wang, Chi-Hung Chi, Kwok-Yan Lam, Sen Wang. A Comparative Study of Transactional and Semantic Approaches for Predicting Cascades on Twitter, The 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), pages 1212-1218, 2018 [CORE Ranking: A*]

Approximate query answering in large graphs Description

Supervisors: A/Prof. Junhu Wang

Description: Graphs are increasingly being used to model complex data, and collections of graphs are getting very large, which brings big challenges to query processing. On one hand, many queries in graph databases are expensive by nature, and computing their exact answers can be infeasible when the graph size is large. On the other, in many applications an error-bounded estimate will suffice.  These motivates the work on approximate query answering in large graphs.

This project will investigate approximate query answering in large dynamic graphs where nodes and edges can be frequently updated. We will focus on property graphs where the nodes (and/or edges) are associated with key-value pairs, and queries that may involve simple aggregation (e.g., counting the number of occurrences of substructures), and develop novel techniques to efficiently find high-quality approximate answers.

The approaches will generally involve offline pre-processing (e.g., summarization, smart indexing), algorithm design, and experimental evaluation.  Due to the dynamic nature of the graphs, any auxiliary data structures need to be efficiently maintainable, and ideally incremental computation of query answers will be explored.  We are particularly interested in summarization-based techniques and applying machine-learning in auxiliary structure construction.

Xuguang Ren and Junhu Wang: Exploiting Vertex Relationships in Speeding up Subgraph Isomorphism over Large Graphs. VLDB 2015.

Xuguang Ren and Junhu Wang: Multi-Query Optimization for Subgraph Isomorphism Search. VLDB 2017.

Natural Language Question-Answering over Knowledge Graphs

Description: Knowledge graphs are tremendously popular nowadays because its ability to model diverse information. A knowledge graph can be regarded as a repository of facts about objects and their relationships, represented as labelled edges of a directed graph.  Over the last few years there have been growing interest in industry and academia to develop natural language question-answering (NLQL) systems over large knowledge graphs.  Such systems typically consists of two parts: question understanding and answer searching. Question understanding is to figure out the precise intention of the question, and answer searching is to actually find the answers based on the search intention.  Both tasks are challenging because of the ambiguity of natural language sentences and the fact that the same question an be raised in multiple ways in natural languages, and large size of knowledge graphs.

Existing approaches, whether based on question templates, machine-learning and graph embedding, or subgraph matching, suffer from limited capability in terms of the question types they can handle (i.e., they are limited to simple questions), accuracy, and efficiency. This PhD project will investigate NLQA over large knowledge graphs, with the aim of developing novel techniques to address the above limitations.

Xiangnan Ren, Neha Sengupta, Xuguang Ren, Junhu Wang, Olivier Cur. Finding Structurally Compact Subgraphs with Ontology Exploration in Large RDF data (under review by PVLDB).

Space Research

Development of new machine learning techniques for spectroscopic analysis of Martian soils and rock samples

Supervisors:  Prof. Paulo de Souza and Dr. Liat Rozemberg

Description: During combined 20 years of daily exploration of the surface of Mars, the NASA Mars Exploration Rovers Spirit and Opportunity performed thousands of spectroscopic analysis on soils and rocks [1-2]. A number of approaches have been employed to analyse these spectra including artificial neural networks [3], genetic algorithms [4, 5], and fuzzy logic [6]. These techniques were useful to extract relevant spectral parameters useful in the identification of minerals such as jarosite, hematite, goethite and primary minerals such as olivine and pyroxene [7-10].

Considering the significant temperature dependence of the spectral features and the daily variation of the Martian surface temperature, quality measurements can be at times difficult to be obtained. However, classifying similar samples and combining spectra over extensive ranges might be an acceptable approach aiming at increasing sampling quality over an extensive region visited by the rovers.

This project aims at the development of a new machine learning technique that will be able to combine similar spectroscopic measurements and utilise this combination to gain insights into mineral phase composition of the Martian surface.

[1] R. E. Arvidson, S. W. Squyres, J. F. Bell, J. G. Catalano, B. C. Clark, L. S. Crumpler, P. A. de Souza, A. G. Fairen, W. H. Farrand, V. K. Fox, R. Gellert, A. Ghosh, M. P. Golombek, J. P. Grotzinger, E. A. Guinness, K. E. Herkenhoff, B. L. Jolliff, A. H. Knoll, R. Li, S. M. McLennan, D. W. Ming, D. W. Mittlefehldt, J. M.  Moore, R. V. Morris, S. L. Murchie, et al. Ancient Aqueous Environments at Endeavour Crater, Mars. Science v. 343, p. 1248097-1248097, 2014. Doi: 10.1126/science.1248097

[2] S. W. Squyres, R. E. Arvidson, J. F. Bell, F. Calef, B. C. Clark, B. A. Cohen, L. A. Crumpler, P. A. de Souza, W. H. Farrand, R. Gellert, J. Grant, K. E. Herkenhoff, J. A. Hurowitz, J. R. Johnson, B. L. Jolliff, A. H. Knoll, R. Li, S. M. Mclennan, D. W. Ming, D. W. Mittlefehldt, T. J. Parker, G. Paulsen, M. S. Rice, S. W. Ruff, C. Schroder, A. S. Yen, K. Zacny, Ancient Impact and Aqueous Processes at Endeavour Crater, Mars. Science, v. 336, p. 570-576, 2012. doi: 10.1126/science.1220476

[3] P. A. de Souza (1998) Advances in Mössbauer data analysis. Hyperfine Interactions, 113, 383-390. doi: 10.1023/A:1012673027232.

[4] F. Susanto, P. de Souza, Mössbauer spectral curve fitting combining fundamentally different techniques, Nuclear Instruments and Methods in Physics Research Section B, v. 385 (2016) 40-45. doi: 10.1016/j.nimb.2016.08.011

[5] Jeremy Breen, P. de Souza, G. Timms, R. Ollington, Onboard assessment of XRF spectra using genetic algorithms for decision making on an autonomous underwater vehicle, Nuclear Instruments and Methods in Physics Research B 269 (2011) 1341-1245. doi: 10.1016/j.nimb.2011.03.012.

[6] P. A. de Souza (1999) Automation in Mössbauer Spectroscopy Data Analysis. Laboratory Robotics and Automation, 113-23. doi: 10.1002/(SICI)1098-2728(1999)11:1<3::AID-LRA2>3.0.CO;2-F.

[7] M. S. Rice, J. F. Bell III, E. A. Cloutis, A. Wang, S. W. Ruff, M. A. Craig, D. T. Bailey, J. R. Johnson, P. A. de Souza, W. H. Farrand (2010) Hydrated Minerals in Gusev Crater. Icarus, Vol 205, 2 (2010) 375-395. doi: 10.1016/j.icarus.2009.03.035.

[8] W. Goetz, P. Bertelsen, C. S. Binau, H. P. Gunnlaugsson, S. F. Hviid, K. M. Kinch, D. E. Madsen, M. B. Madsen, M. Olsen, R. Gellert, G. Klingelhöfer, D. W. Ming, R. V. Morris, R. Rieder, D. S. Rodionov, P. A. de Souza, C. Schröder, S. W. Squyres, T. Wdowiak, A. Yen (2005) Indication of drier periods on Mars from the chemistry and mineralogy of atmospheric dust, Nature, Vol 43662-65. doi: 10.1038/nature03807.

[9] R. V. Morris, Klingelhöfer, B. Bernhardt, C. Schröder, D. Rodionov, P. A. de Souza, A. Yen, R. Gellert, E. N. Evlanov, J. Foh, E. Kankeleit, P. Gutlich (2004) Mineralogy at Gusev Crater from the Mössbauer Spectrometer on the Spirit Rover. Science, 305, 833-836. doi: 10.1126/science.1100020.

[10] G. Klingelhöfer, R. V. Morris, B. Bernhardt, C. Schröder, D. Rodionov, P. A. de Souza, A. Yen, R. Gellert, E. N. Evlanov, B. Zubkov, J. Foh, U. Bonnes, E. Kankeleit, P. Gutlich, D. W. Ming, F. Renz, T. Wdowiak, S. W. Squyres, R. E. Arvidson (2004) Jarosite and Hematite at Meridiani Planum from Opportunity's Mössbauer Spectrometer. Science, 306,1740-1745. doi: 10.1126/science.1104653.

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Technology Research Topics

Table of contents

• 1 What are Technology Research Topics?
• 2 Tips for Writing Technology Research Papers
• 3 Computer Science and Engineering Technology Research Topics
• 4 Energy and Power Technology Research Topics
• 5 Medical Devices & Diagnostics
• 6 Pharmaceutical Technology Research Topics
• 7 Food Technology Research Topic
• 8 Educational Technology Research Topic
• 9 Controversial Technology Research Topics
• 10 Transportation Technology Research Topics
• 11.1 Conclusion

Have you ever wondered what interesting technology topics for research papers mean? Then this article will provide you with the answer and topic examples that you can research and write on.

Have you ever wondered what interesting technology topics for research paper mean? Then this article will provide you with the answer and topic examples that you can research and write on.

Tech-related topics are among the vastest categories for college students, experts, and researchers. The field covers everything development majorly. The good thing about technology is that it cuts across every business sector and education field. It is important in Sciences, Socials and Fine Arts.

There have been many technology research topics about technology and development of sciences in the 21st century. This is due to the massive scope of this field. Researchers and thesis students have continued to research the foundation of every development. Triggering new findings that contribute to the overall improvement of the field. There have been many thesis papers on technologies, and there will still be more over the years. This is because the field has witnessed the highest and fastest growth among other disciplines and sciences.

This article seeks to take the research of technology and its concepts to a higher level. By considering very recent topics in line with the evolution and revolution of the field. The topics suggested in this article are divided into various categories to give readers a very good understanding of the latest technological concepts.

What are Technology Research Topics?

A technology research topic is a research or thesis title that gives a researcher or expert an idea of what to work on. While in certain instances, people who make technology research will have their topic scribbled out for them, most of the time, they will need to get a topic themselves. These topics make it easy for them to work on.

Generally, a topic based on the technological field will be very formal. It must contain researched data and facts. The topic must have a final aim of projecting a solution, answer, or knowledge to the targeted audience. With this being the case, getting a technology research title requires more than just picking any topic. What will pass on as the best topic for research title will be one that can be researched and provides a solution to a problem that the target audience needs. In certain instances, both the problem and the solution may be completely new to the target audience.

However, the ability of the writer to make their target audience know that there is a problem and a corresponding solution could do the thesis and project a pathway to ground-breaking research. Hence, a research title must open the researcher, thesis student, and expert to opportunities that could trigger landmark solutions.

Based on the importance of a research title to an entire technology thesis or research, a potential writer must ensure that they know what it takes to draft an excellent technology and scientific research paper title. The good thing is that tips are available to draft an excellent thesis topic.

Tips for Writing Technology Research Papers

There are very important steps that must be followed for a writer to make an excellent thesis topic. One major tip is that any topic selected must include at least one recent technology. A thesis topic that needs today’s basic technology as a roadmap has a higher probability of coming out much more successfully than one that does not include any current or new technology. It is also possible to buy a research paper based on technology to avoid all the processes of learning new technology concepts. Below are the top tips for writing excellent technology Research projects.

• Understand The Research Assignment This step is very important and will determine whether you need to purchase a research paper or not. You have to understand the assignment to be asked to research if you seek to give out great quality work. You need to ensure that you know the problem being projected to you and what is needed as a solution. The best research paper topics technology are those the writers fully understood and created.
• Get the Topic Idea You can only carve out a topic for an assignment that you understand. This is why the first step is imperative and why this one must follow. Understanding the topic that currently and comprehensively covers the assignment and its solution will help you develop a catching title. Even if you seek to purchase research papers for sale , you will need to fully understand the assignment and the relevant fitting topic before purchasing. You will get value for your money and wow your target audience.
• Choose a Scope to Research If you are writing your research yourself, you should know that getting a topic is not just enough. A topic may cut into very vast areas, and it would be impossible for you to research all of these areas before your submission deadline. So the best way to ensure that you give quality research assignments is by specifying the scope of your topic. Identify which questions you want to provide answers for and focus on them. That way, your effort will be concentrated with a better output.
• Get Good Links Knowing how to get great links for your work is very important as that will help you give out excellent work. Relying on established sources for important theories will help you establish a more convincing solution to the problem your research is about.

This article will consider major research topics on different technology research topics so that researchers and students planning to write a thesis or research paper can select from them and start their project immediately.

Computer Science and Engineering Technology Research Topics

Computer Science is one of the widest fields of Technology projects. As such, there are multiple writing topics to explore following the consistent and continuous development of the sector. As for Computer Science, there are many research works on computer engineering and more to explore. Thanks to the growth in better computer hardware and the more seamless management systems developed over time.

This section consists of 15 different research topics that thesis and college students can work on and get approval from their supervisors.

All the topics are recent and in line with global needs in 2023 and the next couple of years. They include:

• Blockchain technology and the banking industry
• The connection between human perception and virtual reality
• Computer-assisted education and the future
• High-dimensional data modeling and computer science
• Parallel computing Languages
• Imperative and declarative language in computer science
• The machine architecture and the efficiency of code
• The use of mesh generation for computational domains
• Persistent data structure optimization
• System programming language development
• Cyber-physical system vs sensor network
• Network economics and game theory
• Computational thinking and science
• Types of software security
• Programming language and floating-point

Energy and Power Technology Research Topics

Unlike many technology-related topics, Energy and Power is one that cuts into the spheres of politics, economics, and pure science. In the areas of Economics, Energy and Power are the second most arbitrated cases. It’s only behind Construction disputes.

However, energy and Power in Science and Practicality are not for the sake of disputes. In recent years, there has been more harmony between energy and other tech-related disciplines. This has triggered many research projects, and writing research assignments is not out of the equation.

So do you have an energy/Power research assignment to handle, then this section provides you with amazing topic ideas and scopes that you can choose and pick from? All the topics are very recent and in line with the needs of today’s assignments.

Get topics that focus on Cars, power industries, chemicals, and more.

• The use of fuel cells for stationary power generation
• Energy density
• Lithium-air and lithium-ion battery
• The better between gasoline and lithium-air batteries
• Renewable energy technologies
• The pros and cons of renewable energy usage
• Algae and biofuels
• Solar installations of India
• The use of robots in adjusting solar panels to weather
• Create energy and inertial confinement fusion
• Hydrogen energy and the future
• Alternative energy sources amidst gas price increase
• The application of energy transformation methods in respect to hydrogen energy
• AC systems and thermal storage
• Loading balance using smart grid

Medical Devices & Diagnostics

Medical devices and diagnostics are fast-growing fields with many opportunities for researchers to explore. There are thousands of devices that aid doctors in treating and managing patients. However, it cannot be emphatically stated that all of these devices offer the best results, where research assignments come into play.

As medical devices, medical diagnoses are also A very concentrated research area. Diagnostic research is highly related to medical devices because diagnoses are carried out with modern gadgets being produced by experts.

This section will consider top medical devices and diagnoses research titles in line with recent needs.

• Difference between Medical Devices and Drugs
• How Diagnostics helps treatment in 2023
• The Era of genetics Diagnostics and Discovery of Hidden Vulnerabilities
• How are Medical Smart Carts changing the game of Medicine?
• The Eventuality of AI in Smart Medical Devices
• The Regulation of Medical Devices
• Should Private Diagnoses Be Used for Making Critical Medical Decisions?
• Diagnostic Devices, Genetic Tests, and In Vitro Devices
• 3D & 4D Printing in Biomedicine
• Innovation in Minimally Invasive Therapies, Screening and Biosensing: Complex Networks, Data-driven Models
• Are medical Devices turning the Health Sector into a small interconnected powerhouse?
• Advances in Methods of Diagnostic & Therapeutic Devices
• What are Intra-Body Communication & Sensing?
• Smart Gadgets Data Collection in terms of Neuroscience
• The Contribution of Smartphone-Enabled Point-of-Care Diagnostic & Communication Systems

Pharmaceutical Technology Research Topics

Medicine has continued to improve, with technology in this area spiking in the last 20 years than it did centuries before. Pharmaceutical technology is one of the major flag bearers of this growth. As the COVID-19 pandemic revealed, the potential of pharmaceutical technology knows no bounds as long as there is continuous research in the field.

With that being the case, there are multiple research titles and projects available to take on in this field, with the opportunity almost endless. This section lists some of these topics to help research students get great topics that they can work on for the best effect. While they are only 15, they all cover a large scope of inexhaustible topics, leaving the researcher to make their choice.

• The technologies of pharmaceuticals and their specialty medications
• The technology and trend of prior electronic authorization in pharmacy
• Medication therapy management and its effectiveness
• Electronic prescription of a controlled substance as regards the issues of drug abuse
• Health information exchange and medication therapy management
• How efficient and effective is a drug prescription monitoring program?
• The script standard of NCPDP for specialty pharmacies
• The patient’s interest in real-time pharmacy
• AIDS: development of drugs and vaccines
• Pharmaceutical technologies and data security
• The DNA library technology: an overview
• The impact of cloud ERP in the pharmaceutical industry
• Cannabidiol medication in pain management and the future
• Pharmaceutical research with phenotypic screening
• The benefits of cloud technology for small pharmaceutical companies

Food Technology Research Topic

Food research assignments and thesis have been going on for decades and even centuries due to their importance to living organisms. In 2023, this trend is expected to continue with more research topics to explore. Here are some amazing topic ideas that you can choose from and offer a mind-blowing research assignment.

• The types of machines used in the food industry
• 3D printing and the food industry
• Micro packaging and the future
• The impacts of robots as regards safety in butchery
• Swallowing disorder: 3D printed food as a solution
• Food technology and food waste: what are the solutions
• Biofilms and cold plasma
• Drones and precision agriculture
• Food industry and the time-temperature indicators
• Preservatives, additives, and the human gut microbiome
• Hydroponic and conventional farming
• The elimination of byproducts in edible oil production
• The baking industry and the newest technology
• Electronic nose in agriculture and food industry
• Food safety

Educational Technology Research Topic

As far as college students are concerned, technology in education and its subsequent research is the biggest assignment and thesis they have to consider. Education technology has continued to grow, with many gadgets and smart equipment introduced to facilitate better learning.

This section will consider some of the major education research titles that technology students can pick and provide excellent research.

• How is computational thinking improving critical thinking among students
• The effect of professional learning for college student
• The impact of technology in educational research
• The relevance of technology in advancing scientific research
• Virtual reality and its role in helping student understand complex concepts
• Global learning through technology and how it affects education standards
• Data centers and their role in education
• Cultural competence and socio-emotional learning
• Artificial intelligence and educational system
• Is the development of sufficient national capacities related to science, technology, and innovation possible?
• How inclusive is the architecture of learning systems?
• Student-centered learning
• The impact of connectivity for schools and learning, especially in rural environments
• Energy sources: their technological relativity and use in education
• Community college: advantages and disadvantages

Controversial Technology Research Topics

As the name suggests, Controversial technology topics are among the most researched in science. How good is technology considering its effects on the global world and nature? This argument is the foundation of Controversial technology topics. See 15 different technology topics to choose from as you start your research assignment.

• Can Human Trials Improve Drugs and Medicines Faster?
• The Legality of Euthanasia and Assisted Killings in Medicine
• Why Kids should not be exposed to the Internet and Social Media Gadgets in Their Earlier Years
• How Is Technology Destroying the World’s Ecology?
• Is Technology Leading the Destruction of the World’s Climate?
• How Has Technology Increased Radiation and the Depletion of the World?
• Does Technology Increase Gang Initiation due to Internet Access?
• How Social Media Increases the Rate of Children and Young Adults Death?
• The Relationship Between Technology and Depression
• Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR): Editing the Human Genome
• The Possible Devastation of World from High-Tech Military Weapons
• Space Colonization: How it is Good and Bad for Mother Earth
• Are Law Enforcement use of Hidden Cameras an Encroachment of Privacy?
• How Virtual Reality Can Become the New Reality If Developed?
• The Wins of Cochlear Implant Research

Transportation Technology Research Topics

Transportation technology research titles are among the hottest categories for students currently. See 15 best research topics for tech and science-related research to pick from.

• Are Computerized self-driving Cars Safe?
• The development and Advantages of hybrid cars and Electric cars
• How to Protect your smart car from hijackers and Car Thieves?
• Will the next-generation Cars Have Reliable GPS devices and Replace Drivers?
• The Evolution of High-speed rail networks and How They Change Rail Transport
• Driving and Using Cell phones: The Global Stats of Cell Phone Related Auto Accidents
• Is Teleportation an Impossible Physics?
• Will Gyroscopes be the new convenient solutions for public transportation?
• Will Logistics Companies be More Efficient With Electric Trucks?
• How Carbon fiber Serves as an optional material for unit load devices
• The Benefits of Advanced Transport Management Systems (TMS)?
• Can Solar Roadways Become More Cost-Effective?
• Does Technology Provide the Possibility of Water Powered Cars?
• How AI has Penetrated the Transport System and Make It More Effective
• Speed and Safety: How Technology Has Revolutionized Transport Systems

Information Communication Technologies (ITC) Research Topics

ICT is arguably the biggest field of technology, thanks to the amazing developments that have been achieved over the years in the field. ICT plays a major role in different areas of human life. This includes the area of TELECOMs, Education, Family, and Industries.

This section will consider 15 major technology titles on ICT to help students get topics to work on.

• How is technology improving Humans reading ability?
• Do online formats of readable information encourage readers to skim through instead of Understanding the Topics?
• How has technology made it extremely easy to get information in Seconds: a good or bad development?
• The Misconception of Gauging Intelligence?
• How are Internet Search Engines changing us?
• The introduction of ICT and new technologies in Education and How they improve Students’ learning
• Is it worrisome that schools and Colleges now educate students via iPads, social media, Smart Boards, and other new Applications?
• Did the Digital Age trigger any loss of information and Unique Intelligence that conventional and Traditional Learning and research methods provided in the Old era?
• Do Search Engines and Web2 Platforms censor information, leave users blindsided, and Keep them in the Dark?
• Should Encyclopedia sites such as Wikipedia be Regulated because of the High Risk of it Providing Wrong Information to the Public?
• Are Blogs and Online Websites Better than Books?
• The Importance of Traditional Researching and learning in a Highly Digital World
• Do PDFs and Other Electronic Books Promote Short Attention Span?
• Are Tech-Savvy generations dumber or Smarter?
• Should Schools Become Fully Digitalized?

This article shows that technology research papers require a good understanding of technological and scientific concepts. That way, people can easily understand the basis of an assignment. They know how to draft the topic and scope. They also get excellent resources for completing the projects.

This article explained what technological research papers are. It explained how to write them and listed many topic examples people can use for their projects. Therefore, if you follow all the information discussed in this article, you will get top technology ideas for research.

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Welcome to ictresearchmethods.nl

As an ICT student or professional, you need to solve all kind of ICT challenges. Answering the questions and tackling the problems or opportunities of your ICT project requires research and often a combination of various ICT research methods. The toolkit on this website offers you a set of possible research methods and a framework to select the appropriate (combination of) methods .

Make sure to combine strategies to tackle trade-offs between overview and certainity , and between expertise and fit . Also think of mixing methods that are more data- or inspiration oriented. The Research Pattern Navigator helps you to combine strategies.

In most cases, every phase of your project requires research. An example of phasing and possible methods can be found here . You can also start at this list of methods ordered by research strategy .

Note that a physical copy of the card set can be obtained from Studie Bijdehand .

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Choose Your Test

Sat / act prep online guides and tips, 113 great research paper topics.

General Education

One of the hardest parts of writing a research paper can be just finding a good topic to write about. Fortunately we've done the hard work for you and have compiled a list of 113 interesting research paper topics. They've been organized into ten categories and cover a wide range of subjects so you can easily find the best topic for you.

In addition to the list of good research topics, we've included advice on what makes a good research paper topic and how you can use your topic to start writing a great paper.

What Makes a Good Research Paper Topic?

Not all research paper topics are created equal, and you want to make sure you choose a great topic before you start writing. Below are the three most important factors to consider to make sure you choose the best research paper topics.

#1: It's Something You're Interested In

A paper is always easier to write if you're interested in the topic, and you'll be more motivated to do in-depth research and write a paper that really covers the entire subject. Even if a certain research paper topic is getting a lot of buzz right now or other people seem interested in writing about it, don't feel tempted to make it your topic unless you genuinely have some sort of interest in it as well.

#2: There's Enough Information to Write a Paper

Even if you come up with the absolute best research paper topic and you're so excited to write about it, you won't be able to produce a good paper if there isn't enough research about the topic. This can happen for very specific or specialized topics, as well as topics that are too new to have enough research done on them at the moment. Easy research paper topics will always be topics with enough information to write a full-length paper.

Trying to write a research paper on a topic that doesn't have much research on it is incredibly hard, so before you decide on a topic, do a bit of preliminary searching and make sure you'll have all the information you need to write your paper.

#3: It Fits Your Teacher's Guidelines

Don't get so carried away looking at lists of research paper topics that you forget any requirements or restrictions your teacher may have put on research topic ideas. If you're writing a research paper on a health-related topic, deciding to write about the impact of rap on the music scene probably won't be allowed, but there may be some sort of leeway. For example, if you're really interested in current events but your teacher wants you to write a research paper on a history topic, you may be able to choose a topic that fits both categories, like exploring the relationship between the US and North Korea. No matter what, always get your research paper topic approved by your teacher first before you begin writing.

113 Good Research Paper Topics

Below are 113 good research topics to help you get you started on your paper. We've organized them into ten categories to make it easier to find the type of research paper topics you're looking for.

Arts/Culture

• Discuss the main differences in art from the Italian Renaissance and the Northern Renaissance .
• Analyze the impact a famous artist had on the world.
• How is sexism portrayed in different types of media (music, film, video games, etc.)? Has the amount/type of sexism changed over the years?
• How has the music of slaves brought over from Africa shaped modern American music?
• How has rap music evolved in the past decade?
• How has the portrayal of minorities in the media changed?

Current Events

• What have been the impacts of China's one child policy?
• How have the goals of feminists changed over the decades?
• How has the Trump presidency changed international relations?
• Analyze the history of the relationship between the United States and North Korea.
• What factors contributed to the current decline in the rate of unemployment?
• What have been the impacts of states which have increased their minimum wage?
• How do US immigration laws compare to immigration laws of other countries?
• How have the US's immigration laws changed in the past few years/decades?
• How has the Black Lives Matter movement affected discussions and view about racism in the US?
• What impact has the Affordable Care Act had on healthcare in the US?
• What factors contributed to the UK deciding to leave the EU (Brexit)?
• What factors contributed to China becoming an economic power?
• Discuss the history of Bitcoin or other cryptocurrencies  (some of which tokenize the S&P 500 Index on the blockchain) .
• Do students in schools that eliminate grades do better in college and their careers?
• Do students from wealthier backgrounds score higher on standardized tests?
• Do students who receive free meals at school get higher grades compared to when they weren't receiving a free meal?
• Do students who attend charter schools score higher on standardized tests than students in public schools?
• Do students learn better in same-sex classrooms?
• How does giving each student access to an iPad or laptop affect their studies?
• What are the benefits and drawbacks of the Montessori Method ?
• Do children who attend preschool do better in school later on?
• What was the impact of the No Child Left Behind act?
• How does the US education system compare to education systems in other countries?
• What impact does mandatory physical education classes have on students' health?
• Which methods are most effective at reducing bullying in schools?
• Do homeschoolers who attend college do as well as students who attended traditional schools?
• Does offering tenure increase or decrease quality of teaching?
• How does college debt affect future life choices of students?
• Should graduate students be able to form unions?

• What are different ways to lower gun-related deaths in the US?
• How and why have divorce rates changed over time?
• Is affirmative action still necessary in education and/or the workplace?
• Should physician-assisted suicide be legal?
• How has stem cell research impacted the medical field?
• How can human trafficking be reduced in the United States/world?
• Should people be able to donate organs in exchange for money?
• Which types of juvenile punishment have proven most effective at preventing future crimes?
• Has the increase in US airport security made passengers safer?
• Analyze the immigration policies of certain countries and how they are similar and different from one another.
• Several states have legalized recreational marijuana. What positive and negative impacts have they experienced as a result?
• Do tariffs increase the number of domestic jobs?
• Which prison reforms have proven most effective?
• Should governments be able to censor certain information on the internet?
• Which methods/programs have been most effective at reducing teen pregnancy?
• What are the benefits and drawbacks of the Keto diet?
• How effective are different exercise regimes for losing weight and maintaining weight loss?
• How do the healthcare plans of various countries differ from each other?
• What are the most effective ways to treat depression ?
• What are the pros and cons of genetically modified foods?
• Which methods are most effective for improving memory?
• What can be done to lower healthcare costs in the US?
• What factors contributed to the current opioid crisis?
• Analyze the history and impact of the HIV/AIDS epidemic .
• Are low-carbohydrate or low-fat diets more effective for weight loss?
• How much exercise should the average adult be getting each week?
• Which methods are most effective to get parents to vaccinate their children?
• What are the pros and cons of clean needle programs?
• How does stress affect the body?
• Discuss the history of the conflict between Israel and the Palestinians.
• What were the causes and effects of the Salem Witch Trials?
• Who was responsible for the Iran-Contra situation?
• How has New Orleans and the government's response to natural disasters changed since Hurricane Katrina?
• What events led to the fall of the Roman Empire?
• What were the impacts of British rule in India ?
• Was the atomic bombing of Hiroshima and Nagasaki necessary?
• What were the successes and failures of the women's suffrage movement in the United States?
• What were the causes of the Civil War?
• How did Abraham Lincoln's assassination impact the country and reconstruction after the Civil War?
• Which factors contributed to the colonies winning the American Revolution?
• What caused Hitler's rise to power?
• Discuss how a specific invention impacted history.
• What led to Cleopatra's fall as ruler of Egypt?
• How has Japan changed and evolved over the centuries?
• What were the causes of the Rwandan genocide ?

• Why did Martin Luther decide to split with the Catholic Church?
• Analyze the history and impact of a well-known cult (Jonestown, Manson family, etc.)
• How did the sexual abuse scandal impact how people view the Catholic Church?
• How has the Catholic church's power changed over the past decades/centuries?
• What are the causes behind the rise in atheism/ agnosticism in the United States?
• What were the influences in Siddhartha's life resulted in him becoming the Buddha?
• How has media portrayal of Islam/Muslims changed since September 11th?

Science/Environment

• How has the earth's climate changed in the past few decades?
• How has the use and elimination of DDT affected bird populations in the US?
• Analyze how the number and severity of natural disasters have increased in the past few decades.
• Analyze deforestation rates in a certain area or globally over a period of time.
• How have past oil spills changed regulations and cleanup methods?
• How has the Flint water crisis changed water regulation safety?
• What are the pros and cons of fracking?
• What impact has the Paris Climate Agreement had so far?
• What have NASA's biggest successes and failures been?
• How can we improve access to clean water around the world?
• Does ecotourism actually have a positive impact on the environment?
• Should the US rely on nuclear energy more?
• What can be done to save amphibian species currently at risk of extinction?
• What impact has climate change had on coral reefs?
• How are black holes created?
• Are teens who spend more time on social media more likely to suffer anxiety and/or depression?
• How will the loss of net neutrality affect internet users?
• Analyze the history and progress of self-driving vehicles.
• How has the use of drones changed surveillance and warfare methods?
• Has social media made people more or less connected?
• What progress has currently been made with artificial intelligence ?
• Do smartphones increase or decrease workplace productivity?
• What are the most effective ways to use technology in the classroom?
• How is Google search affecting our intelligence?
• When is the best age for a child to begin owning a smartphone?
• Has frequent texting reduced teen literacy rates?

How to Write a Great Research Paper

Even great research paper topics won't give you a great research paper if you don't hone your topic before and during the writing process. Follow these three tips to turn good research paper topics into great papers.

#1: Figure Out Your Thesis Early

Before you start writing a single word of your paper, you first need to know what your thesis will be. Your thesis is a statement that explains what you intend to prove/show in your paper. Every sentence in your research paper will relate back to your thesis, so you don't want to start writing without it!

As some examples, if you're writing a research paper on if students learn better in same-sex classrooms, your thesis might be "Research has shown that elementary-age students in same-sex classrooms score higher on standardized tests and report feeling more comfortable in the classroom."

If you're writing a paper on the causes of the Civil War, your thesis might be "While the dispute between the North and South over slavery is the most well-known cause of the Civil War, other key causes include differences in the economies of the North and South, states' rights, and territorial expansion."

#2: Back Every Statement Up With Research

Remember, this is a research paper you're writing, so you'll need to use lots of research to make your points. Every statement you give must be backed up with research, properly cited the way your teacher requested. You're allowed to include opinions of your own, but they must also be supported by the research you give.

#3: Do Your Research Before You Begin Writing

You don't want to start writing your research paper and then learn that there isn't enough research to back up the points you're making, or, even worse, that the research contradicts the points you're trying to make!

Get most of your research on your good research topics done before you begin writing. Then use the research you've collected to create a rough outline of what your paper will cover and the key points you're going to make. This will help keep your paper clear and organized, and it'll ensure you have enough research to produce a strong paper.

What's Next?

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Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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Top 10 Research Topics For Students In 2024

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Research Beyond the Obvious!

Are you struggling to find a topic that can unearth new findings? Even before starting, many students feel drowning with the mere task of sorting out the best research topics. Don’t sweat it! This blog explores the top 10 research topics for students, with a focus on different subjects, including psychology, social sciences, etc. From exploring the impact of AI to dealing with social issues, let’s discover good ideas for a research paper! 

Why Do You Need to Find a Research Topic?

Before we get down to the top 10 research topics for students, let’s understand what they are. Research topics help students to drill down into a subject and break down a wide aspect into smaller things. The topics serve the purpose of bringing fresh perspectives to the table and point out a potential knowledge gap or core problem. 

Research Topic vs. Research Question 

Going by its definition, a research topic focuses on a broad theme that calls for deep investigation. On the other hand, a research question is a particular query that researchers use to find plausible answers and new scopes. While you may be busy finding the top 10 research topics for students in college or senior high school, always remember that the topic reflects an aspect of a subject. 

Factors to Choose Research Project Topics 

The key to finding the top 10 research topics may leave you confused but don’t worry. The table below portrays the characteristics of interesting research paper topics that you must keep in mind: 

What Makes a Good Research Topic

While we will give you some ideas about the top 10 research topics for students, you still need to pick one. However, getting closer to this sole topic may feel soul-crushing! Don’t worry; these tips will help you select the best research topics for students. 

1. Focus on Personal Interests

The research topics for students usually stem from what motivates them. If you are interested in a specific field, you can go forward with the topic as long as it is relevant to your field. However, this does not mean you can overlook potential biases - being too close to the subject might even lead you nowhere. 

2. Check the Guidelines

When looking for the top 10 research topics for students, it’s imperative to adhere to guidelines laid out by your school. Sometimes, they approve good topics for research papers only if they are related to the public interest or environment. Ask your professor/mentor whether you need to follow certain guidelines while finding the best research topics for students. 

3. Availability of Resources  

Your research project might never see the daylight if you do not have enough resources available. Make sure the resources are within your limits! In case your research has funding, always check how you will be able to use it. Finances, access to participants, and timings are key factors in finalising among the top 10 research topics for students. 

Top 10 Research Topics for Students in College and Senior High School

Here, we will delve into the top 10 research topics for students. Whether you are in a college or senior high school, these topics will show you light at the end of the tunnel. You might find inspiration from these topics and may even come up with original research topics and research questions. So, let’s unveil the best research topic ideas for students! 

1. Psychology Research Paper Topics 

Psychology papers offer an exciting opportunity to explore and understand the human psyche. Emerging technologies and their impact on mental health is one of the best research topics for students, yet there are more you can explore. Below, are some of the best research topics for students: 

1. The impact of social media on mental health among adolescents and young adults.

2. The potential benefits and risks of virtual reality therapy for mental health conditions. 

3. The ethical considerations of using AI in psychological treatment. 

4. The influence of mindfulness practices on cognitive performance and well-being.

5. The link between sleep quality and cognitive decline in ageing populations.

2. Business and Economics Research Topics 

From sustainable business practices to global trade dynamics, the best research topics for students regarding business and economics revolve around many areas. While you may initially find it challenging how to conduct research , you can draw inspiration from these topics for research paper:

1. The impact of AI on various aspects of business, such as marketing and financial analysis.

2. The ethical considerations and challenges associated with the use of big data and analytics in business practices.

3. The potential of blockchain technology to revolutionise supply chain management and improve data privacy.

4. The effectiveness of policy interventions to promote sustainable economic growth & development.

5. Exploring the factors that contribute to the success of startups and new ventures in the digital age.

3. Social Sciences Research Topics 

Social sciences deal with the study of human behaviour and explore socioeconomic inequalities, political ideologies, urban development, and more. If you are looking for good ideas for a research paper regarding social sciences, here are some: 

1. The effectiveness of different social policy interventions aimed at addressing global issues. 

2. The potential of blockchain technology to improve transparency and accountability in social structures.

3. The social and ethical implications of artificial intelligence (AI) in various aspects of life. 

4. The psychological and social impacts of climate change on individuals and communities. 

5. The increasing focus on interdisciplinary research that combines social science with data science.

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4. Language and Linguistics Research Topics 

From computational linguistics to semantics to language preservation, the field of language  leads to some really good topics for research papers. While going through our list of top 10 research topics for students, you can already grasp that there are a few things to keep in mind when writing a college paper ! So, here are the best research topic ideas for students regarding language studies: 

1. The influence of social media and digital communication on language use and evolution.   

2. The impact of language learning apps and online platforms on pedagogy. 

3. The role of language in perpetuating social inequalities.

4. The link between language and mental health in the context of cultural displacement.

5. The potential of multilingualism to enhance cognitive abilities. 

5. Health and Medicine Research Topics

Finding the best research topics for students is daunting when it comes to a dynamic field like health and medicine. After going through this compilation of the top 10 research topics for students, you can understand how to come up with the right one. Here, based on the emerging areas of interest, we share some of the potentially impactful and the best research ideas for students: 

1. The integration of AI in medical diagnosis and treatment. 

2. Investigating the ethical considerations of using AI in the healthcare sector. 

3. The developing field of preventive health measures and promoting healthy lifestyles.

4. The link between social determinants of health and mental well-being. 

5. Improving access to healthcare and promoting health equity in minority communities.

6. Renewable Energy & Clean Technologies Research Topics

Initially, you might find it impossible to understand how to write a research paper for college , but these top 10 research topics for students will have you covered. Especially when your focus is on clean energy sources and the emission of greenhouse gases, there is a lot to cover nowadays. Here are some of the best research topics for students: 

1. The potential of next-generation solar cell technologies. 

2. The social and environmental aspects of renewable energy deployment.

3. Discovering the potential of decentralised energy systems.

4. The potential of hydrogen energy, including production, storage, and utilisation.

5. The impact of climate change on renewable energy resources.

7. Technology and Innovation Research Topics 

The sector for technology is ever-evolving, with innovations taking place every other. With the emergence of IoT, artificial intelligence, and ML, the world of technology is your oyster. Here are the best research topics for students: 

1. The societal implications of AI in healthcare, finance, and autonomous vehicles. 

2. The potential of blockchain technology to revolutionise cybersecurity and voting systems.

3. Innovative solutions to combat climate change, including renewable energy technologies and sustainable infrastructure. 

4. The role of technology in disaster preparedness and risk management. 

5. The use of technology to bridge the digital divide and ensure equitable access to information. 

8. Arts and Design Research Topics 

Whether your niche lies in art therapy, cultural studies in arts, or architecture innovation, there are interesting research paper topics. While exploring the top 10 research topics for students, constructing research may seem difficult – going through the research design - elements and characteristics can solve your problems. So, here are the best research topics for students in college: 

1. The impact of AI on artistic creation. 

2. The use of virtual reality and augmented reality in storytelling. 

3. The role of art in addressing social and environmental challenges. 

4.  The use of art as a tool for social commentary and activism. 

5. The evolving nature of art museums and galleries in the digital age.

9. Argumentative Research Topics 

Building a specific argument and exploring topics can bring you some unique topics for research paper. Through these top 10 research topics for students, you can evaluate human interest on a global scale and beyond. Let’s have a look at these best research topics for students: 

1. Is universal basic income a viable solution to poverty?

2. Is nuclear power a solution to the global energy crisis?

3. Does increased global cooperation offer a solution to climate change?

4. The impact of automation and AI on the future of work and employment.

5. The ethical implications of gene editing and other emerging biotechnologies.

10. Human Rights Research Paper Topics  

Our list of top 10 research topics remains incomplete without human rights. This field is evolving and has become a growing interest for everyone around the world. If you want to probe questions about gender equality or privacy rights, here are a few of the research title examples for students: 

1. The role of human rights defenders and activists in promoting social change. 

2. The human rights of marginalised groups, such as LGBTQ+ individuals and people with disabilities.

3. The impact of AI on human rights, including issues of bias and discrimination.

4. Examining the ethical implications of facial recognition technology. 

5. Exploring the human rights implications of environmental pollution and toxic waste disposal. 

Now that you have an idea about some of the top 10 research topics for students, we hope you come up with an original one. Remember, successful research always starts with the right question. Take time, dig deep into the relevant theories, and find thought-provoking topics for research papers. Meanwhile, don’t overlook the power of how to write a research paper appendix and how to create the right structure for the paper. You can also check out amber+ for essential tools that can help make your thesis writing process smoother! So, happy researching! 

Frequently Asked Questions

What are the top 10 research topics for students, what are some easy yet good research topic ideas for students, what is a good research topic, how to find research topic ideas for students in college, what are the rules for choosing good research project topics.

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177 of the Finest Technology Research Topics in 2023

We live in a technological era, and you can be sure of being asked to write a technology-oriented paper. Despite the contrary opinion that this is one of the most complicated tasks, students can comfortably develop a professional topic about technology for writing a research paper . In a technology research paper, students are tasked with exploring the various aspects of technology, such as inventions, their impacts, and emerging challenges. Since almost every sphere of life encompasses technology, it is nearly impossible to miss out on a technology topic or two.

High-quality technology research paper topics should: Demonstrate your understanding of various technological concepts Portray your ability to apply these concepts to real-life situations Show how technology impacts society.

The task of coming up with technology topics involves the following stages:

• Extensively reading on technology
• Identifying distinct technological aspects
• Brainstorming on potential technology titles for your paper
• Consulting your supervisor

The last step is essential in ensuring that your topic aligns with the academic standards of your institutions. Have a look at the following writing prompts for your inspiration:

Medical laboratory Technology Research Topics

• The role of technological innovations in the medical laboratories
• Cost-saving technologies in the field of medical laboratory
• A comparative analysis of the current techniques in the microbial examination
• The role of technology in the isolation and identification of nematodes
• The effects of 5G on the study of cancerous cells
• Evaluating the concentration of electrolytes using technology
• Describe the various parameters used in biochemical reactions
• A comparative analysis of the activities of cells under a light microscope
• Assess the various technologies used to view microscopic organisms
• An evaluation of the role of technology in combating COVID-19

Interesting Information Technology Topics

• Challenges facing cloud computing and virtualization
• Various Federal information standards that affect information technologies
• Discuss the various identity and access management practices for information technologies
• Why the male dominates the field of computational science
• Analyze the various cybersecurity issues arising
• Evaluate the various challenges associated with software research
• Why is the field of networking prone to attacks?
• Health issues arising from the use of biometrics in companies
• Why data entry is attracting a large number of interested parties
• The role of the Internet of Things is transforming the world.

Argumentative Technology Topics

• Why mobile devices can be both instruments and victims of privacy violations
• Why PINs and passwords for mobile devices are a security threat
• The impact of downloading malware disguised as a useful application
• Reasons why out-of-date operating systems are a threat to your computer’s security
• Why it is not advisable to use wireless transmissions that are not always encrypted
• Changes in workflow and project management arising from technological advancements
• The best method to develop and implement cloud solutions for companies
• The cost of having cloud engineers and support professionals
• The role of workplace monitoring in interfering with people’s privacy
• Why information technology laws vary from one country to another

Trending Topics in Technology

• Why technology is essential for an informed society
• The impact of freedom of speech on social networking sites
• Was Facebook justified in blocking Donald Trump from its platform?
• Ethical challenges arising from the new technological innovations
• Why it is not possible to achieve social media privacy
• The impact of online learning sites on the quality of workplace professionals
• Are electric cars the future of the world
• Reasons why technology is essential in developing coronavirus vaccines
• Discuss the various aspects of the Internet of Behaviours (IoB)
• Strides made in the development of intelligent process automation technologies

Hot Research Proposal Topics in Information Technology

• Discuss the considerations in developing human augmentation technologies
• Will big data analytics survive in the future?
• Is it possible to achieve a paper-free world?
• Long-term effects of over-dependence on technology
• Is technology solving world problems or creating more of them?
• What is the impact of children growing up in a technology oriented world?
• How was social media responsible for the chaos at the US Capitol?
• Is it right for governments to monitor and censor citizen’s access to the internet?
• The impact of texting and calling on family relationships
• What are the implications of depending on online thesis help?

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Top-notch Research Paper Topics on Technology

• The impact of Genetically Modified organisms on the health of a population
• Compare and contrast the functioning of the human brain to that of a computer.
• The role of video games on a person’s problem-solving skills
• Where is technology taking the world in the next ten years?
• What digital tools make people less productive?
• What censorship mechanisms are needed to control people’s behavior on the internet?
• The impact of digital learning on schools
• Why is genetic testing essential for couples?
• Discuss the ethical implications of mechanical reproduction
• Discuss the role of innovations in finding treatment for terminal diseases

Latest Research Topics About technology

• The impact of computers in academic research
• Why artificial intelligence may not be the best option for our daily lives
• Should parents restrict the amount of time spent on the internet by their kids?
• What are the legal and moral implications of digital voting?
• Is augmented reality the new way of online shopping?
• Discuss the challenges that arise from game addiction
• Evaluate the safety of VPNs in a global enterprise
• Why is streaming becoming the best option for church services?
• Discuss the efficiency of working from home versus physically going to the workplace
• The effects of computer-generated imagery in films and games

Controversial Technology Topics

• Does online communication make the world bigger or smaller?
• What is the ethical implication of having ID chips in our brains?
• Should families use gene editing for coming up with children of desirable qualities?
• Are the cybersecurity laws punitive enough?
• Is cryptocurrency turning around the financial industry for the worst?
• Are self-driving vehicles safe on our roads?
• Is it possible to attain self-awareness using Artificial Intelligence technologies?
• The risk of x-rays on a person’s health
• Is it possible for robots to live peacefully with humans?
• Compare and contrast between machine learning and natural language processing

Impressive Technology Topic Ideas for High School

• The impact of developing autonomous cars using computer vision applications
• Discuss the interconnection between the internet of things and artificial intelligence
• The effects of ultra-violet technologies in the health industry
• The impact of communication networks on people’s attitudes
• The role of internet technologies on marketing and branding
• How has the world of music changed with the emergence of video editing technologies?
• Describe the psychology behind video blog communication
• Effective ways of maintaining privacy in social media
• Is it possible to live without mass media in the world?
• The impact of technology on the morality of the world in the 21 st century

Educational Technology Topics

• Why is technology relevant in advancing scientific research?
• Discuss how computational thinking is shaping critical thinking among students
• What is the effect of professional learning for college students?
• The role of virtual reality in helping students understand complex concepts
• Is global learning through technology watering down education standards?
• Discuss various energy sources to support technology use in education
• Is the architecture of learning systems inclusive enough?
• Discuss the impact of connectivity for schools & learning, esp. in rural environments
• The role of data centers in education
• Is it possible to develop sufficient national capacities related to science, technology, and innovation?

Updated Technology Related Topics in Agriculture

• The role of soil and water sensors in improving crop yields
• Why farmers rely on weather tracking technologies for their farming activities
• The significant role of satellite imaging in agricultural activities
• How do farmers use pervasive automation technologies for their farms?
• The effect of mini-chromosomal technologies on agriculture
• Why vertical agriculture is the future of agriculture
• Conditions necessary for hydroponics in developed nations
• The impact of agricultural technologies in ensuring stable food supply
• How agricultural technologies can be used to ensure decreased use of water
• Using agricultural technologies to enhance worker safety on the farm

Top Technology Persuasive Speech Topics

• An analysis of digital media outreach and engagement in workplaces
• What are the challenges experienced in distance learning
• Describe personalized and adaptive learning platforms and tools
• Should computer viruses count as life?
• Describe the connection between human perception and virtual reality
• What is the future of computer-assisted education in colleges?
• Analyze the high dimensional data modeling procedure
• Evaluate the imperative and declarative languages in computer programming
• Analyze how the machine architecture affects the efficiency of the code
• What are the discrepancies in different languages for parallel computing?

Latest Controversial Topics in Technology

• Do you think computational thinking affects science?
• An overview of the phishing trends in the recent past
• How are sensor networks a threat to one’s privacy?
• Compare and contrast lithium-ion and lithium-air batteries.
• Can hydrogen replace all other energy sources in the future?
• Discuss the future of tidal power: Will it persist or become extinct?
• Why robots are a threat to the survival of humanity
• Analyze the effectiveness of small nuclear reactors in the wake of climatic change
• An overview of the different types of renewable energy technologies in the world
• Are drones a threat to security or a potential security mechanism?

Hot Topics in Technology

• Discuss the impacts of new technologies on food production and security
• The effectiveness of 3D printing for medical products
• What is the ethical argument behind the production of artificial body organs?
• Discuss the role of genetic engineering in medicine
• Challenges associated with the development of telemedicine
• Conduct a case study analysis on the effectiveness of genome editing
• Discuss the role of nanotechnology in cancer treatment
• The role of virtual reality in medical schools
• Discuss the effectiveness of wireless communication technologies for teenagers
• How safe are you when connected to a wireless network?

Science and Technology Topics

• Analyze the security threats associated with pharmaceutical technologies
• An overview of the chip technology in the practice of medicine
• Compare and contrast between electric cars and hybrid cars
• Why are personal transportation pods the future of transport
• Threats and solutions to cell phone use during driving
• Effects of scientific innovations on the world
• Are water-fueled cars a future fantasy or reality?
• The role of robotics in food packaging
• Modern solar system innovations
• The role of smart energy in combating global warming

Top-Notch Research Topics on Technology

• An overview of the different operating systems
• The role of theoretical computer science
• Discuss the development of computer graphics
• What are the loopholes in block-chain technology?
• Why banking systems need extra security measures
• What is the future of cyber systems?
• Ways of protecting your password from hackers
• The role of ICT in new media technologies
• How to deal with cyberbullying from Twitter
• The future of interpersonal communication with the rise of social media

Researchable Topics About Technology

• Factors that lead to viral messages on Twitter
• Freedom of speech and social media
• Activism in the wake of new media
• Discuss the psychology behind advertising techniques
• Interactive media technologies
• How has the internet changed communication networks?
• Role of media during pandemics
• Ethics in internet technologies
• The persistence of newspapers in the digital age
• Impact of technology on lifestyle diseases

Bonus Technology Topic Ideas

• Agricultural biotechnology
• Gene therapy
• Development of vaccines
• Genome sequencing
• Food processing technologies
• Technology and drugs
• Recommended systems

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100+ Quantitative Research Topics For Students

Quantitative research is a research strategy focusing on quantified data collection and analysis processes. This research strategy emphasizes testing theories on various subjects. It also includes collecting and analyzing non-numerical data.

Quantitative research is a common approach in the natural and social sciences , like marketing, business, sociology, chemistry, biology, economics, and psychology. So, if you are fond of statistics and figures, a quantitative research title would be an excellent option for your research proposal or project.

How to Get a Title of Quantitative Research

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Finding a great title is the key to writing a great quantitative research proposal or paper. A title for quantitative research prepares you for success, failure, or mediocre grades. This post features examples of quantitative research titles for all students.

Putting together a research title and quantitative research design is not as easy as some students assume. So, an example topic of quantitative research can help you craft your own. However, even with the examples, you may need some guidelines for personalizing your research project or proposal topics.

So, here are some tips for getting a title for quantitative research:

• Consider your area of studies
• Look out for relevant subjects in the area
• Expert advice may come in handy
• Check out some sample quantitative research titles

Making a quantitative research title is easy if you know the qualities of a good title in quantitative research. Reading about how to make a quantitative research title may not help as much as looking at some samples. Looking at a quantitative research example title will give you an idea of where to start.

However, let’s look at some tips for how to make a quantitative research title:

• The title should seem interesting to readers
• Ensure that the title represents the content of the research paper
• Reflect on the tone of the writing in the title
• The title should contain important keywords in your chosen subject to help readers find your paper
• The title should not be too lengthy
• It should be grammatically correct and creative
• It must generate curiosity

An excellent quantitative title should be clear, which implies that it should effectively explain the paper and what readers can expect. A research title for quantitative research is the gateway to your article or proposal. So, it should be well thought out. Additionally, it should give you room for extensive topic research.

A sample of quantitative research titles will give you an idea of what a good title for quantitative research looks like. Here are some examples:

• What is the correlation between inflation rates and unemployment rates?
• Has climate adaptation influenced the mitigation of funds allocation?
• Job satisfaction and employee turnover: What is the link?
• A look at the relationship between poor households and the development of entrepreneurship skills
• Urbanization and economic growth: What is the link between these elements?
• Does education achievement influence people’s economic status?
• What is the impact of solar electricity on the wholesale energy market?
• Debt accumulation and retirement: What is the relationship between these concepts?
• Can people with psychiatric disorders develop independent living skills?
• Children’s nutrition and its impact on cognitive development

Quantitative research applies to various subjects in the natural and social sciences. Therefore, depending on your intended subject, you have numerous options. Below are some good quantitative research topics for students:

• The difference between the colorific intake of men and women in your country
• Top strategies used to measure customer satisfaction and how they work
• Black Friday sales: are they profitable?
• The correlation between estimated target market and practical competitive risk assignment
• Are smartphones making us brighter or dumber?
• Nuclear families Vs. Joint families: Is there a difference?
• What will society look like in the absence of organized religion?
• A comparison between carbohydrate weight loss benefits and high carbohydrate diets?
• How does emotional stability influence your overall well-being?
• The extent of the impact of technology in the communications sector

Creativity is the key to creating a good research topic in quantitative research. Find a good quantitative research topic below:

• How much exercise is good for lasting physical well-being?
• A comparison of the nutritional therapy uses and contemporary medical approaches
• Does sugar intake have a direct impact on diabetes diagnosis?
• Education attainment: Does it influence crime rates in society?
• Is there an actual link between obesity and cancer rates?
• Do kids with siblings have better social skills than those without?
• Computer games and their impact on the young generation
• Has social media marketing taken over conventional marketing strategies?
• The impact of technology development on human relationships and communication
• What is the link between drug addiction and age?

Need more quantitative research title examples to inspire you? Here are some quantitative research title examples to look at:

• Habitation fragmentation and biodiversity loss: What is the link?
• Radiation has affected biodiversity: Assessing its effects
• An assessment of the impact of the CORONA virus on global population growth
• Is the pandemic truly over, or have human bodies built resistance against the virus?
• The ozone hole and its impact on the environment
• The greenhouse gas effect: What is it and how has it impacted the atmosphere
• GMO crops: are they good or bad for your health?
• Is there a direct link between education quality and job attainment?
• How have education systems changed from traditional to modern times?
• The good and bad impacts of technology on education qualities

Your examiner will give you excellent grades if you come up with a unique title and outstanding content. Here are some quantitative research examples titles.

• Online classes: are they helpful or not?
• What changes has the global CORONA pandemic had on the population growth curve?
• Daily habits influenced by the global pandemic
• An analysis of the impact of culture on people’s personalities
• How has feminism influenced the education system’s approach to the girl child’s education?
• Academic competition: what are its benefits and downsides for students?
• Is there a link between education and student integrity?
• An analysis of how the education sector can influence a country’s economy
• An overview of the link between crime rates and concern for crime
• Is there a link between education and obesity?

Research title example quantitative topics when well-thought guarantees a paper that is a good read. Look at the examples below to get started.

• What are the impacts of online games on students?
• Sex education in schools: how important is it?
• Should schools be teaching about safe sex in their sex education classes?
• The correlation between extreme parent interference on student academic performance
• Is there a real link between academic marks and intelligence?
• Teacher feedback: How necessary is it, and how does it help students?
• An analysis of modern education systems and their impact on student performance
• An overview of the link between academic performance/marks and intelligence
• Are grading systems helpful or harmful to students?
• What was the impact of the pandemic on students?

Irrespective of the course you take, here are some titles that can fit diverse subjects pretty well. Here are some creative quantitative research title ideas:

• A look at the pre-corona and post-corona economy
• How are conventional retail businesses fairing against eCommerce sites like Amazon and Shopify?
• An evaluation of mortality rates of heart attacks
• Effective treatments for cardiovascular issues and their prevention
• A comparison of the effectiveness of home care and nursing home care
• Strategies for managing effective dissemination of information to modern students
• How does educational discrimination influence students’ futures?
• The impacts of unfavorable classroom environment and bullying on students and teachers
• An overview of the implementation of STEM education to K-12 students
• How effective is digital learning?

If your paper addresses a problem, you must present facts that solve the question or tell more about the question. Here are examples of quantitative research titles that will inspire you.

• An elaborate study of the influence of telemedicine in healthcare practices
• How has scientific innovation influenced the defense or military system?
• The link between technology and people’s mental health
• Has social media helped create awareness or worsened people’s mental health?
• How do engineers promote green technology?
• How can engineers raise sustainability in building and structural infrastructures?
• An analysis of how decision-making is dependent on someone’s sub-conscious
• A comprehensive study of ADHD and its impact on students’ capabilities
• The impact of racism on people’s mental health and overall wellbeing
• How has the current surge in social activism helped shape people’s relationships?

Are you looking for an example of a quantitative research title? These ten examples below will get you started.

• The prevalence of nonverbal communication in social control and people’s interactions
• The impacts of stress on people’s behavior in society
• A study of the connection between capital structures and corporate strategies
• How do changes in credit ratings impact equality returns?
• A quantitative analysis of the effect of bond rating changes on stock prices
• The impact of semantics on web technology
• An analysis of persuasion, propaganda, and marketing impact on individuals
• The dominant-firm model: what is it, and how does it apply to your country’s retail sector?
• The role of income inequality in economy growth
• An examination of juvenile delinquents’ treatment in your country

Excellent Topics For Quantitative Research

Here are some titles for quantitative research you should consider:

• Does studying mathematics help implement data safety for businesses
• How are art-related subjects interdependent with mathematics?
• How do eco-friendly practices in the hospitality industry influence tourism rates?
• A deep insight into how people view eco-tourisms
• Religion vs. hospitality: Details on their correlation
• Has your country’s tourist sector revived after the pandemic?
• How effective is non-verbal communication in conveying emotions?
• Are there similarities between the English and French vocabulary?
• How do politicians use persuasive language in political speeches?
• The correlation between popular culture and translation

Here are some quantitative research titles examples for your consideration:

• How do world leaders use language to change the emotional climate in their nations?
• Extensive research on how linguistics cultivate political buzzwords
• The impact of globalization on the global tourism sector
• An analysis of the effects of the pandemic on the worldwide hospitality sector
• The influence of social media platforms on people’s choice of tourism destinations
• Educational tourism: What is it and what you should know about it
• Why do college students experience math anxiety?
• Is math anxiety a phenomenon?
• A guide on effective ways to fight cultural bias in modern society
• Creative ways to solve the overpopulation issue

An example of quantitative research topics for 12 th -grade students will come in handy if you want to score a good grade. Here are some of the best ones:

• The link between global warming and climate change
• What is the greenhouse gas impact on biodiversity and the atmosphere
• Has the internet successfully influenced literacy rates in society
• The value and downsides of competition for students
• A comparison of the education system in first-world and third-world countries
• The impact of alcohol addiction on the younger generation
• How has social media influenced human relationships?
• Has education helped boost feminism among men and women?
• Are computers in classrooms beneficial or detrimental to students?
• How has social media improved bullying rates among teenagers?

High school students can apply research titles on social issues  or other elements, depending on the subject. Let’s look at some quantitative topics for students:

• What is the right age to introduce sex education for students
• Can extreme punishment help reduce alcohol consumption among teenagers?
• Should the government increase the age of sexual consent?
• The link between globalization and the local economy collapses
• How are global companies influencing local economies?

There are numerous possible quantitative research topics you can write about. Here are some great quantitative research topics examples:

• The correlation between video games and crime rates
• Do college studies impact future job satisfaction?
• What can the education sector do to encourage more college enrollment?
• The impact of education on self-esteem
• The relationship between income and occupation

You can find inspiration for your research topic from trending affairs on social media or in the news. Such topics will make your research enticing. Find a trending topic for quantitative research example from the list below:

• How the country’s economy is fairing after the pandemic
• An analysis of the riots by women in Iran and what the women gain to achieve
• Is the current US government living up to the voter’s expectations?
• How is the war in Ukraine affecting the global economy?
• Can social media riots affect political decisions?

A proposal is a paper you write proposing the subject you would like to cover for your research and the research techniques you will apply. If the proposal is approved, it turns to your research topic. Here are some quantitative titles you should consider for your research proposal:

• Military support and economic development: What is the impact in developing nations?
• How does gun ownership influence crime rates in developed countries?
• How can the US government reduce gun violence without influencing people’s rights?
• What is the link between school prestige and academic standards?
• Is there a scientific link between abortion and the definition of viability?

You can never have too many sample titles. The samples allow you to find a unique title you’re your research or proposal. Find a sample quantitative research title here:

• Does weight loss indicate good or poor health?
• Should schools do away with grading systems?
• The impact of culture on student interactions and personalities
• How can parents successfully protect their kids from the dangers of the internet?
• Is the US education system better or worse than Europe’s?

If you’re a business major, then you must choose a research title quantitative about business. Let’s look at some research title examples quantitative in business:

• Creating shareholder value in business: How important is it?
• The changes in credit ratings and their impact on equity returns
• The importance of data privacy laws in business operations
• How do businesses benefit from e-waste and carbon footprint reduction?
• Organizational culture in business: what is its importance?

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Interesting, creative, unique, and easy quantitative research topics allow you to explain your paper and make research easy. Therefore, you should not take choosing a research paper or proposal topic lightly. With your topic ready, reach out to us today for excellent research paper writing services .

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