Trends and Topics in Educational Technology, 2023 Edition

  • Column: Guest Editors’ Notes
  • Published: 05 April 2023
  • Volume 67 , pages 583–591, ( 2023 )

Cite this article

  • Bohdana Allman 1 ,
  • Royce Kimmons 1 ,
  • Joshua Rosenberg 2 &
  • Monalisa Dash 3  

5588 Accesses

4 Citations

11 Altmetric

Explore all metrics

Avoid common mistakes on your manuscript.


In this editorial, we present trends and popular topics in educational technology for the year 2022. We used a similar public internet data mining approach (Kimmons & Veletsianos, 2018 ) to previous years (Kimmons, 2020 ; Kimmons et al., 2021 ; Kimmons & Rosenberg, 2022 ), extracting and analyzing data from three large data sources: the Scopus research article database, the Twitter #EdTech affinity group, and K-12 school and district Facebook pages. This year, we also added information related to Open Educational Resources (OER), specifically data from an edtech-focused open publishing platform, EdTech Books. Our analysis provides a snapshot of educational technology trends in 2022 from four different perspectives, affording insights into what is of interest in the field as institutions, educators, learners, and researchers adjust to the post-pandemic ‘normal’ and adopt educational technologies, resources, and practices at a more mature level.

What Were Trending Topics in EdTech Journals in 2022?

Research topics in the field of educational technology in 2022 were, with a few exceptions, noticeably consistent with those of previous years (see Table  1 ; Kimmons et al., 2021 ; Kimmons & Rosenberg, 2022 ). We compiled the titles of 2699 articles from top educational technology journals ( n  = 16) identified by Google Scholar and retrieved their abstracts from Scopus. Following this, we looked at the number of times each keyword and bigram (two-word phrase) appeared in the titles and abstracts of the papers to see which words were most frequently referenced. Generic word stems like “learn,” “student,” “education,” and “teach,” modalities like “online” and “digital,” and methods-related terms like “study” and “review” were the most frequently occurring words in titles. Analysis of bigrams showed recurring references to (a) educational settings, like “higher education,” (b) specific modalities like “online learning,” “virtual reality,” and “augmented reality,” and (c) methods, like “systematic review,” “meta-analysis,” and “case study.” Moreover, references to “COVID-19” understandably dropped from 2021 to 2022, while references to “online learning” continued to grow. This may imply that interest in online learning has continued and even grown beyond the pandemic. Appearance of “during+COVID” in the top 15 bigrams in EdTech article titles in 2022 suggested that researchers and practitioners were still reporting on educational practices during the pandemic.

To aid in making sense of the results, we further manually categorized keywords and bigrams into the four information types suggested by the data (contexts, methods, modalities, and topics). Context included terms related to the research settings. Methods included terms referring to research methods in the article. Modalities included terms referring to the technical modality featured in the study. Topics included terms referring to the intervention, objective, or theoretical goal of the study. The most common keywords and bigrams for each type may be found in Table  2 . Contextual bigrams like “higher education” (3.9%) and “COVID-19” (3.6%) were among the most popular bigrams used in educational technology journal article titles in 2022. When we looked specifically at the educational level, we found that references to “higher+education” (3.9%) continued to be considerably higher than to “K-12” (1.2%). The abstract analysis of context bigrams paralleled the title bigram analysis.

A closer analysis of methods mentioned in the titles suggested that the terms “systematic review” (3.1%), “case study” (2.2%), and “meta-analysis” (2%) remained the top three methods mentioned in the journal article titles, just like in previous years., followed by “literature review” (1.5%) and “systematic literature” (1.1%; see Table 2 for details). Rather than assuming that these methods were more prevalent, we recognized that researchers commonly mention these particular methods in their titles, whereas other methods are generally mentioned only in the abstract or in the body of an article. Bigram analyses of abstracts confirmed this notion, suggesting a broader coverage of distinct research approaches, such as “mixed method,” “quasi-experimental,” “randomly assigned,” “pre-post,” “systematic review,” and “meta-analysis.” Amongst the methods, bigrams “mixed method” and “quasi-experimental” occupied the leading position in journal abstracts, each carrying an equal percentage of 4.6%, whereas “systematic review” and “meta-analysis” scored 2.3% and 1.8%, respectively. These results suggested that in 2022 EdTech articles with primary data sources were published more frequently than articles using secondary data sources, although secondary data methods were more frequently mentioned in the article titles. Moreover, quantitative components (e.g., “test,” “experiment,” and “survey”) were found more frequently than qualitative components (e.g., “interview” and “qualitative”) in the 2022 EdTech journal article abstracts. Finally, several specific methods that frequently appeared in the article abstracts included “structure equation,” “thematic analysis,” “equation modeling,” “network analysis,” “data mining,” and “cluster analysis.”

When we looked at modality types, we saw that, similarly to 2021, “online learning” (3.5%) and “virtual reality” (2.7%) were the most referenced modalities mentioned in EdTech journal titles (Table 2 ). In abstracts, the occurrence of “virtual reality,” “online learning,” and “online courses” were far more common than “emergency remote” learning, clearly indicating a post-pandemic adoption of online technologies and an end of pandemic-related emergency remote learning research. Finally, the analysis of topics revealed that “computational thinking” (2.8%) and “learning environments” (2.8%) were the most-referenced bigrams in journal titles (Table 2 ). In the abstracts, the keyword “science” was used 33.9% and “language” 14.6% implying research focus in these content areas. Another noteworthy trend in the topic analysis of article abstracts was the popularity of terms related to Open Educational Resources (OER), specifically, the frequent use of terms such as “creative commons” and “cc license.”

What Were the Trending #EdTech Topics and Tools on Twitter in 2022?

We also continued to analyze trending #EdTech topics on Twitter (cf., Kimmons et al., 2021 ; Kimmons & Rosenberg, 2022 ). In 2022, #EdTech continued to be popular, and its analysis provided a window into relevant conversations, resources, and ideas that researchers and practitioners shared. We collected all English-language original tweets using the hashtag #EdTech for 2022. This included 478,269 original tweets (ignoring retweets) posted by 35,789 authors, which was 39,856 average monthly tweets. This indicated a 10.43% growth in #edtech original tweets (45,191) and average tweets (3766) from 2021, whereas the number of authors declined by 12.21% (4978; cf. Table  3 ).

The increase in total tweets indicated continuous popularity of the #edtech affinity space in general. The growth in tweets despite declining authorship suggested that the loyal authors increased their activity. Decreases in authorship could be connected to the general Twitter struggle to keep its most active users (Dang, 2022 ), but it could also be connected to uncertainties brought on by changes in Twitter ownership. Some users might have become more hesitant tweeters, fearing and anticipating changes in the platform’s nature and culture. Others may have abandoned the platform completely for more deeply-rooted reasons (Sweney, 2022 ). In the future, changes in Twitter ownership may even impact this report. Shifts in the platform’s business model may make data collection less feasible and analyzed information may become less useful.

We also looked at the most popular #EdTech co-occurring hashtags in two categories: audience and topics (see Table  4 ). #edchat remained the most popular co-occurring hashtag in the audience category. Other top hashtags from 2021 representing audience, such as #edutwitter, #teachers, #edtechchat, #students, #highered, and #k12, remained in the top 10 but slightly changed ranking. Interestingly, many top co-occurring hashtags (#edchat, #highered, #k12, #school, #highereducation) experienced at least a 15% reduction in the number of tweets and at least a 20% decrease in authorship. Another noteworthy trend is the appearance of more specialized, audience-related hashtags, such as #homeschool, #homeschooling, #suptchat, and #iste, in the top 50. Such differentiation in hashtag usage may reflect evolving users’ needs and desires (Kimmons & Veletsianos, 2016 ; Veletsianos, 2017 ).

The most popular topic by number of tweets in 2022 was #byjus, a hashtag associated with an educational technology company from India. In spite of its popularity (108,794 or 22.75% of all #edtech tweets), the low diversity score (0.62%) indicated that this hashtag was used by relatively few accounts at high frequencies, likely a result of focused marketing campaigns. This points to the fact that the Twitter space, and #edtech space in particular, can be unduly influenced by corporate influences and marketing. To keep these outliers from our dataset, we determined popularity first through sorting by number of users, then we sorted the top 200 by number of tweets.

We saw similar trends in co-occurring topics. The top ten topics slightly changed order but remained popular overall. The top two hashtags, #education and #learning, remained top ranking, but both experienced a significant loss of total tweets and number of authors. Other top hashtags, such as #technology, #stem, #teaching, and #innovation, had both fewer tweets and fewer authors. The exception was #ai, which had 2908 (25.7%) more tweets despite 484 (22.9%) fewer authors. This may not be a surprising trend as #ai has been gaining popularity in recent years. Other hashtags, such as #artificialintelligence, #machinelearning, #ML, and #mlearning, also appeared in the list. We can probably anticipate a sharp rise in this subgroup’s activity, including #chatGTP and related hashtags, in the #EdTech space in 2023.

As with the audience co-occurring hashtags, there was a clear pattern of emerging specialized topic-related hashtags that modified previously popular ones. For example, the popular term #stem evolved to include #steam, #stemeducation, #stemed, and #womeninstem appearing in the top 100. This differentiation and increased related hashtag usage could be one reason for decreased tweet count for top hashtags in 2022: greater specialization yields lower numbers in the general tags. Users gravitated to related, more specialized hashtags to create more focused dialogic spaces. Additionally, looking at the overall trends in both the audience and topic co-occurring hashtags, we noticed that diversity (#dei, #inclusion, #diversity, #quality, #equity), women (#womenintech, #womeninstem, #womenempowerment), and English language learning (#esl, #tefl, #efl, #elt, #tesol) became increasingly important in the #EdTech space. This specific type of differentiation may reflect the rising importance of these issues to the audience.

Another important trend in the #EdTech space this year was related to COVID-19 hashtags. In 2020, the most popular co-occurring hashtags after #education and #edchat were #remotelearning, #onlinelearning, #elearning, and #distancelearning, making up 11.47% (15,114 tweets by 4600 authors). These hashtags remained very popular in 2021, and together with #virtuallearning, #blendedlearning, #onlineeducation, and #digitallearning made up 16.10% (69,737 tweets by 10,611 authors) of #EdTech, while dropping to a mere 9% (43,034 tweets by 5910 authors) in 2022 (see Fig.  1 ). Clearly, conversations on Twitter paralleled a shift in perspective as we transitioned from the pandemic years. Of note, #elearning and #onlinelearning remained relatively popular (31,029 tweets or 72.1% of the 2022 subset). These two hashtags are more general and may represent the post-pandemic transition into accepting online learning environments and digital courseware (Seaman & Seaman, 2022a ). On the other hand, #remotelearning and #distancelearning, hashtags closely tied to COVID-19 emergency learning, significantly decreased in usage (76.6% and 69.2%, respectively) in 2022.

figure 1

COVID-19 Related Tweets in the #EdTech Affinity Space

Our #EdTech tweet analysis also examined attached external links. We found that 454,258 (95.0%) tweets included either an external link or an embedded media item (e.g., an image). Similarly, as in the past, prominent external links included news sites ( , , ), specifically those connected to India ( , , and ). Multimedia resources ( ), file-sharing platforms ( ), and other social media ( ) links were also among the most common external links. Noteworthy among the top shared external links is the increased popularity of links to learning resource sites, such as , , , and .

What Were Trending Topics among School and School District Facebook Groups in 2022?

To understand which technologies were shared on school and district Facebook pages, we examined the domain names for all the hyperlinks posted by 16,309 publicly accessible pages. To carry out this analysis, we searched the homepages of all of the schools and school districts in the U.S. for links to Facebook pages. We then uploaded the links to Facebook pages we found to the CrowdTangle platform Footnote 1 to access publicly available posts for 2020–2022 and identified the domains of websites linked within schools’ and districts’ posts; more information on the data collection approach is provided in Rosenberg et al. ( 2022 ). The ten most-shared domains broken down by year (2020, 2021, and 2022) are presented in Table  5 . The n represents the number of schools or districts sharing one or more links to these domains, and the percentage is the proportion of pages sharing one or more links that year. Thus, 9705 is the frequency with which links to YouTube were shared in 2020, and the percentage indicates that 60% of schools and districts with publicly accessible Facebook pages posted one or more links to YouTube over the year.

Looking across the years, we found that domains shared were largely consistent, with Google services—YouTube, Google Docs, and Google Drive—being the most shared in 2020, 2021, and 2022. We note that a greater proportion of districts shared links to YouTube in 2020 than in 2021 and 2022, possibly due to fewer activities being recorded and shared during the months following the beginning of the COVID-19 pandemic, specifically, late 2019 and early 2020. After Google services, links to Zoom were commonly shared the fourth-most across all three years, though the number of districts sharing Zoom links decreased from 26% in 2020 and 21% in 2021 to 11% in 2022—like fewer links to YouTube, a suggestion that districts were carrying out fewer activities remotely. Links to the CDC were the eighth-most shared in 2020, but such links were not in the top ten in 2021 and 2022. Apart from these, the domains shared were similar in makeup and frequency across years, showing the importance of tools for carrying out digital work and productivity as well as tools to facilitate event sign-ups (SignUpGenius), school-parent communication (Smore), and book and sports ticket sales (Scholastic and GoFan).

What Were Trends in EdTech Open Educational Resources (OER) in 2022?

In addition to Scopus and social media trends, we also examined an EdTech-focused Open Educational Resource (OER) platform EdTech Books ( ). OER are “teaching, learning, and research materials that reside in the public domain or have been released under an open license that permits their free use and re-purposing by others” (Creative Commons, 2020 ). OER can take various forms and sizes, including textbooks, lessons, courses, learning activities, assessments, technologies, syllabi, images, presentations, videos, and graphics. Being ‘open’ means that OER are freely accessible to anyone with internet access and can be retained, reused, redistributed, revised, and remixed as needed (Wiley, n.d. ), providing significant opportunities for improving “the quality and affordability of education for learners everywhere” (Wiley & Hilton, 2018 , p. 144). Research has repeatedly shown that OER quality is comparable to commercial resources (Clinton & Khan, 2019 ; Kimmons, 2015 ), and their adoption does not negatively impact student learning (Hilton, 2016 ; Hilton, 2019 ) while saving students money (Clinton, 2018 ; Hilton, 2016 ; Ikahihifo et al., 2017 ) and providing a variety of other benefits (Kimmons, 2016 ).

Though a shift to OER over the years has been slower than many would like (Seaman & Seaman, 2022b ), and research on adoption patterns is problematized by an absence of central controlling agencies and systems, the field of educational technology may be somewhat ahead of the curve when compared to many other fields (cf., Rosenberg, 2023 ). The emergence of OER platforms like EdTech Books, Pressbooks, and LibreTexts supports this notion. For this year’s OER analysis, we selected EdTech Books as the authors are most familiar with this platform and have ready access to data. We believe that as an EdTech-focused platform, EdTech Books analytics may provide valuable insights into user behavior and how OER are developed, adopted, and used in our field.

In 2022, ETB provided free OER to more than 1.4 million users worldwide. A perusal of the most popular books or journal issues (Table  6 ), chapters (Table  7 ), and search terms revealed that readers seemed to be drawn to these resources when they were seeking information on broad theoretical aspects of educational technology (e.g., cognitivism, constructivism, sociocultural theory), technology-specific guidance (e.g., how to use Blooket, MySQL, or Photopea), or research and evaluation materials (e.g., sampling procedures or survey design), and analysis of end-of-chapter quality assurance ratings (similar to e-commerce five-star reviews) revealed that readers generally found the provided OER to be “High Quality” (3.0 = “Moderate Quality,” 4.0 = “High Quality,” 5.0 = “Very High Quality”).

Some of these works were peer-reviewed, while others were not. Some chapters and books were authored by professional scholars, while others were authored by students as part of open pedagogical learning projects (cf. Casey et al., 2023 ). Notably, some of the most-used and highest-quality OER in EdTech Books were authored by students or were published without peer review. This trend suggests the need to rethink peer reviews as a sole indicator of quality (Woodward et al., 2017 ; Kimmons, 2015 ), potentially including triangulation of data points, such as quality assurance ratings, citations and dissemination rates, times remixed, accessibility, usefulness, and prestige of adopting organizations.

Additionally, one of the stated goals of EdTech Books (and OER more broadly) is to improve access to learning opportunities for people all over the world. Analysis of readers’ country of origin and device type (Fig. 2 ) revealed that EdTech Books resources were heavily used throughout the world and accessed on a variety of devices. The top users of the site were the United States (33.8%), the Philippines (16.6%), and India (6.7%), with each other country accounting for 2.7% or less of total traffic. Moreover, more than one-third of users accessed resources on mobile devices, underscoring the importance of mobile-first design when creating OER because, in many countries, mobile devices with limited internet access are the norm for online-enabled learning.

figure 2

Most Common Countries and Device Types of ETB Users for 2022

Summary and Discussion

The analysis of 2022 edtech-related data from Scopus, Twitter, Facebook, and EdTech Books provided triangulated snapshots of the state of the educational technology field in 2022. Additionally, comparisons of the 2022 data trends to trends from previous years afforded additional insights into developments, directions, and shifts as the EdTech field responds to past and current events. We observed several noteworthy patterns, such as the general stability of trends in the field, specific post-pandemic shifts, the maturation of specialized topics, and emerging areas of interest. We hope that researchers and practitioners find the overall trends useful and those focusing on specific areas find the more detailed analyses of topics and terms helpful.

First, we found that the overall patterns across the platforms remained similar to previous years. The emphasis remained on “e-learning” and “online learning” in Scopus and on Twitter and Facebook. We continued to see a keen interest in emergent technologies, such as artificial intelligence and virtual/augmented reality, in Scopus data and on Twitter. It is possible that these topics are not as frequently mentioned on school and district Facebook pages because they serve a different communication function than Twitter and Scopus (schools-to-families vs. scholars-to-scholars). Rather than exchanging the latest technology ideas and tips among researchers and practitioners, school and district Facebook pages serve as a day-to-day communication tool and an information hub between schools (teachers and administrators) and families (students and parents). As in previous years, the school and school district Facebook page analysis and the Twitter external link analysis highlighted the continuous predominance of digital services by a single tech company: Google. Indeed, tools such as YouTube, Google Docs, and Google Drive have been widely adopted and have become intrinsic to any technology-related activities.

Second, not surprisingly, the analysis revealed a strong post-pandemic shift across the data on all three platforms: Scopus, Twitter, and Facebook. The Twitter data analysis suggested a sharp decline in COVID-19-related terms usage, including technology terms like “remote teaching.” Facebook data clearly indicated a shift from remote learning (a decline in remote technology use) to in-person activities (an increase in sports and events). Despite this shift, we saw increased references to online and hybrid learning across all three platforms, suggesting more ubiquitous use of these technologies and practices within existing educational systems as a supplement rather than a wholesale replacement (e.g., Seaman & Seaman, 2022a , b ). Additionally, the appearance of “COVID-19,” “online learning,” and “during COVID” bigrams in Scopus data suggested that researchers are still reporting on EdTech activities during the pandemic.

Third, among other trends, Twitter data analysis suggested the maturation and specialization of topics reflective of evolving users’ needs and desires. Many popular hashtags remained at the top in 2022. However, the number of their tweets dropped, and new, yet related hashtags noticeably appeared at the top. For example, #stem evolved to include #steam, #stemeducation, #stemed, and #womeninstem. Such development suggests users’ understanding of hashtag functionality and responsiveness to the dynamic social media landscape. As hashtags become popular and mature, they may lose their differentiating power, and users start coining related hashtags to create more specialized spaces. As a related trend, we saw the emergence of diversity, women, and English language learning hashtags on Twitter this year, possibly suggesting that these issues are becoming increasingly important to the EdTech community.

In response to the commentaries from previous editorials, this year’s analysis indicates that many technology-related changes initiated during the pandemic may influence longer-term shifts, such as the increased interest in and normalization of online and blended learning. In addition, our OER analysis suggests that there is an appetite for resources to support both theoretical and practical work in educational technology and that the quality of resources available to professionals at all levels may be indicated by a variety of emergent methods beyond historic reliance on peer review and expertise (e.g., consider the widespread use and perceived quality of student-generated OER). As educational technology professionals grapple with this new reality in a world that increasingly requires focused guidance for our professionals worldwide, we should continue to move the field in directions that are responsive to the needs of a global educational technology community, in terms of topics, resources, contexts, formats, and accessibility.

Casey, C. C., Goodsett, M., Hoover, J. K., Robertson, S., & Whitchurch, M. (2023). Open Pedagogy. EdTechnica: The Open Encyclopedia of Educational Technology .

Clinton, V. (2018). Savings without sacrifices: A case study of open-source textbook adoption. Open Learning: The Journal of Distance and Open Learning, 33 (3), 177–189.

Article   Google Scholar  

Clinton, V., & Khan, S. (2019). Efficacy of open textbook adoption on learning performance and course withdrawal rates: A meta-analysis. AERA Open, 5 (3), 1–20.

Creative Commons (2020). Open education.

Dang, S. (2022, October 26). Exclusive: Twitter is losing its most active users, internal documents show. Reuters.

Hilton, J. (2016). Open educational resources and college textbook choices: A review of research on efficacy and perceptions. Educational Technology Research and Development, 64 (4), 573–590.

Hilton, J. (2019). Open educational resources, student efficacy, and user perceptions: A synthesis of research published between 2015-2018. Educational Technology Research and development.

Ikahihifo, T. K., Spring, K. J., Rosecrans, J., & Watson, J. (2017). Assessing the savings from open educational resources on student academic goals. The International Review of Research in Open and Distance Learning, 18 (7).

Kimmons, R. (2015). OER quality and adaptation in K-12: Comparing teacher evaluations of copyright-restricted, open, and open/adapted textbooks. The International Review of Research in Open and Distributed Learning, 16 (5).

Kimmons, R. (2016). Expansive openness in teacher practice. Teachers College Record, 118 (9).

Kimmons, R. (2020). Current trends (and missing links) in educational technology research and practice. TechTrends, 64 (6), 803–809.

Kimmons, R., & Rosenberg, J. M. (2022). Trends and topics in educational technology, 2022 edition. TechTrends, 66 (2), 134–140.

Kimmons, R., & Veletsianos, G. (2016). Education scholars’ evolving uses of twitter as a conference backchannel and social commentary platform. British Journal of Educational Technology, 47 (3), 445–464.

Kimmons, R., & Veletsianos, G. (2018). Public internet data mining methods in instructional design, educational technology, and online learning research. TechTrends, 62 (5), 492–500.

Kimmons, R., Rosenberg, J., & Allman, B. (2021). Trends in educational technology: What Facebook, twitter, and Scopus can tell us about current research and practice. TechTrends, 65 , 125–136.

Rosenberg, J. M. (2023). Open and useful? An exploration of the science education resources on OER Commons. Contemporary Issues in Technology and Teacher Education.

Rosenberg, J. M., Borchers, C., Stegenga, S. M., Burchfield, M. A., Anderson, D., & Fischer, C. (2022). How educational institutions reveal students’ personally identifiable information on Facebook. Learning, Media and Technology, 1–17.

Seaman, J. E. & Seaman, J. (2022a). Coming back together: Educational resources in U.S. K-12 education, 2022 .

Seaman, J. E. & Seaman, J. (2022b). Turning point for digital curricula: Educational resources in U.S. higher education, 2022.

Sweney, M. (2022, December 13). Twitter ‘to lose 32m users in two years after Elon Musk takeover.’ The Guardian.

Veletsianos, G. (2017). Three cases of hashtags used as learning and professional development environments. TechTrends, 61 , 284–292.

Wiley, D. (n.d.). Defining the “open” in open content and open educational resources.  Retrieved from . Accessed 1 Feb 2023.

Wiley, D., & Hilton, J. L., III. (2018). Defining OER-enabled pedagogy. The International Review of Research in Open and Distributed Learning, 19 (4).

Woodward, S., Lloyd, A., & Kimmons, R. (2017). Student voice in textbook evaluation: Comparing open and restricted textbooks. The International Review of Research in Open and Distributed Learning, 18 (6).

Download references

Author information

Authors and affiliations.

Brigham Young University, Provo, UT, USA

Bohdana Allman & Royce Kimmons

University of Tennessee, Knoxville, TN, USA

Joshua Rosenberg

Brajrajnagar College, Sambalpur University, Burla, India

Monalisa Dash

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Royce Kimmons .

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Allman, B., Kimmons, R., Rosenberg, J. et al. Trends and Topics in Educational Technology, 2023 Edition. TechTrends 67 , 583–591 (2023).

Download citation

Published : 05 April 2023

Issue Date : May 2023


Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Find a journal
  • Publish with us
  • Track your research

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • My Account Login
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • Review Article
  • Open access
  • Published: 12 February 2024

Education reform and change driven by digital technology: a bibliometric study from a global perspective

  • Chengliang Wang 1 ,
  • Xiaojiao Chen 1 ,
  • Teng Yu   ORCID: 2 , 3 ,
  • Yidan Liu 1 , 4 &
  • Yuhui Jing 1  

Humanities and Social Sciences Communications volume  11 , Article number:  256 ( 2024 ) Cite this article

2948 Accesses

1 Citations

1 Altmetric

Metrics details

  • Development studies
  • Science, technology and society

Amidst the global digital transformation of educational institutions, digital technology has emerged as a significant area of interest among scholars. Such technologies have played an instrumental role in enhancing learner performance and improving the effectiveness of teaching and learning. These digital technologies also ensure the sustainability and stability of education during the epidemic. Despite this, a dearth of systematic reviews exists regarding the current state of digital technology application in education. To address this gap, this study utilized the Web of Science Core Collection as a data source (specifically selecting the high-quality SSCI and SCIE) and implemented a topic search by setting keywords, yielding 1849 initial publications. Furthermore, following the PRISMA guidelines, we refined the selection to 588 high-quality articles. Using software tools such as CiteSpace, VOSviewer, and Charticulator, we reviewed these 588 publications to identify core authors (such as Selwyn, Henderson, Edwards), highly productive countries/regions (England, Australia, USA), key institutions (Monash University, Australian Catholic University), and crucial journals in the field ( Education and Information Technologies , Computers & Education , British Journal of Educational Technology ). Evolutionary analysis reveals four developmental periods in the research field of digital technology education application: the embryonic period, the preliminary development period, the key exploration, and the acceleration period of change. The study highlights the dual influence of technological factors and historical context on the research topic. Technology is a key factor in enabling education to transform and upgrade, and the context of the times is an important driving force in promoting the adoption of new technologies in the education system and the transformation and upgrading of education. Additionally, the study identifies three frontier hotspots in the field: physical education, digital transformation, and professional development under the promotion of digital technology. This study presents a clear framework for digital technology application in education, which can serve as a valuable reference for researchers and educational practitioners concerned with digital technology education application in theory and practice.

Similar content being viewed by others

research topics on technology in education

A bibliometric analysis of knowledge mapping in Chinese education digitalization research from 2012 to 2022

Rui Shi & XiuLan Wan

research topics on technology in education

Digital transformation and digital literacy in the context of complexity within higher education institutions: a systematic literature review

Silvia Farias-Gaytan, Ignacio Aguaded & Maria-Soledad Ramirez-Montoya

research topics on technology in education

Education big data and learning analytics: a bibliometric analysis

Shaza Arissa Samsul, Noraffandy Yahaya & Hassan Abuhassna


Digital technology has become an essential component of modern education, facilitating the extension of temporal and spatial boundaries and enriching the pedagogical contexts (Selwyn and Facer, 2014 ). The advent of mobile communication technology has enabled learning through social media platforms (Szeto et al. 2015 ; Pires et al. 2022 ), while the advancement of augmented reality technology has disrupted traditional conceptions of learning environments and spaces (Perez-Sanagustin et al., 2014 ; Kyza and Georgiou, 2018 ). A wide range of digital technologies has enabled learning to become a norm in various settings, including the workplace (Sjöberg and Holmgren, 2021 ), home (Nazare et al. 2022 ), and online communities (Tang and Lam, 2014 ). Education is no longer limited to fixed locations and schedules, but has permeated all aspects of life, allowing learning to continue at any time and any place (Camilleri and Camilleri, 2016 ; Selwyn and Facer, 2014 ).

The advent of digital technology has led to the creation of several informal learning environments (Greenhow and Lewin, 2015 ) that exhibit divergent form, function, features, and patterns in comparison to conventional learning environments (Nygren et al. 2019 ). Consequently, the associated teaching and learning processes, as well as the strategies for the creation, dissemination, and acquisition of learning resources, have undergone a complete overhaul. The ensuing transformations have posed a myriad of novel issues, such as the optimal structuring of teaching methods by instructors and the adoption of appropriate learning strategies by students in the new digital technology environment. Consequently, an examination of the principles that underpin effective teaching and learning in this environment is a topic of significant interest to numerous scholars engaged in digital technology education research.

Over the course of the last two decades, digital technology has made significant strides in the field of education, notably in extending education time and space and creating novel educational contexts with sustainability. Despite research attempts to consolidate the application of digital technology in education, previous studies have only focused on specific aspects of digital technology, such as Pinto and Leite’s ( 2020 ) investigation into digital technology in higher education and Mustapha et al.’s ( 2021 ) examination of the role and value of digital technology in education during the pandemic. While these studies have provided valuable insights into the practical applications of digital technology in particular educational domains, they have not comprehensively explored the macro-mechanisms and internal logic of digital technology implementation in education. Additionally, these studies were conducted over a relatively brief period, making it challenging to gain a comprehensive understanding of the macro-dynamics and evolutionary process of digital technology in education. Some studies have provided an overview of digital education from an educational perspective but lack a precise understanding of technological advancement and change (Yang et al. 2022 ). Therefore, this study seeks to employ a systematic scientific approach to collate relevant research from 2000 to 2022, comprehend the internal logic and development trends of digital technology in education, and grasp the outstanding contribution of digital technology in promoting the sustainability of education in time and space. In summary, this study aims to address the following questions:

RQ1: Since the turn of the century, what is the productivity distribution of the field of digital technology education application research in terms of authorship, country/region, institutional and journal level?

RQ2: What is the development trend of research on the application of digital technology in education in the past two decades?

RQ3: What are the current frontiers of research on the application of digital technology in education?

Literature review

Although the term “digital technology” has become ubiquitous, a unified definition has yet to be agreed upon by scholars. Because the meaning of the word digital technology is closely related to the specific context. Within the educational research domain, Selwyn’s ( 2016 ) definition is widely favored by scholars (Pinto and Leite, 2020 ). Selwyn ( 2016 ) provides a comprehensive view of various concrete digital technologies and their applications in education through ten specific cases, such as immediate feedback in classes, orchestrating teaching, and community learning. Through these specific application scenarios, Selwyn ( 2016 ) argues that digital technology encompasses technologies associated with digital devices, including but not limited to tablets, smartphones, computers, and social media platforms (such as Facebook and YouTube). Furthermore, Further, the behavior of accessing the internet at any location through portable devices can be taken as an extension of the behavior of applying digital technology.

The evolving nature of digital technology has significant implications in the field of education. In the 1890s, the focus of digital technology in education was on comprehending the nuances of digital space, digital culture, and educational methodologies, with its connotations aligned more towards the idea of e-learning. The advent and subsequent widespread usage of mobile devices since the dawn of the new millennium have been instrumental in the rapid expansion of the concept of digital technology. Notably, mobile learning devices such as smartphones and tablets, along with social media platforms, have become integral components of digital technology (Conole and Alevizou, 2010 ; Batista et al. 2016 ). In recent times, the burgeoning application of AI technology in the education sector has played a vital role in enriching the digital technology lexicon (Banerjee et al. 2021 ). ChatGPT, for instance, is identified as a novel educational technology that has immense potential to revolutionize future education (Rospigliosi, 2023 ; Arif, Munaf and Ul-Haque, 2023 ).

Pinto and Leite ( 2020 ) conducted a comprehensive macroscopic survey of the use of digital technologies in the education sector and identified three distinct categories, namely technologies for assessment and feedback, mobile technologies, and Information Communication Technologies (ICT). This classification criterion is both macroscopic and highly condensed. In light of the established concept definitions of digital technology in the educational research literature, this study has adopted the characterizations of digital technology proposed by Selwyn ( 2016 ) and Pinto and Leite ( 2020 ) as crucial criteria for analysis and research inclusion. Specifically, this criterion encompasses several distinct types of digital technologies, including Information and Communication Technologies (ICT), Mobile tools, eXtended Reality (XR) Technologies, Assessment and Feedback systems, Learning Management Systems (LMS), Publish and Share tools, Collaborative systems, Social media, Interpersonal Communication tools, and Content Aggregation tools.

Methodology and materials

Research method: bibliometric.

The research on econometric properties has been present in various aspects of human production and life, yet systematic scientific theoretical guidance has been lacking, resulting in disorganization. In 1969, British scholar Pritchard ( 1969 ) proposed “bibliometrics,” which subsequently emerged as an independent discipline in scientific quantification research. Initially, Pritchard defined bibliometrics as “the application of mathematical and statistical methods to books and other media of communication,” however, the definition was not entirely rigorous. To remedy this, Hawkins ( 2001 ) expanded Pritchard’s definition to “the quantitative analysis of the bibliographic features of a body of literature.” De Bellis further clarified the objectives of bibliometrics, stating that it aims to analyze and identify patterns in literature, such as the most productive authors, institutions, countries, and journals in scientific disciplines, trends in literary production over time, and collaboration networks (De Bellis, 2009 ). According to Garfield ( 2006 ), bibliometric research enables the examination of the history and structure of a field, the flow of information within the field, the impact of journals, and the citation status of publications over a longer time scale. All of these definitions illustrate the unique role of bibliometrics as a research method for evaluating specific research fields.

This study uses CiteSpace, VOSviewer, and Charticulator to analyze data and create visualizations. Each of these three tools has its own strengths and can complement each other. CiteSpace and VOSviewer use set theory and probability theory to provide various visualization views in fields such as keywords, co-occurrence, and co-authors. They are easy to use and produce visually appealing graphics (Chen, 2006 ; van Eck and Waltman, 2009 ) and are currently the two most widely used bibliometric tools in the field of visualization (Pan et al. 2018 ). In this study, VOSviewer provided the data necessary for the Performance Analysis; Charticulator was then used to redraw using the tabular data exported from VOSviewer (for creating the chord diagram of country collaboration); this was to complement the mapping process, while CiteSpace was primarily utilized to generate keyword maps and conduct burst word analysis.

Data retrieval

This study selected documents from the Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) in the Web of Science Core Collection as the data source, for the following reasons:

(1) The Web of Science Core Collection, as a high-quality digital literature resource database, has been widely accepted by many researchers and is currently considered the most suitable database for bibliometric analysis (Jing et al. 2023a ). Compared to other databases, Web of Science provides more comprehensive data information (Chen et al. 2022a ), and also provides data formats suitable for analysis using VOSviewer and CiteSpace (Gaviria-Marin et al. 2019 ).

(2) The application of digital technology in the field of education is an interdisciplinary research topic, involving technical knowledge literature belonging to the natural sciences and education-related literature belonging to the social sciences. Therefore, it is necessary to select Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) as the sources of research data, ensuring the comprehensiveness of data while ensuring the reliability and persuasiveness of bibliometric research (Hwang and Tsai, 2011 ; Wang et al. 2022 ).

After establishing the source of research data, it is necessary to determine a retrieval strategy (Jing et al. 2023b ). The choice of a retrieval strategy should consider a balance between the breadth and precision of the search formula. That is to say, it should encompass all the literature pertaining to the research topic while excluding irrelevant documents as much as possible. In light of this, this study has set a retrieval strategy informed by multiple related papers (Mustapha et al. 2021 ; Luo et al. 2021 ). The research by Mustapha et al. ( 2021 ) guided us in selecting keywords (“digital” AND “technolog*”) to target digital technology, while Luo et al. ( 2021 ) informed the selection of terms (such as “instruct*,” “teach*,” and “education”) to establish links with the field of education. Then, based on the current application of digital technology in the educational domain and the scope of selection criteria, we constructed the final retrieval strategy. Following the general patterns of past research (Jing et al. 2023a , 2023b ), we conducted a specific screening using the topic search (Topics, TS) function in Web of Science. For the specific criteria used in the screening for this study, please refer to Table 1 .

Literature screening

Literature acquired through keyword searches may contain ostensibly related yet actually unrelated works. Therefore, to ensure the close relevance of literature included in the analysis to the research topic, it is often necessary to perform a manual screening process to identify the final literature to be analyzed, subsequent to completing the initial literature search.

The manual screening process consists of two steps. Initially, irrelevant literature is weeded out based on the title and abstract, with two members of the research team involved in this phase. This stage lasted about one week, resulting in 1106 articles being retained. Subsequently, a comprehensive review of the full text is conducted to accurately identify the literature required for the study. To carry out the second phase of manual screening effectively and scientifically, and to minimize the potential for researcher bias, the research team established the inclusion criteria presented in Table 2 . Three members were engaged in this phase, which took approximately 2 weeks, culminating in the retention of 588 articles after meticulous screening. The entire screening process is depicted in Fig. 1 , adhering to the PRISMA guidelines (Page et al. 2021 ).

figure 1

The process of obtaining and filtering the necessary literature data for research.

Data standardization

Nguyen and Hallinger ( 2020 ) pointed out that raw data extracted from scientific databases often contains multiple expressions of the same term, and not addressing these synonymous expressions could affect research results in bibliometric analysis. For instance, in the original data, the author list may include “Tsai, C. C.” and “Tsai, C.-C.”, while the keyword list may include “professional-development” and “professional development,” which often require merging. Therefore, before analyzing the selected literature, a data disambiguation process is necessary to standardize the data (Strotmann and Zhao, 2012 ; Van Eck and Waltman, 2019 ). This study adopted the data standardization process proposed by Taskin and Al ( 2019 ), mainly including the following standardization operations:

Firstly, the author and source fields in the data are corrected and standardized to differentiate authors with similar names.

Secondly, the study checks whether the journals to which the literature belongs have been renamed in the past over 20 years, so as to avoid the influence of periodical name change on the analysis results.

Finally, the keyword field is standardized by unifying parts of speech and singular/plural forms of keywords, which can help eliminate redundant entries in the knowledge graph.

Performance analysis (RQ1)

This section offers a thorough and detailed analysis of the state of research in the field of digital technology education. By utilizing descriptive statistics and visual maps, it provides a comprehensive overview of the development trends, authors, countries, institutions, and journal distribution within the field. The insights presented in this section are of great significance in advancing our understanding of the current state of research in this field and identifying areas for further investigation. The use of visual aids to display inter-country cooperation and the evolution of the field adds to the clarity and coherence of the analysis.

Time trend of the publications

To understand a research field, it is first necessary to understand the most basic quantitative information, among which the change in the number of publications per year best reflects the development trend of a research field. Figure 2 shows the distribution of publication dates.

figure 2

Time trend of the publications on application of digital technology in education.

From the Fig. 2 , it can be seen that the development of this field over the past over 20 years can be roughly divided into three stages. The first stage was from 2000 to 2007, during which the number of publications was relatively low. Due to various factors such as technological maturity, the academic community did not pay widespread attention to the role of digital technology in expanding the scope of teaching and learning. The second stage was from 2008 to 2019, during which the overall number of publications showed an upward trend, and the development of the field entered an accelerated period, attracting more and more scholars’ attention. The third stage was from 2020 to 2022, during which the number of publications stabilized at around 100. During this period, the impact of the pandemic led to a large number of scholars focusing on the role of digital technology in education during the pandemic, and research on the application of digital technology in education became a core topic in social science research.

Analysis of authors

An analysis of the author’s publication volume provides information about the representative scholars and core research strengths of a research area. Table 3 presents information on the core authors in adaptive learning research, including name, publication number, and average number of citations per article (based on the analysis and statistics from VOSviewer).

Variations in research foci among scholars abound. Within the field of digital technology education application research over the past two decades, Neil Selwyn stands as the most productive author, having published 15 papers garnering a total of 1027 citations, resulting in an average of 68.47 citations per paper. As a Professor at the Faculty of Education at Monash University, Selwyn concentrates on exploring the application of digital technology in higher education contexts (Selwyn et al. 2021 ), as well as related products in higher education such as Coursera, edX, and Udacity MOOC platforms (Bulfin et al. 2014 ). Selwyn’s contributions to the educational sociology perspective include extensive research on the impact of digital technology on education, highlighting the spatiotemporal extension of educational processes and practices through technological means as the greatest value of educational technology (Selwyn, 2012 ; Selwyn and Facer, 2014 ). In addition, he provides a blueprint for the development of future schools in 2030 based on the present impact of digital technology on education (Selwyn et al. 2019 ). The second most productive author in this field, Henderson, also offers significant contributions to the understanding of the important value of digital technology in education, specifically in the higher education setting, with a focus on the impact of the pandemic (Henderson et al. 2015 ; Cohen et al. 2022 ). In contrast, Edwards’ research interests focus on early childhood education, particularly the application of digital technology in this context (Edwards, 2013 ; Bird and Edwards, 2015 ). Additionally, on the technical level, Edwards also mainly prefers digital game technology, because it is a digital technology that children are relatively easy to accept (Edwards, 2015 ).

Analysis of countries/regions and organization

The present study aimed to ascertain the leading countries in digital technology education application research by analyzing 75 countries related to 558 works of literature. Table 4 depicts the top ten countries that have contributed significantly to this field in terms of publication count (based on the analysis and statistics from VOSviewer). Our analysis of Table 4 data shows that England emerged as the most influential country/region, with 92 published papers and 2401 citations. Australia and the United States secured the second and third ranks, respectively, with 90 papers (2187 citations) and 70 papers (1331 citations) published. Geographically, most of the countries featured in the top ten publication volumes are situated in Australia, North America, and Europe, with China being the only exception. Notably, all these countries, except China, belong to the group of developed nations, suggesting that economic strength is a prerequisite for fostering research in the digital technology education application field.

This study presents a visual representation of the publication output and cooperation relationships among different countries in the field of digital technology education application research. Specifically, a chord diagram is employed to display the top 30 countries in terms of publication output, as depicted in Fig. 3 . The chord diagram is composed of nodes and chords, where the nodes are positioned as scattered points along the circumference, and the length of each node corresponds to the publication output, with longer lengths indicating higher publication output. The chords, on the other hand, represent the cooperation relationships between any two countries, and are weighted based on the degree of closeness of the cooperation, with wider chords indicating closer cooperation. Through the analysis of the cooperation relationships, the findings suggest that the main publishing countries in this field are engaged in cooperative relationships with each other, indicating a relatively high level of international academic exchange and research internationalization.

figure 3

In the diagram, nodes are scattered along the circumference of a circle, with the length of each node representing the volume of publications. The weighted arcs connecting any two points on the circle are known as chords, representing the collaborative relationship between the two, with the width of the arc indicating the closeness of the collaboration.

Further analyzing Fig. 3 , we can extract more valuable information, enabling a deeper understanding of the connections between countries in the research field of digital technology in educational applications. It is evident that certain countries, such as the United States, China, and England, display thicker connections, indicating robust collaborative relationships in terms of productivity. These thicker lines signify substantial mutual contributions and shared objectives in certain sectors or fields, highlighting the interconnectedness and global integration in these areas. By delving deeper, we can also explore potential future collaboration opportunities through the chord diagram, identifying possible partners to propel research and development in this field. In essence, the chord diagram successfully encapsulates and conveys the multi-dimensionality of global productivity and cooperation, allowing for a comprehensive understanding of the intricate inter-country relationships and networks in a global context, providing valuable guidance and insights for future research and collaborations.

An in-depth examination of the publishing institutions is provided in Table 5 , showcasing the foremost 10 institutions ranked by their publication volume. Notably, Monash University and Australian Catholic University, situated in Australia, have recorded the most prolific publications within the digital technology education application realm, with 22 and 10 publications respectively. Moreover, the University of Oslo from Norway is featured among the top 10 publishing institutions, with an impressive average citation count of 64 per publication. It is worth highlighting that six institutions based in the United Kingdom were also ranked within the top 10 publishing institutions, signifying their leading position in this area of research.

Analysis of journals

Journals are the main carriers for publishing high-quality papers. Some scholars point out that the two key factors to measure the influence of journals in the specified field are the number of articles published and the number of citations. The more papers published in a magazine and the more citations, the greater its influence (Dzikowski, 2018 ). Therefore, this study utilized VOSviewer to statistically analyze the top 10 journals with the most publications in the field of digital technology in education and calculated the average citations per article (see Table 6 ).

Based on Table 6 , it is apparent that the highest number of articles in the domain of digital technology in education research were published in Education and Information Technologies (47 articles), Computers & Education (34 articles), and British Journal of Educational Technology (32 articles), indicating a higher article output compared to other journals. This underscores the fact that these three journals concentrate more on the application of digital technology in education. Furthermore, several other journals, such as Technology Pedagogy and Education and Sustainability, have published more than 15 articles in this domain. Sustainability represents the open access movement, which has notably facilitated research progress in this field, indicating that the development of open access journals in recent years has had a significant impact. Although there is still considerable disagreement among scholars on the optimal approach to achieve open access, the notion that research outcomes should be accessible to all is widely recognized (Huang et al. 2020 ). On further analysis of the research fields to which these journals belong, except for Sustainability, it is evident that they all pertain to educational technology, thus providing a qualitative definition of the research area of digital technology education from the perspective of journals.

Temporal keyword analysis: thematic evolution (RQ2)

The evolution of research themes is a dynamic process, and previous studies have attempted to present the developmental trajectory of fields by drawing keyword networks in phases (Kumar et al. 2021 ; Chen et al. 2022b ). To understand the shifts in research topics across different periods, this study follows past research and, based on the significant changes in the research field and corresponding technological advancements during the outlined periods, divides the timeline into four stages (the first stage from January 2000 to December 2005, the second stage from January 2006 to December 2011, the third stage from January 2012 to December 2017; and the fourth stage from January 2018 to December 2022). The division into these four stages was determined through a combination of bibliometric analysis and literature review, which presented a clear trajectory of the field’s development. The research analyzes the keyword networks for each time period (as there are only three articles in the first stage, it was not possible to generate an appropriate keyword co-occurrence map, hence only the keyword co-occurrence maps from the second to the fourth stages are provided), to understand the evolutionary track of the digital technology education application research field over time.

2000.1–2005.12: germination period

From January 2000 to December 2005, digital technology education application research was in its infancy. Only three studies focused on digital technology, all of which were related to computers. Due to the popularity of computers, the home became a new learning environment, highlighting the important role of digital technology in expanding the scope of learning spaces (Sutherland et al. 2000 ). In specific disciplines and contexts, digital technology was first favored in medical clinical practice, becoming an important tool for supporting the learning of clinical knowledge and practice (Tegtmeyer et al. 2001 ; Durfee et al. 2003 ).

2006.1–2011.12: initial development period

Between January 2006 and December 2011, it was the initial development period of digital technology education research. Significant growth was observed in research related to digital technology, and discussions and theoretical analyses about “digital natives” emerged. During this phase, scholars focused on the debate about “how to use digital technology reasonably” and “whether current educational models and school curriculum design need to be adjusted on a large scale” (Bennett and Maton, 2010 ; Selwyn, 2009 ; Margaryan et al. 2011 ). These theoretical and speculative arguments provided a unique perspective on the impact of cognitive digital technology on education and teaching. As can be seen from the vocabulary such as “rethinking”, “disruptive pedagogy”, and “attitude” in Fig. 4 , many scholars joined the calm reflection and analysis under the trend of digital technology (Laurillard, 2008 ; Vratulis et al. 2011 ). During this phase, technology was still undergoing dramatic changes. The development of mobile technology had already caught the attention of many scholars (Wong et al. 2011 ), but digital technology represented by computers was still very active (Selwyn et al. 2011 ). The change in technological form would inevitably lead to educational transformation. Collins and Halverson ( 2010 ) summarized the prospects and challenges of using digital technology for learning and educational practices, believing that digital technology would bring a disruptive revolution to the education field and bring about a new educational system. In addition, the term “teacher education” in Fig. 4 reflects the impact of digital technology development on teachers. The rapid development of technology has widened the generation gap between teachers and students. To ensure smooth communication between teachers and students, teachers must keep up with the trend of technological development and establish a lifelong learning concept (Donnison, 2009 ).

figure 4

In the diagram, each node represents a keyword, with the size of the node indicating the frequency of occurrence of the keyword. The connections represent the co-occurrence relationships between keywords, with a higher frequency of co-occurrence resulting in tighter connections.

2012.1–2017.12: critical exploration period

During the period spanning January 2012 to December 2017, the application of digital technology in education research underwent a significant exploration phase. As can be seen from Fig. 5 , different from the previous stage, the specific elements of specific digital technology have started to increase significantly, including the enrichment of technological contexts, the greater variety of research methods, and the diversification of learning modes. Moreover, the temporal and spatial dimensions of the learning environment were further de-emphasized, as noted in previous literature (Za et al. 2014 ). Given the rapidly accelerating pace of technological development, the education system in the digital era is in urgent need of collaborative evolution and reconstruction, as argued by Davis, Eickelmann, and Zaka ( 2013 ).

figure 5

In the domain of digital technology, social media has garnered substantial scholarly attention as a promising avenue for learning, as noted by Pasquini and Evangelopoulos ( 2016 ). The implementation of social media in education presents several benefits, including the liberation of education from the restrictions of physical distance and time, as well as the erasure of conventional educational boundaries. The user-generated content (UGC) model in social media has emerged as a crucial source for knowledge creation and distribution, with the widespread adoption of mobile devices. Moreover, social networks have become an integral component of ubiquitous learning environments (Hwang et al. 2013 ). The utilization of social media allows individuals to function as both knowledge producers and recipients, which leads to a blurring of the conventional roles of learners and teachers. On mobile platforms, the roles of learners and teachers are not fixed, but instead interchangeable.

In terms of research methodology, the prevalence of empirical studies with survey designs in the field of educational technology during this period is evident from the vocabulary used, such as “achievement,” “acceptance,” “attitude,” and “ict.” in Fig. 5 . These studies aim to understand learners’ willingness to adopt and attitudes towards new technologies, and some seek to investigate the impact of digital technologies on learning outcomes through quasi-experimental designs (Domínguez et al. 2013 ). Among these empirical studies, mobile learning emerged as a hot topic, and this is not surprising. First, the advantages of mobile learning environments over traditional ones have been empirically demonstrated (Hwang et al. 2013 ). Second, learners born around the turn of the century have been heavily influenced by digital technologies and have developed their own learning styles that are more open to mobile devices as a means of learning. Consequently, analyzing mobile learning as a relatively novel mode of learning has become an important issue for scholars in the field of educational technology.

The intervention of technology has led to the emergence of several novel learning modes, with the blended learning model being the most representative one in the current phase. Blended learning, a novel concept introduced in the information age, emphasizes the integration of the benefits of traditional learning methods and online learning. This learning mode not only highlights the prominent role of teachers in guiding, inspiring, and monitoring the learning process but also underlines the importance of learners’ initiative, enthusiasm, and creativity in the learning process. Despite being an early conceptualization, blended learning’s meaning has been expanded by the widespread use of mobile technology and social media in education. The implementation of new technologies, particularly mobile devices, has resulted in the transformation of curriculum design and increased flexibility and autonomy in students’ learning processes (Trujillo Maza et al. 2016 ), rekindling scholarly attention to this learning mode. However, some scholars have raised concerns about the potential drawbacks of the blended learning model, such as its significant impact on the traditional teaching system, the lack of systematic coping strategies and relevant policies in several schools and regions (Moskal et al. 2013 ).

2018.1–2022.12: accelerated transformation period

The period spanning from January 2018 to December 2022 witnessed a rapid transformation in the application of digital technology in education research. The field of digital technology education research reached a peak period of publication, largely influenced by factors such as the COVID-19 pandemic (Yu et al. 2023 ). Research during this period was built upon the achievements, attitudes, and social media of the previous phase, and included more elements that reflect the characteristics of this research field, such as digital literacy, digital competence, and professional development, as depicted in Fig. 6 . Alongside this, scholars’ expectations for the value of digital technology have expanded, and the pursuit of improving learning efficiency and performance is no longer the sole focus. Some research now aims to cultivate learners’ motivation and enhance their self-efficacy by applying digital technology in a reasonable manner, as demonstrated by recent studies (Beardsley et al. 2021 ; Creely et al. 2021 ).

figure 6

The COVID-19 pandemic has emerged as a crucial backdrop for the digital technology’s role in sustaining global education, as highlighted by recent scholarly research (Zhou et al. 2022 ; Pan and Zhang, 2020 ; Mo et al. 2022 ). The online learning environment, which is supported by digital technology, has become the primary battleground for global education (Yu, 2022 ). This social context has led to various studies being conducted, with some scholars positing that the pandemic has impacted the traditional teaching order while also expanding learning possibilities in terms of patterns and forms (Alabdulaziz, 2021 ). Furthermore, the pandemic has acted as a catalyst for teacher teaching and technological innovation, and this viewpoint has been empirically substantiated (Moorhouse and Wong, 2021 ). Additionally, some scholars believe that the pandemic’s push is a crucial driving force for the digital transformation of the education system, serving as an essential mechanism for overcoming the system’s inertia (Romero et al. 2021 ).

The rapid outbreak of the pandemic posed a challenge to the large-scale implementation of digital technologies, which was influenced by a complex interplay of subjective and objective factors. Objective constraints included the lack of infrastructure in some regions to support digital technologies, while subjective obstacles included psychological resistance among certain students and teachers (Moorhouse, 2021 ). These factors greatly impacted the progress of online learning during the pandemic. Additionally, Timotheou et al. ( 2023 ) conducted a comprehensive systematic review of existing research on digital technology use during the pandemic, highlighting the critical role played by various factors such as learners’ and teachers’ digital skills, teachers’ personal attributes and professional development, school leadership and management, and administration in facilitating the digitalization and transformation of schools.

The current stage of research is characterized by the pivotal term “digital literacy,” denoting a growing interest in learners’ attitudes and adoption of emerging technologies. Initially, the term “literacy” was restricted to fundamental abilities and knowledge associated with books and print materials (McMillan, 1996 ). However, with the swift advancement of computers and digital technology, there have been various attempts to broaden the scope of literacy beyond its traditional meaning, including game literacy (Buckingham and Burn, 2007 ), information literacy (Eisenberg, 2008 ), and media literacy (Turin and Friesem, 2020 ). Similarly, digital literacy has emerged as a crucial concept, and Gilster and Glister ( 1997 ) were the first to introduce this concept, referring to the proficiency in utilizing technology and processing digital information in academic, professional, and daily life settings. In practical educational settings, learners who possess higher digital literacy often exhibit an aptitude for quickly mastering digital devices and applying them intelligently to education and teaching (Yu, 2022 ).

The utilization of digital technology in education has undergone significant changes over the past two decades, and has been a crucial driver of educational reform with each new technological revolution. The impact of these changes on the underlying logic of digital technology education applications has been noticeable. From computer technology to more recent developments such as virtual reality (VR), augmented reality (AR), and artificial intelligence (AI), the acceleration in digital technology development has been ongoing. Educational reforms spurred by digital technology development continue to be dynamic, as each new digital innovation presents new possibilities and models for teaching practice. This is especially relevant in the post-pandemic era, where the importance of technological progress in supporting teaching cannot be overstated (Mughal et al. 2022 ). Existing digital technologies have already greatly expanded the dimensions of education in both time and space, while future digital technologies aim to expand learners’ perceptions. Researchers have highlighted the potential of integrated technology and immersive technology in the development of the educational metaverse, which is highly anticipated to create a new dimension for the teaching and learning environment, foster a new value system for the discipline of educational technology, and more effectively and efficiently achieve the grand educational blueprint of the United Nations’ Sustainable Development Goals (Zhang et al. 2022 ; Li and Yu, 2023 ).

Hotspot evolution analysis (RQ3)

The examination of keyword evolution reveals a consistent trend in the advancement of digital technology education application research. The emergence and transformation of keywords serve as indicators of the varying research interests in this field. Thus, the utilization of the burst detection function available in CiteSpace allowed for the identification of the top 10 burst words that exhibited a high level of burst strength. This outcome is illustrated in Table 7 .

According to the results presented in Table 7 , the explosive terminology within the realm of digital technology education research has exhibited a concentration mainly between the years 2018 and 2022. Prior to this time frame, the emerging keywords were limited to “information technology” and “computer”. Notably, among them, computer, as an emergent keyword, has always had a high explosive intensity from 2008 to 2018, which reflects the important position of computer in digital technology and is the main carrier of many digital technologies such as Learning Management Systems (LMS) and Assessment and Feedback systems (Barlovits et al. 2022 ).

Since 2018, an increasing number of research studies have focused on evaluating the capabilities of learners to accept, apply, and comprehend digital technologies. As indicated by the use of terms such as “digital literacy” and “digital skill,” the assessment of learners’ digital literacy has become a critical task. Scholarly efforts have been directed towards the development of literacy assessment tools and the implementation of empirical assessments. Furthermore, enhancing the digital literacy of both learners and educators has garnered significant attention. (Nagle, 2018 ; Yu, 2022 ). Simultaneously, given the widespread use of various digital technologies in different formal and informal learning settings, promoting learners’ digital skills has become a crucial objective for contemporary schools (Nygren et al. 2019 ; Forde and OBrien, 2022 ).

Since 2020, the field of applied research on digital technology education has witnessed the emergence of three new hotspots, all of which have been affected to some extent by the pandemic. Firstly, digital technology has been widely applied in physical education, which is one of the subjects that has been severely affected by the pandemic (Parris et al. 2022 ; Jiang and Ning, 2022 ). Secondly, digital transformation has become an important measure for most schools, especially higher education institutions, to cope with the impact of the pandemic globally (García-Morales et al. 2021 ). Although the concept of digital transformation was proposed earlier, the COVID-19 pandemic has greatly accelerated this transformation process. Educational institutions must carefully redesign their educational products to face this new situation, providing timely digital learning methods, environments, tools, and support systems that have far-reaching impacts on modern society (Krishnamurthy, 2020 ; Salas-Pilco et al. 2022 ). Moreover, the professional development of teachers has become a key mission of educational institutions in the post-pandemic era. Teachers need to have a certain level of digital literacy and be familiar with the tools and online teaching resources used in online teaching, which has become a research hotspot today. Organizing digital skills training for teachers to cope with the application of emerging technologies in education is an important issue for teacher professional development and lifelong learning (Garzón-Artacho et al. 2021 ). As the main organizers and practitioners of emergency remote teaching (ERT) during the pandemic, teachers must put cognitive effort into their professional development to ensure effective implementation of ERT (Romero-Hall and Jaramillo Cherrez, 2022 ).

The burst word “digital transformation” reveals that we are in the midst of an ongoing digital technology revolution. With the emergence of innovative digital technologies such as ChatGPT and Microsoft 365 Copilot, technology trends will continue to evolve, albeit unpredictably. While the impact of these advancements on school education remains uncertain, it is anticipated that the widespread integration of technology will significantly affect the current education system. Rejecting emerging technologies without careful consideration is unwise. Like any revolution, the technological revolution in the education field has both positive and negative aspects. Detractors argue that digital technology disrupts learning and memory (Baron, 2021 ) or causes learners to become addicted and distracted from learning (Selwyn and Aagaard, 2020 ). On the other hand, the prudent use of digital technology in education offers a glimpse of a golden age of open learning. Educational leaders and practitioners have the opportunity to leverage cutting-edge digital technologies to address current educational challenges and develop a rational path for the sustainable and healthy growth of education.

Discussion on performance analysis (RQ1)

The field of digital technology education application research has experienced substantial growth since the turn of the century, a phenomenon that is quantifiably apparent through an analysis of authorship, country/region contributions, and institutional engagement. This expansion reflects the increased integration of digital technologies in educational settings and the heightened scholarly interest in understanding and optimizing their use.

Discussion on authorship productivity in digital technology education research

The authorship distribution within digital technology education research is indicative of the field’s intellectual structure and depth. A primary figure in this domain is Neil Selwyn, whose substantial citation rate underscores the profound impact of his work. His focus on the implications of digital technology in higher education and educational sociology has proven to be seminal. Selwyn’s research trajectory, especially the exploration of spatiotemporal extensions of education through technology, provides valuable insights into the multifaceted role of digital tools in learning processes (Selwyn et al. 2019 ).

Other notable contributors, like Henderson and Edwards, present diversified research interests, such as the impact of digital technologies during the pandemic and their application in early childhood education, respectively. Their varied focuses highlight the breadth of digital technology education research, encompassing pedagogical innovation, technological adaptation, and policy development.

Discussion on country/region-level productivity and collaboration

At the country/region level, the United Kingdom, specifically England, emerges as a leading contributor with 92 published papers and a significant citation count. This is closely followed by Australia and the United States, indicating a strong English-speaking research axis. Such geographical concentration of scholarly output often correlates with investment in research and development, technological infrastructure, and the prevalence of higher education institutions engaging in cutting-edge research.

China’s notable inclusion as the only non-Western country among the top contributors to the field suggests a growing research capacity and interest in digital technology in education. However, the lower average citation per paper for China could reflect emerging engagement or different research focuses that may not yet have achieved the same international recognition as Western counterparts.

The chord diagram analysis furthers this understanding, revealing dense interconnections between countries like the United States, China, and England, which indicates robust collaborations. Such collaborations are fundamental in addressing global educational challenges and shaping international research agendas.

Discussion on institutional-level contributions to digital technology education

Institutional productivity in digital technology education research reveals a constellation of universities driving the field forward. Monash University and the Australian Catholic University have the highest publication output, signaling Australia’s significant role in advancing digital education research. The University of Oslo’s remarkable average citation count per publication indicates influential research contributions, potentially reflecting high-quality studies that resonate with the broader academic community.

The strong showing of UK institutions, including the University of London, The Open University, and the University of Cambridge, reinforces the UK’s prominence in this research field. Such institutions are often at the forefront of pedagogical innovation, benefiting from established research cultures and funding mechanisms that support sustained inquiry into digital education.

Discussion on journal publication analysis

An examination of journal outputs offers a lens into the communicative channels of the field’s knowledge base. Journals such as Education and Information Technologies , Computers & Education , and the British Journal of Educational Technology not only serve as the primary disseminators of research findings but also as indicators of research quality and relevance. The impact factor (IF) serves as a proxy for the quality and influence of these journals within the academic community.

The high citation counts for articles published in Computers & Education suggest that research disseminated through this medium has a wide-reaching impact and is of particular interest to the field. This is further evidenced by its significant IF of 11.182, indicating that the journal is a pivotal platform for seminal work in the application of digital technology in education.

The authorship, regional, and institutional productivity in the field of digital technology education application research collectively narrate the evolution of this domain since the turn of the century. The prominence of certain authors and countries underscores the importance of socioeconomic factors and existing academic infrastructure in fostering research productivity. Meanwhile, the centrality of specific journals as outlets for high-impact research emphasizes the role of academic publishing in shaping the research landscape.

As the field continues to grow, future research may benefit from leveraging the collaborative networks that have been elucidated through this analysis, perhaps focusing on underrepresented regions to broaden the scope and diversity of research. Furthermore, the stabilization of publication numbers in recent years invites a deeper exploration into potential plateaus in research trends or saturation in certain sub-fields, signaling an opportunity for novel inquiries and methodological innovations.

Discussion on the evolutionary trends (RQ2)

The evolution of the research field concerning the application of digital technology in education over the past two decades is a story of convergence, diversification, and transformation, shaped by rapid technological advancements and shifting educational paradigms.

At the turn of the century, the inception of digital technology in education was largely exploratory, with a focus on how emerging computer technologies could be harnessed to enhance traditional learning environments. Research from this early period was primarily descriptive, reflecting on the potential and challenges of incorporating digital tools into the educational setting. This phase was critical in establishing the fundamental discourse that would guide subsequent research, as it set the stage for understanding the scope and impact of digital technology in learning spaces (Wang et al. 2023 ).

As the first decade progressed, the narrative expanded to encompass the pedagogical implications of digital technologies. This was a period of conceptual debates, where terms like “digital natives” and “disruptive pedagogy” entered the academic lexicon, underscoring the growing acknowledgment of digital technology as a transformative force within education (Bennett and Maton, 2010 ). During this time, the research began to reflect a more nuanced understanding of the integration of technology, considering not only its potential to change where and how learning occurred but also its implications for educational equity and access.

In the second decade, with the maturation of internet connectivity and mobile technology, the focus of research shifted from theoretical speculations to empirical investigations. The proliferation of digital devices and the ubiquity of social media influenced how learners interacted with information and each other, prompting a surge in studies that sought to measure the impact of these tools on learning outcomes. The digital divide and issues related to digital literacy became central concerns, as scholars explored the varying capacities of students and educators to engage with technology effectively.

Throughout this period, there was an increasing emphasis on the individualization of learning experiences, facilitated by adaptive technologies that could cater to the unique needs and pacing of learners (Jing et al. 2023a ). This individualization was coupled with a growing recognition of the importance of collaborative learning, both online and offline, and the role of digital tools in supporting these processes. Blended learning models, which combined face-to-face instruction with online resources, emerged as a significant trend, advocating for a balance between traditional pedagogies and innovative digital strategies.

The later years, particularly marked by the COVID-19 pandemic, accelerated the necessity for digital technology in education, transforming it from a supplementary tool to an essential platform for delivering education globally (Mo et al. 2022 ; Mustapha et al. 2021 ). This era brought about an unprecedented focus on online learning environments, distance education, and virtual classrooms. Research became more granular, examining not just the pedagogical effectiveness of digital tools, but also their role in maintaining continuity of education during crises, their impact on teacher and student well-being, and their implications for the future of educational policy and infrastructure.

Across these two decades, the research field has seen a shift from examining digital technology as an external addition to the educational process, to viewing it as an integral component of curriculum design, instructional strategies, and even assessment methods. The emergent themes have broadened from a narrow focus on specific tools or platforms to include wider considerations such as data privacy, ethical use of technology, and the environmental impact of digital tools.

Moreover, the field has moved from considering the application of digital technology in education as a primarily cognitive endeavor to recognizing its role in facilitating socio-emotional learning, digital citizenship, and global competencies. Researchers have increasingly turned their attention to the ways in which technology can support collaborative skills, cultural understanding, and ethical reasoning within diverse student populations.

In summary, the past over twenty years in the research field of digital technology applications in education have been characterized by a progression from foundational inquiries to complex analyses of digital integration. This evolution has mirrored the trajectory of technology itself, from a facilitative tool to a pervasive ecosystem defining contemporary educational experiences. As we look to the future, the field is poised to delve into the implications of emerging technologies like AI, AR, and VR, and their potential to redefine the educational landscape even further. This ongoing metamorphosis suggests that the application of digital technology in education will continue to be a rich area of inquiry, demanding continual adaptation and forward-thinking from educators and researchers alike.

Discussion on the study of research hotspots (RQ3)

The analysis of keyword evolution in digital technology education application research elucidates the current frontiers in the field, reflecting a trajectory that is in tandem with the rapidly advancing digital age. This landscape is sculpted by emergent technological innovations and shaped by the demands of an increasingly digital society.

Interdisciplinary integration and pedagogical transformation

One of the frontiers identified from recent keyword bursts includes the integration of digital technology into diverse educational contexts, particularly noted with the keyword “physical education.” The digitalization of disciplines traditionally characterized by physical presence illustrates the pervasive reach of technology and signifies a push towards interdisciplinary integration where technology is not only a facilitator but also a transformative agent. This integration challenges educators to reconceptualize curriculum delivery to accommodate digital tools that can enhance or simulate the physical aspects of learning.

Digital literacy and skills acquisition

Another pivotal frontier is the focus on “digital literacy” and “digital skill”, which has intensified in recent years. This suggests a shift from mere access to technology towards a comprehensive understanding and utilization of digital tools. In this realm, the emphasis is not only on the ability to use technology but also on critical thinking, problem-solving, and the ethical use of digital resources (Yu, 2022 ). The acquisition of digital literacy is no longer an additive skill but a fundamental aspect of modern education, essential for navigating and contributing to the digital world.

Educational digital transformation

The keyword “digital transformation” marks a significant research frontier, emphasizing the systemic changes that education institutions must undergo to align with the digital era (Romero et al. 2021 ). This transformation includes the redesigning of learning environments, pedagogical strategies, and assessment methods to harness digital technology’s full potential. Research in this area explores the complexity of institutional change, addressing the infrastructural, cultural, and policy adjustments needed for a seamless digital transition.

Engagement and participation

Further exploration into “engagement” and “participation” underscores the importance of student-centered learning environments that are mediated by technology. The current frontiers examine how digital platforms can foster collaboration, inclusivity, and active learning, potentially leading to more meaningful and personalized educational experiences. Here, the use of technology seeks to support the emotional and cognitive aspects of learning, moving beyond the transactional view of education to one that is relational and interactive.

Professional development and teacher readiness

As the field evolves, “professional development” emerges as a crucial area, particularly in light of the pandemic which necessitated emergency remote teaching. The need for teacher readiness in a digital age is a pressing frontier, with research focusing on the competencies required for educators to effectively integrate technology into their teaching practices. This includes familiarity with digital tools, pedagogical innovation, and an ongoing commitment to personal and professional growth in the digital domain.

Pandemic as a catalyst

The recent pandemic has acted as a catalyst for accelerated research and application in this field, particularly in the domains of “digital transformation,” “professional development,” and “physical education.” This period has been a litmus test for the resilience and adaptability of educational systems to continue their operations in an emergency. Research has thus been directed at understanding how digital technologies can support not only continuity but also enhance the quality and reach of education in such contexts.

Ethical and societal considerations

The frontier of digital technology in education is also expanding to consider broader ethical and societal implications. This includes issues of digital equity, data privacy, and the sociocultural impact of technology on learning communities. The research explores how educational technology can be leveraged to address inequities and create more equitable learning opportunities for all students, regardless of their socioeconomic background.

Innovation and emerging technologies

Looking forward, the frontiers are set to be influenced by ongoing and future technological innovations, such as artificial intelligence (AI) (Wu and Yu, 2023 ; Chen et al. 2022a ). The exploration into how these technologies can be integrated into educational practices to create immersive and adaptive learning experiences represents a bold new chapter for the field.

In conclusion, the current frontiers of research on the application of digital technology in education are multifaceted and dynamic. They reflect an overarching movement towards deeper integration of technology in educational systems and pedagogical practices, where the goals are not only to facilitate learning but to redefine it. As these frontiers continue to expand and evolve, they will shape the educational landscape, requiring a concerted effort from researchers, educators, policymakers, and technologists to navigate the challenges and harness the opportunities presented by the digital revolution in education.

Conclusions and future research


The utilization of digital technology in education is a research area that cuts across multiple technical and educational domains and continues to experience dynamic growth due to the continuous progress of technology. In this study, a systematic review of this field was conducted through bibliometric techniques to examine its development trajectory. The primary focus of the review was to investigate the leading contributors, productive national institutions, significant publications, and evolving development patterns. The study’s quantitative analysis resulted in several key conclusions that shed light on this research field’s current state and future prospects.

(1) The research field of digital technology education applications has entered a stage of rapid development, particularly in recent years due to the impact of the pandemic, resulting in a peak of publications. Within this field, several key authors (Selwyn, Henderson, Edwards, etc.) and countries/regions (England, Australia, USA, etc.) have emerged, who have made significant contributions. International exchanges in this field have become frequent, with a high degree of internationalization in academic research. Higher education institutions in the UK and Australia are the core productive forces in this field at the institutional level.

(2) Education and Information Technologies , Computers & Education , and the British Journal of Educational Technology are notable journals that publish research related to digital technology education applications. These journals are affiliated with the research field of educational technology and provide effective communication platforms for sharing digital technology education applications.

(3) Over the past two decades, research on digital technology education applications has progressed from its early stages of budding, initial development, and critical exploration to accelerated transformation, and it is currently approaching maturity. Technological progress and changes in the times have been key driving forces for educational transformation and innovation, and both have played important roles in promoting the continuous development of education.

(4) Influenced by the pandemic, three emerging frontiers have emerged in current research on digital technology education applications, which are physical education, digital transformation, and professional development under the promotion of digital technology. These frontier research hotspots reflect the core issues that the education system faces when encountering new technologies. The evolution of research hotspots shows that technology breakthroughs in education’s original boundaries of time and space create new challenges. The continuous self-renewal of education is achieved by solving one hotspot problem after another.

The present study offers significant practical implications for scholars and practitioners in the field of digital technology education applications. Firstly, it presents a well-defined framework of the existing research in this area, serving as a comprehensive guide for new entrants to the field and shedding light on the developmental trajectory of this research domain. Secondly, the study identifies several contemporary research hotspots, thus offering a valuable decision-making resource for scholars aiming to explore potential research directions. Thirdly, the study undertakes an exhaustive analysis of published literature to identify core journals in the field of digital technology education applications, with Sustainability being identified as a promising open access journal that publishes extensively on this topic. This finding can potentially facilitate scholars in selecting appropriate journals for their research outputs.

Limitation and future research

Influenced by some objective factors, this study also has some limitations. First of all, the bibliometrics analysis software has high standards for data. In order to ensure the quality and integrity of the collected data, the research only selects the periodical papers in SCIE and SSCI indexes, which are the core collection of Web of Science database, and excludes other databases, conference papers, editorials and other publications, which may ignore some scientific research and original opinions in the field of digital technology education and application research. In addition, although this study used professional software to carry out bibliometric analysis and obtained more objective quantitative data, the analysis and interpretation of data will inevitably have a certain subjective color, and the influence of subjectivity on data analysis cannot be completely avoided. As such, future research endeavors will broaden the scope of literature screening and proactively engage scholars in the field to gain objective and state-of-the-art insights, while minimizing the adverse impact of personal subjectivity on research analysis.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository:

Alabdulaziz MS (2021) COVID-19 and the use of digital technology in mathematics education. Educ Inf Technol 26(6):7609–7633.

Arif TB, Munaf U, Ul-Haque I (2023) The future of medical education and research: is ChatGPT a blessing or blight in disguise? Med Educ Online 28.

Banerjee M, Chiew D, Patel KT, Johns I, Chappell D, Linton N, Cole GD, Francis DP, Szram J, Ross J, Zaman S (2021) The impact of artificial intelligence on clinical education: perceptions of postgraduate trainee doctors in London (UK) and recommendations for trainers. BMC Med Educ 21.

Barlovits S, Caldeira A, Fesakis G, Jablonski S, Koutsomanoli Filippaki D, Lázaro C, Ludwig M, Mammana MF, Moura A, Oehler DXK, Recio T, Taranto E, Volika S(2022) Adaptive, synchronous, and mobile online education: developing the ASYMPTOTE learning environment. Mathematics 10:1628.

Article   Google Scholar  

Baron NS(2021) Know what? How digital technologies undermine learning and remembering J Pragmat 175:27–37.

Batista J, Morais NS, Ramos F (2016) Researching the use of communication technologies in higher education institutions in Portugal.

Beardsley M, Albó L, Aragón P, Hernández-Leo D (2021) Emergency education effects on teacher abilities and motivation to use digital technologies. Br J Educ Technol 52.

Bennett S, Maton K(2010) Beyond the “digital natives” debate: towards a more nuanced understanding of students’ technology experiences J Comput Assist Learn 26:321–331.

Buckingham D, Burn A (2007) Game literacy in theory and practice 16:323–349

Google Scholar  

Bulfin S, Pangrazio L, Selwyn N (2014) Making “MOOCs”: the construction of a new digital higher education within news media discourse. In: The International Review of Research in Open and Distributed Learning 15.

Camilleri MA, Camilleri AC(2016) Digital learning resources and ubiquitous technologies in education Technol Knowl Learn 22:65–82.

Chen C(2006) CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature J Am Soc Inf Sci Technol 57:359–377.

Chen J, Dai J, Zhu K, Xu L(2022) Effects of extended reality on language learning: a meta-analysis Front Psychol 13:1016519.

Article   PubMed   PubMed Central   Google Scholar  

Chen J, Wang CL, Tang Y (2022b) Knowledge mapping of volunteer motivation: a bibliometric analysis and cross-cultural comparative study. Front Psychol 13.

Cohen A, Soffer T, Henderson M(2022) Students’ use of technology and their perceptions of its usefulness in higher education: International comparison J Comput Assist Learn 38(5):1321–1331.

Collins A, Halverson R(2010) The second educational revolution: rethinking education in the age of technology J Comput Assist Learn 26:18–27.

Conole G, Alevizou P (2010) A literature review of the use of Web 2.0 tools in higher education. Walton Hall, Milton Keynes, UK: the Open University, retrieved 17 February

Creely E, Henriksen D, Crawford R, Henderson M(2021) Exploring creative risk-taking and productive failure in classroom practice. A case study of the perceived self-efficacy and agency of teachers at one school Think Ski Creat 42:100951.

Davis N, Eickelmann B, Zaka P(2013) Restructuring of educational systems in the digital age from a co-evolutionary perspective J Comput Assist Learn 29:438–450.

De Belli N (2009) Bibliometrics and citation analysis: from the science citation index to cybermetrics, Scarecrow Press.

Domínguez A, Saenz-de-Navarrete J, de-Marcos L, Fernández-Sanz L, Pagés C, Martínez-Herráiz JJ(2013) Gamifying learning experiences: practical implications and outcomes Comput Educ 63:380–392.

Donnison S (2009) Discourses in conflict: the relationship between Gen Y pre-service teachers, digital technologies and lifelong learning. Australasian J Educ Technol 25.

Durfee SM, Jain S, Shaffer K (2003) Incorporating electronic media into medical student education. Acad Radiol 10:205–210.

Dzikowski P(2018) A bibliometric analysis of born global firms J Bus Res 85:281–294.

van Eck NJ, Waltman L(2009) Software survey: VOSviewer, a computer program for bibliometric mapping Scientometrics 84:523–538

Edwards S(2013) Digital play in the early years: a contextual response to the problem of integrating technologies and play-based pedagogies in the early childhood curriculum Eur Early Child Educ Res J 21:199–212.

Edwards S(2015) New concepts of play and the problem of technology, digital media and popular-culture integration with play-based learning in early childhood education Technol Pedagogy Educ 25:513–532

Article   MathSciNet   Google Scholar  

Eisenberg MB(2008) Information literacy: essential skills for the information age DESIDOC J Libr Inf Technol 28:39–47.

Forde C, OBrien A (2022) A literature review of barriers and opportunities presented by digitally enhanced practical skill teaching and learning in health science education. Med Educ Online 27.

García-Morales VJ, Garrido-Moreno A, Martín-Rojas R (2021) The transformation of higher education after the COVID disruption: emerging challenges in an online learning scenario. Front Psychol 12.

Garfield E(2006) The history and meaning of the journal impact factor JAMA 295:90.

Article   PubMed   Google Scholar  

Garzón-Artacho E, Sola-Martínez T, Romero-Rodríguez JM, Gómez-García G(2021) Teachers’ perceptions of digital competence at the lifelong learning stage Heliyon 7:e07513.

Gaviria-Marin M, Merigó JM, Baier-Fuentes H(2019) Knowledge management: a global examination based on bibliometric analysis Technol Forecast Soc Change 140:194–220.

Gilster P, Glister P (1997) Digital literacy. Wiley Computer Pub, New York

Greenhow C, Lewin C(2015) Social media and education: reconceptualizing the boundaries of formal and informal learning Learn Media Technol 41:6–30.

Hawkins DT(2001) Bibliometrics of electronic journals in information science Infor Res 7(1):7–1.

Henderson M, Selwyn N, Finger G, Aston R(2015) Students’ everyday engagement with digital technology in university: exploring patterns of use and “usefulness J High Educ Policy Manag 37:308–319

Huang CK, Neylon C, Hosking R, Montgomery L, Wilson KS, Ozaygen A, Brookes-Kenworthy C (2020) Evaluating the impact of open access policies on research institutions. eLife 9.

Hwang GJ, Tsai CC(2011) Research trends in mobile and ubiquitous learning: a review of publications in selected journals from 2001 to 2010 Br J Educ Technol 42:E65–E70.

Hwang GJ, Wu PH, Zhuang YY, Huang YM(2013) Effects of the inquiry-based mobile learning model on the cognitive load and learning achievement of students Interact Learn Environ 21:338–354.

Jiang S, Ning CF (2022) Interactive communication in the process of physical education: are social media contributing to the improvement of physical training performance. Universal Access Inf Soc, 1–10.

Jing Y, Zhao L, Zhu KK, Wang H, Wang CL, Xia Q(2023) Research landscape of adaptive learning in education: a bibliometric study on research publications from 2000 to 2022 Sustainability 15:3115–3115.

Jing Y, Wang CL, Chen Y, Wang H, Yu T, Shadiev R (2023b) Bibliometric mapping techniques in educational technology research: a systematic literature review. Educ Inf Technol 1–29.

Krishnamurthy S (2020) The future of business education: a commentary in the shadow of the Covid-19 pandemic. J Bus Res.

Kumar S, Lim WM, Pandey N, Christopher Westland J (2021) 20 years of electronic commerce research. Electron Commer Res 21:1–40

Kyza EA, Georgiou Y(2018) Scaffolding augmented reality inquiry learning: the design and investigation of the TraceReaders location-based, augmented reality platform Interact Learn Environ 27:211–225.

Laurillard D(2008) Technology enhanced learning as a tool for pedagogical innovation J Philos Educ 42:521–533.

Li M, Yu Z (2023) A systematic review on the metaverse-based blended English learning. Front Psychol 13.

Luo H, Li G, Feng Q, Yang Y, Zuo M (2021) Virtual reality in K-12 and higher education: a systematic review of the literature from 2000 to 2019. J Comput Assist Learn.

Margaryan A, Littlejohn A, Vojt G(2011) Are digital natives a myth or reality? University students’ use of digital technologies Comput Educ 56:429–440.

McMillan S(1996) Literacy and computer literacy: definitions and comparisons Comput Educ 27:161–170.

Mo CY, Wang CL, Dai J, Jin P (2022) Video playback speed influence on learning effect from the perspective of personalized adaptive learning: a study based on cognitive load theory. Front Psychology 13.

Moorhouse BL (2021) Beginning teaching during COVID-19: newly qualified Hong Kong teachers’ preparedness for online teaching. Educ Stud 1–17.

Moorhouse BL, Wong KM (2021) The COVID-19 Pandemic as a catalyst for teacher pedagogical and technological innovation and development: teachers’ perspectives. Asia Pac J Educ 1–16.

Moskal P, Dziuban C, Hartman J (2013) Blended learning: a dangerous idea? Internet High Educ 18:15–23

Mughal MY, Andleeb N, Khurram AFA, Ali MY, Aslam MS, Saleem MN (2022) Perceptions of teaching-learning force about Metaverse for education: a qualitative study. J. Positive School Psychol 6:1738–1745

Mustapha I, Thuy Van N, Shahverdi M, Qureshi MI, Khan N (2021) Effectiveness of digital technology in education during COVID-19 pandemic. a bibliometric analysis. Int J Interact Mob Technol 15:136

Nagle J (2018) Twitter, cyber-violence, and the need for a critical social media literacy in teacher education: a review of the literature. Teach Teach Education 76:86–94

Nazare J, Woolf A, Sysoev I, Ballinger S, Saveski M, Walker M, Roy D (2022) Technology-assisted coaching can increase engagement with learning technology at home and caregivers’ awareness of it. Comput Educ 188:104565

Nguyen UP, Hallinger P (2020) Assessing the distinctive contributions of simulation & gaming to the literature, 1970-2019: a bibliometric review. Simul Gaming 104687812094156.

Nygren H, Nissinen K, Hämäläinen R, Wever B(2019) Lifelong learning: formal, non-formal and informal learning in the context of the use of problem-solving skills in technology-rich environments Br J Educ Technol 50:1759–1770.

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg 88:105906

Pan SL, Zhang S(2020) From fighting COVID-19 pandemic to tackling sustainable development goals: an opportunity for responsible information systems research Int J Inf Manage 55:102196.

Pan X, Yan E, Cui M, Hua W(2018) Examining the usage, citation, and diffusion patterns of bibliometric mapping software: a comparative study of three tools J Informetr 12:481–493.

Parris Z, Cale L, Harris J, Casey A (2022) Physical activity for health, covid-19 and social media: what, where and why?. Movimento, 28.

Pasquini LA, Evangelopoulos N (2016) Sociotechnical stewardship in higher education: a field study of social media policy documents. J Comput High Educ 29:218–239

Pérez-Sanagustín M, Hernández-Leo D, Santos P, Delgado Kloos C, Blat J(2014) Augmenting reality and formality of informal and non-formal settings to enhance blended learning IEEE Trans Learn Technol 7:118–131.

Pinto M, Leite C (2020) Digital technologies in support of students learning in Higher Education: literature review. Digital Education Review 343–360.

Pires F, Masanet MJ, Tomasena JM, Scolari CA(2022) Learning with YouTube: beyond formal and informal through new actors, strategies and affordances Convergence 28(3):838–853.

Pritchard A (1969) Statistical bibliography or bibliometrics 25:348

Romero M, Romeu T, Guitert M, Baztán P (2021) Digital transformation in higher education: the UOC case. In ICERI2021 Proceedings (pp. 6695–6703). IATED

Romero-Hall E, Jaramillo Cherrez N (2022) Teaching in times of disruption: faculty digital literacy in higher education during the COVID-19 pandemic. Innovations in Education and Teaching International 1–11.

Rospigliosi PA(2023) Artificial intelligence in teaching and learning: what questions should we ask of ChatGPT? Interactive Learning Environments 31:1–3.

Salas-Pilco SZ, Yang Y, Zhang Z(2022) Student engagement in online learning in Latin American higher education during the COVID-19 pandemic: a systematic review. Br J Educ Technol 53(3):593–619.

Selwyn N(2009) The digital native-myth and reality In Aslib proceedings 61(4):364–379.

Selwyn N(2012) Making sense of young people, education and digital technology: the role of sociological theory Oxford Review of Education 38:81–96.

Selwyn N, Facer K(2014) The sociology of education and digital technology: past, present and future Oxford Rev Educ 40:482–496.

Selwyn N, Banaji S, Hadjithoma-Garstka C, Clark W(2011) Providing a platform for parents? Exploring the nature of parental engagement with school Learning Platforms J Comput Assist Learn 27:314–323.

Selwyn N, Aagaard J (2020) Banning mobile phones from classrooms-an opportunity to advance understandings of technology addiction, distraction and cyberbullying. Br J Educ Technol 52.

Selwyn N, O’Neill C, Smith G, Andrejevic M, Gu X (2021) A necessary evil? The rise of online exam proctoring in Australian universities. Media Int Austr 1329878X2110058.

Selwyn N, Pangrazio L, Nemorin S, Perrotta C (2019) What might the school of 2030 be like? An exercise in social science fiction. Learn, Media Technol 1–17.

Selwyn, N (2016) What works and why?* Understanding successful technology enabled learning within institutional contexts 2016 Final report Appendices (Part B). Monash University Griffith University

Sjöberg D, Holmgren R (2021) Informal workplace learning in swedish police education-a teacher perspective. Vocations and Learning.

Strotmann A, Zhao D (2012) Author name disambiguation: what difference does it make in author-based citation analysis? J Am Soc Inf Sci Technol 63:1820–1833

Article   CAS   Google Scholar  

Sutherland R, Facer K, Furlong R, Furlong J(2000) A new environment for education? The computer in the home. Comput Educ 34:195–212.

Szeto E, Cheng AY-N, Hong J-C(2015) Learning with social media: how do preservice teachers integrate YouTube and Social Media in teaching? Asia-Pac Educ Res 25:35–44.

Tang E, Lam C(2014) Building an effective online learning community (OLC) in blog-based teaching portfolios Int High Educ 20:79–85.

Taskin Z, Al U(2019) Natural language processing applications in library and information science Online Inf Rev 43:676–690.

Tegtmeyer K, Ibsen L, Goldstein B(2001) Computer-assisted learning in critical care: from ENIAC to HAL Crit Care Med 29:N177–N182.

Article   CAS   PubMed   Google Scholar  

Timotheou S, Miliou O, Dimitriadis Y, Sobrino SV, Giannoutsou N, Cachia R, Moné AM, Ioannou A(2023) Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: a literature review. Educ Inf Technol 28(6):6695–6726.

Trujillo Maza EM, Gómez Lozano MT, Cardozo Alarcón AC, Moreno Zuluaga L, Gamba Fadul M (2016) Blended learning supported by digital technology and competency-based medical education: a case study of the social medicine course at the Universidad de los Andes, Colombia. Int J Educ Technol High Educ 13.

Turin O, Friesem Y(2020) Is that media literacy?: Israeli and US media scholars’ perceptions of the field J Media Lit Educ 12:132–144.

Van Eck NJ, Waltman L (2019) VOSviewer manual. Universiteit Leiden

Vratulis V, Clarke T, Hoban G, Erickson G(2011) Additive and disruptive pedagogies: the use of slowmation as an example of digital technology implementation Teach Teach Educ 27:1179–1188.

Wang CL, Dai J, Xu LJ (2022) Big data and data mining in education: a bibliometrics study from 2010 to 2022. In 2022 7th International Conference on Cloud Computing and Big Data Analytics ( ICCCBDA ) (pp. 507-512). IEEE.

Wang CL, Dai J, Zhu KK, Yu T, Gu XQ (2023) Understanding the continuance intention of college students toward new E-learning spaces based on an integrated model of the TAM and TTF. Int J Hum-Comput Int 1–14.

Wong L-H, Boticki I, Sun J, Looi C-K(2011) Improving the scaffolds of a mobile-assisted Chinese character forming game via a design-based research cycle Comput Hum Behav 27:1783–1793.

Wu R, Yu Z (2023) Do AI chatbots improve students learning outcomes? Evidence from a meta-analysis. Br J Educ Technol.

Yang D, Zhou J, Shi D, Pan Q, Wang D, Chen X, Liu J (2022) Research status, hotspots, and evolutionary trends of global digital education via knowledge graph analysis. Sustainability 14:15157–15157.

Yu T, Dai J, Wang CL (2023) Adoption of blended learning: Chinese university students’ perspectives. Humanit Soc Sci Commun 10:390.

Yu Z (2022) Sustaining student roles, digital literacy, learning achievements, and motivation in online learning environments during the COVID-19 pandemic. Sustainability 14:4388.

Za S, Spagnoletti P, North-Samardzic A(2014) Organisational learning as an emerging process: the generative role of digital tools in informal learning practices Br J Educ Technol 45:1023–1035.

Zhang X, Chen Y, Hu L, Wang Y (2022) The metaverse in education: definition, framework, features, potential applications, challenges, and future research topics. Front Psychol 13:1016300.

Zhou M, Dzingirai C, Hove K, Chitata T, Mugandani R (2022) Adoption, use and enhancement of virtual learning during COVID-19. Education and Information Technologies.

Download references


This research was supported by the Zhejiang Provincial Social Science Planning Project, “Mechanisms and Pathways for Empowering Classroom Teaching through Learning Spaces under the Strategy of High-Quality Education Development”, the 2022 National Social Science Foundation Education Youth Project “Research on the Strategy of Creating Learning Space Value and Empowering Classroom Teaching under the background of ‘Double Reduction’” (Grant No. CCA220319) and the National College Student Innovation and Entrepreneurship Training Program of China (Grant No. 202310337023).

Author information

Authors and affiliations.

College of Educational Science and Technology, Zhejiang University of Technology, Zhejiang, China

Chengliang Wang, Xiaojiao Chen, Yidan Liu & Yuhui Jing

Graduate School of Business, Universiti Sains Malaysia, Minden, Malaysia

Department of Management, The Chinese University of Hong Kong, Hong Kong, China

College of Humanities and Social Sciences, Beihang University, Beijing, China

You can also search for this author in PubMed   Google Scholar


Conceptualization: Y.J., C.W.; methodology, C.W.; software, C.W., Y.L.; writing-original draft preparation, C.W., Y.L.; writing-review and editing, T.Y., Y.L., C.W.; supervision, X.C., T.Y.; project administration, Y.J.; funding acquisition, X.C., Y.L. All authors read and approved the final manuscript. All authors have read and approved the re-submission of the manuscript.

Corresponding author

Correspondence to Yuhui Jing .

Ethics declarations

Ethical approval.

Ethical approval was not required as the study did not involve human participants.

Informed consent

Informed consent was not required as the study did not involve human participants.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit .

Reprints and permissions

About this article

Cite this article.

Wang, C., Chen, X., Yu, T. et al. Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanit Soc Sci Commun 11 , 256 (2024).

Download citation

Received : 11 July 2023

Accepted : 17 January 2024

Published : 12 February 2024


Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

research topics on technology in education

15 EdTech research papers that we share all the time

We hope you saw our recent blog post responding to questions we often get about interesting large-scale EdTech initiatives. Another question we are often asked is: “What EdTech research should I know about?” 

As Sara’s blog post explains, one of the Hub’s core spheres of work is research, so we ourselves are very interested in the answer to this question. Katy’s latest blog post explains how the Hub’s research programme is addressing this question through a literature review to create a foundation for further research.  While the literature review is in progress, we thought we would share an initial list of EdTech papers that we often reach for. At the Hub we are fortunate enough to have authors of several papers on this list as members of our team. 

All papers on this list are linked to a record in the EdTech Hub’s growing document library – where you will find the citation and source to the full text. This library is currently an alpha version. This means it’s the first version of the service and we’re testing how it works for you. If you have any feedback or find any issues with our evidence library, please get in touch.

Tablet use in schools: a critical review of the evidence for learning outcomes

This critical review by our own Bjӧrn Haßler, Sara Hennessy, and Louis Major has been cited over 200 times since it was published in 2016. It examines evidence from 23 studies on tablet use at the primary and secondary school levels. It discusses the fragmented nature of the knowledge base and limited rigorous evidence on tablet use in education. 

Haßler, B., Major, L., & Hennessy, S. (2016) Tablet use in schools: a critical review of the evidence for learning outcomes . Journal of Computer Assisted Learning, 32(2), 139-156.

The impact and reach of MOOCs: a developing countries’ perspective

This article challenges the narrative that Massive Open Online Courses (MOOCs) are a solution to low and middle-income countries’ (LMIC) lack of access to education, examining the features of MOOCs from their perspectives. It argues that a complicated set of conditions, including access, language, and computer literacy, among others, challenge the viability of MOOCs as a solution for populations in LMIC. 

Liyanagunawardena, T., Williams, S., & Adams, A. (2013) The impact and reach of MOOCs: a developing countries’ perspective. eLearning Papers , 33(33).

Technology and education – Why it’s crucial to be critical

A thought-provoking read, Selwyn’s book chapter argues that technology and education should continuously be viewed through a critical lens. It points to how the use of technology in education is entwined with issues of inequality, domination, and exploitation, and offers suggestions for how to grapple with these issues. 

Selwyn, N. (2015) Technology and education – Why it’s crucial to be critical. In S. Bulfin, N. F. Johnson & L. Rowan (Eds.), Critical Perspectives on Technology and Education (pp. 245-255). Basingstoke and St. Martins, New York: Palgrave Macmillan.

Moving beyond the predictable failure of Ed-Tech initiatives

This article argues that a narrow vision of digital technology, which ignores the complexity of education, is becoming an obstacle to improvement and transformation of education. Specifically, the authors critically reflect on common approaches to introducing digital technology in education under the guise of promoting equality and digital inclusion.

Sancho-Gil, J.M., Rivera-Vargas, P. & Miño-Puigcercós, R. (2019) Moving beyond the predictable failure of Ed-Tech initiatives. Learning, Media and Technology , early view. DOI: 10.1080/17439884.2019.1666873

Synergies Between the Principles for Digital Development and Four Case Studies

The REAL Centre’s report, which includes contributions from the Hub’s own ranks, is one of the few we’ve seen that provides an in-depth exploration of how the Principles for Digital Development apply to the education sector. It uses four case studies on the work of the Aga Khan Foundation, Camfed, the Punjab Education and Technology Board, and the Varkey Foundation. 

REAL Centre (2018). Synergies Between the Principles for Digital Development and Four Case Studies. Cambridge, UK: Research for Equitable Access and Learning (REAL) Centre, Faculty of Education, University of Cambridge .

Education technology map: guidance document

This report by the Hub’s Jigsaw colleagues accompanies a comprehensive map of 401 resources with evidence on the use of EdTech in low-resource environments. The evidence mapping reviews certain criteria of the resources from sources such as journal indices, online research, evaluation repositories, and resource centres and experts. The type of criteria it maps include: the geographical location of study, outcomes studied, and type of EdTech introduced.  While not inclusive of the latest EdTech research and evidence (from 2016 to the present), this mapping represents a strong starting point to understand what we know about EdTech as well as the characteristics of existing evidence.

Muyoya, C., Brugha, M., Hollow, D. (2016). Education technology map: guidance document. Jigsaw, United Kingdom.

Scaling Access & Impact: Realizing the Power of EdTech

Commissioned by Omidyar Network and written by RTI, this executive summary (with the full report expected soon) is a useful examination of the factors needed to enable, scale, and sustain equitable EdTech on a national basis. Four country reports on Chile, China, Indonesia, and the United States examine at-scale access and use of EdTech across a broad spectrum of students. It also provides a framework for an ecosystem that will allow EdTech to be equitable and able to be scaled.  

S caling Access & Impact: Realizing the Power of EdTech (Executive Summary). Omidyar Network.

Perspectives on Technology, Resources and Learning – Productive Classroom Practices, Effective Teacher Professional Development

If you are interested in how technology can be used in the classroom and to support teacher professional development, this report by the Hub’s Björn Haßler and members of the Faculty of Education at the University of Cambridge emphasizes the key point that technology should be seen as complementary to, rather than as a replacement for, teachers. As the authors put it, “the teacher and teacher education are central for the successful integration of digital technology into the classroom.” The report is also accompanied by a toolkit (linked below) with questions that can be used to interrogate EdTech interventions.

Haßler, B., Major, L., Warwick, P., Watson, S., Hennessy, S., & Nichol, B. (2016). Perspectives on Technology, Resources and Learning – Productive Classroom Practices, Effective Teacher Professional Development . Faculty of Education, University of Cambridge. DOI:10.5281/zenodo.2626440

Haßler, B., Major, L., Warwick, P., Watson, S., Hennessy, S., & Nichol, B. (2016). A short guide on the use of technology in learning: Perspectives and Toolkit for Discussion . Faculty of Education, University of Cambridge. DOI:10.5281/zenodo.2626660

Teacher Factors Influencing Classroom Use of ICT in Sub-Saharan Africa

In this paper, the Hub’s Sara Hennessy and co-authors synthesise literature on teachers’ use of ICT, with a focus on using ICT to improve the quality of teaching and learning. They find evidence to support the integration of ICT into subject learning, instead of treating it as a discrete subject, and to provide relevant preparation to teachers during pre- and in-service training to use ICT in classrooms. Although this evidence has been available for a decade, the implications of the paper’s findings are still not often reflected in practice.  

Hennessy, S., Harrison, D., & Wamakote, L. (2010). Teacher Factors Influencing Classroom Use of ICT in Sub-Saharan Africa. Itupale Online Journal of African Studies, 2, 39- 54.

Information and Communications Technologies in Secondary Education in Sub-Saharan Africa: Policies, Practices, Trends, and Recommendations

This landscape review by Burns and co-authors offers a useful descriptive starting point for understanding technology use in sub-Saharan Africa in secondary education, including the policy environment, key actors, promising practices, challenges, trends, and opportunities. The report includes four case studies on South Africa, Mauritius, Botswana, and Cape Verde. 

Burns, M., Santally, M. I., Halkhoree, R., Sungkur, K. R., Juggurnath, B., Rajabalee, Y. B. (2019) Information and Communications Technologies in Secondary Education in Sub-Saharan Africa: Policies, Practices, Trends, and Recommendations. Mastercard Foundation.

The influence of infrastructure, training, content and communication on the success of NEPAD’S pilot e-Schools in Kenya

This study examines the impact of training teachers to use ICT, on the success of NEPAD’S e-Schools. The e-Schools objectives were to impart ICT skills to students, enhance teachers’ capacities through the use of ICT in teaching, improve school management and increase access to education. Unlike other studies on the subject, Nyawoga, Ocholla, and Mutula crucially recognise that while teachers received technical ICT training, they did not receive training on pedagogies for integrating ICT in teaching and learning. 

Nyagowa, H. O., Ocholla, D. N., & Mutula, S. M. (2014). T he influence of infrastructure, training, content and communication on the success of NEPAD’S pilot e-Schools in Kenya . Information Development, 30(3), 235-246 .

Education in Conflict and Crisis: How Can Technology Make a Difference?

This landscape review identifies ICT projects supporting education in conflict and crisis settings. It finds that most of the projects operate in post-conflict settings and focus on the long-term development of such places. The report hones in on major thematic areas of professional development and student learning. It also presents directions for further research, including considerations of conflict sensitivity and inclusion in the use of ICT. 

Dahya, N. (2016) Education in Conflict and Crisis: How Can Technology Make a Difference? A Landscape Review . GIZ.

Does technology improve reading outcomes? Comparing the effectiveness and cost-effectiveness of ICT interventions for early-grade reading in Kenya

This randomized controlled trial contributes to the limited evidence base on the effects of different types of ICT investments on learning outcomes. All groups participated in the ‘base’ initiative which focused on training teachers and headteachers in literacy and numeracy, books for every student, teacher guides that matched closely with the content of the students’ book, and modest ICT intervention with tablets provided only for government-funded instructional supervisors. The RCT then compared outcomes from three interventions:  (1) base program plus e-readers for students, (2) base program plus tablets for teachers, and (3) the control group who were treated only with the base program. The paper finds that the classroom-level ICT investments do not improve literacy outcomes significantly more than the base program alone, and that cost considerations are crucial in selecting ICT investments in education.

Piper, B., Zuilkowski, S., Kwayumba, D., & Strigel, C. (2016). Does technology improve reading outcomes? Comparing the effectiveness and cost-effectiveness of ICT interventions for early-grade reading in Kenya. International Journal of Educational Development (49), 204-214.

[FORTHCOMING] Technology in education in low-income countries: Problem analysis and focus of the EdTech Hub’s work

Informed by the research cited in this list (and much more) – the Hub will soon publish a problem analysis. It will define our focus and the scope of our work. To give a taste of what is to come, the problem analysis will explain why we will prioritise teachers, marginalised groups, and use a systems lens. It will also explore emergent challenges in EdTech research, design, and implementation.

EdTech Hub. (2020). Technology in education in low-income countries: Problem analysis and focus of the Hub’s work (EdTech Hub Working Paper No. 5). London, UK.

It is important to note that we have included a mix of research types at varying levels of rigour, from landscape reviews and evidence maps, to critical reviews and case studies. Our list is not comprehensive and has some obvious limitations (they are all in English, for one). If you are interested in exploring more papers and evidence, don’t forget to check out the EdTech Hub’s growing document library , where you will find not just links to the full papers in this list but over 200 resources, with more being added each day.

What interesting EdTech research have you recently read, and what did you take away from it? Let us know in the comments section or on Twitter at @GlobalEdTechHub and use #EdTechHub

Related Posts

Implementing a virtual learning environment in ..., raising readers: putting parents and schools ..., privacy overview.

Global Education Monitoring Report

technology in education cover image

Technology in education

As recognised in the Incheon Declaration, the achievement of SDG 4 is dependent on opportunities and challenges posed by technology, a relationship that was strengthened by the onset of the COVID-19 pandemic. Technology appears in six out of the ten targets in the fourth Sustainable Development goal on education. These references recognize that technology affects education through five distinct channels, as input, means of delivery, skill, tool for planning, and providing a social and cultural context.

There are often bitter divisions in how the role of technology is viewed, however. These divisions are widening as the technology is evolving at breakneck speed.  The 2023 GEM Report on technology and education explores these debates, examining education challenges to which appropriate use of technology can offer solutions (access, equity and inclusion; quality; technology advancement; system management), while recognizing that many solutions proposed may also be detrimental.

The report also explores three system-wide conditions (access to technology, governance regulation, and teacher preparation) that need to be met for any technology in education to reach its full potential. It provides the mid-term assessment of progress towards SDG 4 , which was summarized in a brochure and promoted at the 2023 SDG Summit.

The 2023 GEM Report and 200 PEER country profiles on technology and education were launched on 26 July. A recording of the global launch event can be watched  here  and a south-south dialogue between Ministers of education in Latin America and Africa here .

research topics on technology in education

Background material


Watch the launch event


research topics on technology in education

The GEM Report is partnering with Restless Development  to mobilize youth globally to inform the development of the 2023 Youth Report, exploring how technology can address various education challenges.

research topics on technology in education

The GEM Report ran a consultation process to collect feedback and evidence on the proposed lines of research of the 2023 concept note.

Technology in education: a tool on whose terms?

Related resources

on technology and education

in quality and school infrastructure

teacher teaching

Related content

Monitoring SDG 4: Quality

  • Become a Member
  • Artificial Intelligence
  • Computational Thinking
  • Digital Citizenship
  • Edtech Selection
  • Global Collaborations
  • STEAM in Education
  • Teacher Preparation
  • ISTE Certification
  • School Partners
  • Career Development
  • ISTELive 24
  • 2024 ASCD Annual Conference
  • Solutions Summit
  • Leadership Exchange
  • 2024 ASCD Leadership Summit
  • Edtech Product Database
  • Solutions Network
  • Sponsorship & Advertising
  • Sponsorship & Advertising
  • Learning Library

The 9 hottest topics in edtech

  • Education Leadership

Img id 674 Version Id B39li HH Shaxv Nx Zt43 APAOX Ee Kp7l0 Lf

The most compelling topics among educators who embrace technology to transform teaching and learning are not about the tech at all, but about the students . Here’s a list of the hottest trends in edtech right now.

1. Computational thinking

Computational thinking  (CT) is no longer a concept discussed only in computer science or coding classes. Educators are finding that computation thinking is a cross-disciplinary skill and is just as relevant in language arts and math classes. Educators are becoming skilled at incorporating CT components like decomposition, generalizing, algorithmic thinking, evaluation and abstraction – no matter the subject area. Together, these steps teach students the foundations of how to approach a problem and solve it using reasoning, creativity and expression, as well as providing a new way to demonstrate content knowledge.

2. Professional learning

Professional development (PD) is out. Professional learning (PL) is in. What’s the difference? Instead of developing people via PD (collective eye roll for the sit-and-get of the past), PL focuses on providing ongoing, embedded opportunities for growth using active methods. Professional learning is differentiated, personalized and workday friendly for busy educators. Look for an added focus on professional learning for instructional technology coaches, helping them up their game as they guide staff integrating technology in their classrooms. 

3. AR, VR and mixed reality

In the past, discussions about artificial reality (AR), virtual reality (VR) and mixed reality in schools focused on using what others had developed. Now, both educators and students are moving into creation mode with these technologies. Students are harnessing their creativity to develop artifacts of their learning in all curricular areas using these tools. 

4. Artificial intelligence

How can we take advantage of artificial intelligence (AI) in learning environments? Digital voice assistants like Alexa and Echo have made their way into classrooms , but educators are just uncovering ways to use them. Look for AI to explode in schools in the near future, predicts ISTE board member Hall Davidson, senior director, global learning initiatives for Discovery Education. He sees the potential of AI to support students in reaching higher levels of learning and thinking as they use the devices to practice asking questions and thinking out loud. 

5. Global learning

The concept of global learning isn’t new. What’s fresh about the topic now is the level of maturity it will reach as more and more educators understand the value of learning in a global context. The excitement around students participating in global collaboration is only going to increase. Why? Because, as educator Mali Bickley puts it, global learning enables students and teachers to harness the power of technology to develop relationships with their global peers while addressing complex and important global issues. Students who have participated in global learning provide the proof – their discussions and collaborative projects have addressed worldwide problems like food scarcity, climate change, refugee crises and child labor.

6. Learner profiles

Both the ISTE Standards for Students and the ISTE Standards for Educators include specific profiles of learners. The Student Standards provide a framework for helping students become Empowered Learners, Digital Citizens , Knowledge Constructors, Innovative Designers, Computational Thinkers, Creative Communicators and Global Collaborators, while the Educator Standards are a road map for becoming Learners, Leaders, Citizens, Collaborators, Designers, Facilitators and Analysts. Both students and educators are embracing their new roles, moving from adoption of learner profiles to successful implementation.

7. Learning sciences

Advances in technology and rigorous scientific experimentation mean scientists know more than ever before about how the brain functions. Increasingly, they’re disseminating that information to educators and education leaders in the hope of optimizing teaching and learning. Informed by neuroscience, cognitive psychology, development psychology, sociology and computer science, the learning sciences speak to the heart of education – how to best help humans learn. Look for a focus on updating educators’ knowledge of the learning sciences and bringing students to the table to help them understand how they learn.

8. Digital citizenship

Digital citizenship  is being redefined. The focus is moving away from warning students about online risks or trying to curtail their activities and toward helping them leverage the power of digital media to work toward creation, social justice and equity. The new digital citizenship, also reflected in the ISTE Standards for Students, is about being in community with others and creating digital citizenship curricula that shows students possibilities over problems, opportunities over risks and community successes over personal gain.

9. Student-centered learning

Student-centered learning environments have been called “the schools of the future.” Truth be told, at many schools, the future is here. That’s because the benefits of student-centered learning and the student agency that comes with it are being proven out.

Chris Lehmann, founding principal of the Science Leadership Academy in Philadelphia, Pennsylvania, says student-driven learning isn’t a lofty ideal. It’s a moral imperative. And by almost any measure, from test scores to graduation rates, next-generation schools that have put students at the center of their learning are outperforming their neighbors. “There are enough examples out there now that you have to work hard to say that this stuff doesn’t work,” Lehmann says.

This list of hot edtech topics emerged from a review of thousands of educator-created sessions submitted for the 2018 ISTE Conference & Expo . Curious how the topics change from year to year? Here were the hottest topics for 2017 .

  • artificial intelligence

How Has Technology Changed Education?

Technology has impacted almost every aspect of life today, and education is no exception. Or is it? In some ways, education seems much the same as it has been for many years. A 14th century illustration by Laurentius de Voltolina depicts a university lecture in medieval Italy. The scene is easily recognizable because of its parallels to the modern day. The teacher lectures from a podium at the front of the room while the students sit in rows and listen. Some of the students have books open in front of them and appear to be following along. A few look bored. Some are talking to their neighbors. One appears to be sleeping. Classrooms today do not look much different, though you might find modern students looking at their laptops, tablets, or smart phones instead of books (though probably open to Facebook). A cynic would say that technology has done nothing to change education.

However, in many ways, technology has profoundly changed education. For one, technology has greatly expanded access to education. In medieval times, books were rare and only an elite few had access to educational opportunities. Individuals had to travel to centers of learning to get an education. Today, massive amounts of information (books, audio, images, videos) are available at one’s fingertips through the Internet, and opportunities for formal learning are available online worldwide through the Khan Academy, MOOCs, podcasts, traditional online degree programs, and more. Access to learning opportunities today is unprecedented in scope thanks to technology.

Opportunities for communication and collaboration have also been expanded by technology. Traditionally, classrooms have been relatively isolated, and collaboration has been limited to other students in the same classroom or building. Today, technology enables forms of communication and collaboration undreamt of in the past. Students in a classroom in the rural U.S., for example, can learn about the Arctic by following the expedition of a team of scientists in the region, read scientists’ blog posting, view photos, e-mail questions to the scientists, and even talk live with the scientists via a videoconference. Students can share what they are learning with students in other classrooms in other states who are tracking the same expedition. Students can collaborate on group projects using technology-based tools such as wikis and Google docs. The walls of the classrooms are no longer a barrier as technology enables new ways of learning, communicating, and working collaboratively.

Technology has also begun to change the roles of teachers and learners. In the traditional classroom, such as what we see depicted in de Voltolina’s illustration, the teacher is the primary source of information, and the learners passively receive it. This model of the teacher as the “sage on the stage” has been in education for a long time, and it is still very much in evidence today. However, because of the access to information and educational opportunity that technology has enabled, in many classrooms today we see the teacher’s role shifting to the “guide on the side” as students take more responsibility for their own learning using technology to gather relevant information. Schools and universities across the country are beginning to redesign learning spaces to enable this new model of education, foster more interaction and small group work, and use technology as an enabler.

Technology is a powerful tool that can support and transform education in many ways, from making it easier for teachers to create instructional materials to enabling new ways for people to learn and work together. With the worldwide reach of the Internet and the ubiquity of smart devices that can connect to it, a new age of anytime anywhere education is dawning. It will be up to instructional designers and educational technologies to make the most of the opportunities provided by technology to change education so that effective and efficient education is available to everyone everywhere.

You can help shape the influence of technology in education with an Online Master of Science in Education in Learning Design and Technology from Purdue University Online. This accredited program offers studies in exciting new technologies that are shaping education and offers students the opportunity to take part in the future of innovation.

Learn more about the online MSEd in Learning Design and Technology at Purdue University today and help redefine the way in which individuals learn. Call (877) 497-5851 to speak with an admissions advisor or to request more information.

  • Frontiers in Education
  • Digital Learning Innovations
  • Research Topics

Leveraging emerging technology for refugee education and social integration

Total Downloads

Total Views and Downloads

About this Research Topic

The unprecedented displacement of over 70 million forcibly displaced people globally has created an urgent need for innovative solutions to provide education, empowerment, and integration for these marginalized populations. Emerging technologies like artificial intelligence, virtual reality, and social robotics show the potential to deliver more personalized, scalable learning and social services to forced migrants. However, deploying these advanced technologies in forced migration contexts carries profound ethical responsibilities. Thoughtful research and evidence-based guidance are crucial to ensure these rapidly developing technologies are harnessed responsibly to empower rather than further marginalize forced migrant communities. This Research Topic seeks contributions that help shape the ethical, rights-based implementation of technologies like AI and VR for forced migrant education, healthcare, political participation, and social services. Specifically, we welcome systematic analysis, discussion papers, theoretical papers, original research, and case studies providing insights on key questions surrounding the risks of algorithmic bias, protection of data privacy, promotion of accessibility, and participatory design of technology in consultation with forced migrant populations themselves. This Research Topic welcomes contributions addressing, but not limited to following topics: • Understanding and mitigating risks of algorithmic bias, inaccessibility, and digital discrimination in education technologies for diverse and vulnerable forced migrant populations. • Promoting digital literacy, access, and motivational design to encourage active and safe participation of forced migrants in tech-enabled education. • Developing ethical frameworks and secure data sharing policies to protect forced migrant data privacy and prevent misuse of education technologies. • Leveraging AI VR and other emerging technologies to provide culturally responsive support and resources while preserving forced migrant diversity, agency, and inclusion. • Optimizing integration of emerging technologies with teachers and communities to holistically support forced migrant well-being, growth, and self-determination. • Comparative assessment of education technologies' impacts on multiculturalism, social inclusion, and empowerment of forced migrants. • Designing human-centered education technologies that amplify forced migrant voices, perspectives, and participation in the process. • Fostering pluralism, dialogue, and understanding between forced migrants and host communities through the thoughtful integration of technologies. The unprecedented global forced migration crisis calls for innovative solutions to empower and support marginalized populations. Emerging technologies like AI and VR show potential to expand access to education, healthcare, and social services when thoughtfully designed and deployed. However, these technologies also risk further excluding forced migrants if issues of algorithmic bias, inaccessibility, discrimination, and threats to data privacy go unaddressed. This Research Topic invites contributions from diverse disciplines to shape an ethical, empowering framework for technologies that uphold forced migrant rights and inclusion. We welcome research identifying risks of advanced technologies in forced migration contexts as well as evidence-based solutions to mitigate harm. This includes studies on promoting digital literacy, participatory design, motivational strategies, secure data policies, and cultural responsiveness in tech-enabled services. We encourage analysis on optimizing technology to support holistic forced migrant well-being and self-determination. Together we can build knowledge to responsibly apply AI, VR, robotics, and more to empower forced migrants, fostering greater equity, dialogue, and social inclusion. We look forward to research outputs spanning systematic reviews, theoretical discussions, original empirical findings, and case studies to publish in this timely issue. Your contributions will help ensure emerging technologies empower rather than further marginalize the world’s most vulnerable people.

Keywords : Refugee Education, AI discrimination, AI, AI bias

Important Note : All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Topic Editors

Topic coordinators, submission deadlines, participating journals.

Manuscripts can be submitted to this Research Topic via the following journals:

total views

  • Demographics

No records found

total views article views downloads topic views

Top countries

Top referring sites, about frontiers research topics.

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Edtech in Elementary Schools

research topics on technology in education

Technology has disrupted everything from healthcare and banking to transportation and printing. Now it’s gunning for that staple of childhood: blocks. 

For a lot of kids, blocks on the floor have been replaced by virtual blocks on a screen. And instead of using them to build castles, they’re using them to learn coding courtesy of a LEGO-inspired programming language called Scratch . 

Designed for kids ages eight to 16, Scratch was created by the Lifelong Kindergarten Lab at MIT and originally released in 2007. (A simpler version,  Scratch Jr. , caters to younger users.) Its mission: to facilitate earlier and more in-depth tech education.

“We need to expand the notion of ‘digital fluency’ to include designing and creating, not just browsing and interacting,” Scratch’s inventors  wrote in 2009 .

Over the past decade, that view has gained mainstream traction, as evidenced by the company's more than 40 million registered users in and out of schools.

Over at UC Irvine’s Digital Learning Lab,  Dr. Mark Walschauer and his team are using funding from the National Science Foundation to design a Scratch curriculum for early elementary-age kids. Walschauer is particularly fond of Scratch’s user-friendly language. Its block structure, he says, makes typos (common in kid coding) impossible. On top of that, its media-friendly interface lets kids incorporate cat photos and their own voices in programming projects.

More broadly, Walschauer appreciates that teachers increasingly employ technology in elementary classrooms.

“I’m a firm believer in developing computational thinking in schools,” he told Built In, “because I think computation is such an important part of all careers.”

7 Edtech Companies to Know

Houghton mifflin harcourt.

  • Mind Research Institute


The screentime conundrum.

Edtech is a multi-billion-dollar industry, and the growing preK-12 software market is currently worth more than $8 billion . Digital tools abound to help kids with reading, writing, basic math and other subjects. In Southern California schools, especially, Dr. Walschauer often sees tablet and laptop carts in elementary classrooms. 

But the combo of elementary-age kids and screentime is a fraught subject for many. Detractors say it’s unhealthy. And it’s true that researchers have scant data on how screens impact kids’ highly malleable brains in the long term. In the spirit of caution , the World Health Organization recently recommended an hour or less of daily screentime for kids under five. “Less,” the group added, “is better.”

Dr. Marilyn Price-Mitchell , a developmental psychologist who studies the impact of technology on young people, seconds the WHO’s sentiments. As technology has become more prevalent over the past few decades, she notes, robust longitudinal studies have found a decline in empathy among college students. 

“We don’t really know what the cause of that decline is,” Dr. Price-Mitchell told Built In, “but we know that children are spending less face-to-face time with humans.”

But their face-to-screen bonding has greatly intensified.

Elementary educators, then, must deal with a conundrum: preparing kids for a hyper-connected world while simultaneously encouraging healthy human development. The latter requires in-person interaction, not coding savvy.

Price-Mitchell and Walschauer agree, however, that tech can play an important role in elementary classrooms by augmenting educational toolkits rather than dominating the curriculum. 

We’ve rounded up 7 companies finding creative ways to integrate technology in the elementary classroom.

research topics on technology in education

Location: Montreal, Quebec

Paper, a remote-first edtech platform, partners with elementary schools throughout the U.S. and Canada to provide tutoring services at no cost to families. Paper tutors can help students in a variety of languages from English and Spanish to French or Mandarin. Tutors are also available to students 24/7 using the company’s live help chat feature. The company also offers a number of online tools to help elementary-school students with reading and math proficiency. 

hmh logo

Location: Boston

How it’s shaping elementary edtech:  The storied textbook publisher also developed  iRead , a digital literacy program designed to get every student reading by third grade. As part of the program, which is backed by a multi-school-district study , kids send digital avatars to virtual classes that automatically adapt to their strengths and weaknesses. 

research topics on technology in education

MIND Education

Location: Irvine, CA

How it’s shaping elementary edtech: This non-profit organization makes ST Math , a math-instruction program whose vibrant world teems with penguins and rainbow balloons. Reportedly used by 1.2 million elementary-age students, the program promotes deep conceptual understanding over mere memorization. 

More in Education and Technology Companies That Hire Former Teachers

research topics on technology in education

Lexia Learning

Location: Concord, MA

How it’s shaping elementary edtech: Designed in accordance with the latest pedagogical research, Lexia’s CORE5 literacy platform cuts testing out of the reading process. Instead, the interface assesses K-5 students as they read, and the adaptive lessons—focused on building blocks like phonetics and comprehension—find and fill in competency gaps as they appear.

research topics on technology in education

Location: New York City

How it’s shaping elementary edtech: Amplify has helped modernize elementary school instruction and assessment for a couple of decades now. Its teacher-friendly tech tools cover a variety of subjects, including reading to science. The company also offers course sequences on more specific topics like fractions and English-language acquisition.

research topics on technology in education

Location: Brooklyn, NY

How it’s shaping elementary edtech: Kids use Flocabulary to learn new vocabulary words from video lessons that feature hip-hop-style songs. The catalog covers K-12 science, math and English terminology, and teachers can reinforce new lingo with worksheets and other app-based assessments.

research topics on technology in education

Location: Berkeley, CA

How it’s shaping elementary edtech: Enuma’s signature app, Todo Math , features more than 40 multi-level math games in which kids master Common Core concepts and earn cyber-stars for correct answers. Like all of the company’s apps, Todo Math welcomes kids of all abilities. It even has a special font for dyslexic readers and tools for circumventing fine motor skill challenges.

The Future of Elementary School

Walschauer, for one, sees a few especially promising elementary edtech tools on the horizon. There’s formative assessment technology, which Lexia and other edtech companies use to assess learning as it happens. By making summative assessments (like testing) redundant, it could help decrease the stress and shame that often accompanies them. 

In a similar vein, Walschauer is excited about the surge of personalized tech tools for kids with special needs. Even the mere presence of a computer, he says, can foster an atmosphere of greater inclusion. As one example, he refers to the time he witnessed an autistic 10-year-old bond with a classmate on a laptop-based project.

“To just to sit there and talk was too painful and too difficult,” he says, “but when they were working together on the computer, showing each what they had done, [the screen] provided a nice bridge for the [autistic] student to communicate a lot more.”

For her part, Price-Mitchell wants edtech companies to incorporate holistic measures like empathy in addition to the narrow numerical measures they already focus on – like the number of fraction-based math problems a child correctly solves. Without empathy, she says, kids can’t apply their mechanical skills — such as those sharpened by video games — to the “social and environmental problems of the world” in an exciting or meaningful way. 

But empathy grows from meaningful human connection and face-to-face interaction. Which isn’t to say every glance at an app is a step towards sociopathy, but both Price-Mitchell and Walschauer agree: No online elementary schools, please. 

“When we eliminate the human interaction, particularly in young people’s learning,” Price-Mitchell says, “we have to ask ourselves the question: how will it affect these children in their development of broader, much more important human characteristics like curiosity or creativity or empathy or resilience?”

Learning First, Gadgets Second

There’s at least one thing on which both Price-Mitchell and Walschauer agree when it comes to the future of elementary education: “blended learning.” A mix of in-person traditional instruction and independent work with technology, it is most effectively implemented by tech-savvy teachers who ensure that edtech serves academics and not vice versa.

“I remember one class where teachers assigned students to do a PowerPoint,” Walschauer said, “and [the students] got a grade based on how many transitions, colors and different formats they used. In other words, [the assignment] was: Create a PowerPoint from hell.”

That’s exactly the wrong way to combine education and tech, he says. The students learned about the tool itself, PowerPoint, but didn’t use it to meet their academic or developmental needs.

When it’s deployed smartly, though, edtech can meet a variety of elementary educational needs. Snapping together Scratch's blocks, for instance, teaches kids more than how to use Scratch — it teaches them modes of reasoning relevant to every programming language.

And just as she hopes edtech companies will focus more on holistic measures, Price-Mitchell also wants schools to take a more holistic approach by incorporating technology “into the other things that we know humans need, like time to reflect and discuss with their peers and teachers. Kids could also use tech to reflect on the feeling of learning, or to catalog what sparks their curiosity.” 

Despite a flood of untested edtech gadgets, however, Walschauer says there's a trend toward familiar and versatile basics like Google docs and spreadsheets — “the kinds of things that you and I use to share, to write, to do research, to share work together.” 

In edtech as in all tech, sometimes less is more. 

Great Companies Need Great People. That's Where We Come In.

College of Engineering

Georgia tech ai makerspace.

A hallway of the makerspace with servers on either side and text overlay "Georgia Tech AI Makerspace"

Using an approach unlike any other in higher education, Georgia Tech’s College of Engineering has created a digital sandbox for students to understand and use artificial intelligence in the classroom.

The AI Makerspace is a supercomputer hub that gives students access to computing resources typically available only to researchers or tech companies. It means hands-on experience for our students, deepening their skills and preparing them to be the new generation of AI professionals.

With the resources in the AI Makerspace, the College can redesign courses to incorporate practical AI tools and develop new ones that impart the essential principles of AI to all students.

The initiative is in collaboration with NVIDIA , one of the country’s largest suppliers of AI hardware and software — and a substantial investment. Students and faculty receive support through NVIDIA Deep Learning Institute resources, including faculty-run NVIDIA workshops, certifications, a university ambassador program, curriculum-aided teaching kits, and a developer community network.

The AI Makerspace also enables Georgia Tech to enhance or redesign courses to incorporate practical AI tools, along with develop new courses — both foundational and advanced — that impart the essential principles of AI to all students. The partnership between Georgia Tech and NVIDIA signifies a substantial investment. The allocated funds will be utilized for technology, including NVIDIA graphics processing units (GPUs), and infrastructure. S tudents and faculty will receive support through NVIDIA Deep Learning Institute resources, including faculty-run NVIDIA workshops, certifications, a university ambassador program, curriculum-aided teaching kits, and a developer community network.

The collaboration is part of the College’s commitment to nurturing a vibrant AI-powered university that will shape the future generation of AI professionals.

Dean Raheem Beyah looks at computer servers in the AI Makerspace

Georgia Tech Unveils New AI Makerspace

By giving students access to powerful supercomputers, Georgia Tech will teach AI to undergraduates in a way unlike any other university in the nation.

What Sets the Georgia Tech AI Makerspace Apart?

person typing on computer with graphics of AI

Educational Empowerment

In an era where AI is increasingly ingrained in our daily lives, the AI Makerspace democratizes access to heavyweight computing resources.

man working with computer equipment

Training the AI Workforce

The AI Makerspace takes a dedicated approach to workforce development through curriculum-based study as well as independent exploration. 

computer chip

National Security

Harnessing the power of AI is a strategic imperative for national security. As nations strive to secure their positions as global leaders in the field, investing in AI education is critical for U.S. competitiveness.

student and faculty member working with simulator

Interdisciplinary Focus

The AI Makerspace offers a unique opportunity for students to harness the power of AI technologies in ways that extend beyond traditional computing applications.

The Georgia Tech AI Makerspace is a dedicated computing cluster paired with NVIDIA AI Enterprise software. The software technology resides on an advanced AI infrastructure that is designed, built, and deployed by  Penguin Solutions , providing a virtual gateway to a high-performance computing environment. 

The first phase of the endeavor is powered by 20 NVIDIA HGX H100 systems, housing 160 NVIDIA H100 Tensor Core GPUs, one of the most powerful computational accelerators capable of enabling and supporting advanced AI and machine learning efforts. The system is interconnected with an NVIDIA Quantum-2 InfiniBand networking platform, featuring in-network computing. 

Infrastructure support is led by Georgia Tech’s Partnership for an Advanced Computing Environment (PACE) .

It would take a single NVIDIA H100 GPU one second to come up with a multiplication operation that would take Georgia Tech’s 50,000 students 22 years to achieve.

20 NVIDIA H100-HGX servers, each with:

  • 8 x NVIDIA H100 GPUs (SXM5 form-factor)
  • 2 x 32-Core Intel Sapphire Rapids CPUs (2.8 GHz)
  • 2TB 4800 MHz DDR5 DRAM
  • 3 x 3.84 TB NVMe storage
  • 1 x ConnectX-7 IB NIC (400 Gbps)

Total System:

  • 160 NVIDIA H100 GPUs
  • 1,280 Intel Sapphire Rapids CPU cores
  • 40TB 4800 MHz DDR5 DRAM
  • 230.4 TB NVMe storage

Frequently Asked Questions

What are gpus and cpus.

GPUs (graphics processing units) are specialized processors designed to handle certain complex computations efficiently, commonly used in tasks such as rendering high-resolution graphics and performing parallel computations in fields like machine learning and artificial intelligence. CPUs (central processing units) are the central component of a computer responsible for executing instructions, managing tasks, and coordinating the operation of various hardware components, serving as the brain of the computer.

GPUs have become prominent due to their exceptional parallel processing capabilities, which make them highly efficient for high-performance computing (HPC) tasks. Additionally, advancements in GPU technology have led to significant improvements in graphics rendering, gaming experiences, and visual computing applications, further driving their prominence in various industries and fields.

How many GPUs are in the Georgia Tech AI Makerspace and what makes them important?

Phase I of the Georgia Tech AI Makerspace comprises a total of 160 NVIDIA H100 Tensor Core GPUs. 20 NVIDIA H100-HGX servers contain 8 GPUs each. The benefit of GPUs is that they provide extremely performant accelerators designed specifically for AI, with a very large unified memory space that can accommodate very big models.

It’s also noteworthy that an important capability of AI is low-precision performance. These nodes provide roughly 640 petaflops (PF) of theoretical 8-bit floating-point for 8-bit integer (FP8/INT8) capability, combined with the 640 gigabytes of GPU memory per server.

Why are there both GPUs and CPUs in the Georgia Tech AI Makerspace? 

CPUs and GPUs are optimized for different kinds of calculations, so it’s useful to have both available. Optimized software will perform certain steps of code on the CPU and others on the GPU to maximize performance.

CPUs are “standard” general-purpose chips that work well for many calculations. GPUs are specialized. A server cannot run without a CPU. The CPU handles all the tasks required for all software on the server to run correctly. 

GPUs are accelerators with more focused computational hardware that rely on a separate host system to operate.

workers loading in GPU hardware

Who will manage the infrastructure of the AI Makerspace? 

The AI Makerspace infrastructure will be supported by Georgia Tech’s Partnership for an Advanced Computing Environment (PACE). PACE provides sustainable leading-edge Research Computing and Data (RCD) cyberinfrastructure, software, and support for research and education requiring high performance computing and other advanced research computing infrastructure. 

PACE is a collaboration between Georgia Tech faculty and the Office of Information Technology (OIT) focused on HPC.

Is the AI Makerspace scalable?

Yes. Each GPU can be physically partitioned into 7 GPUs (with 1/8 the capability of the whole). With 160 total GPUs, the AI Makerspace can provide 1,120 concurrent GPUs to allow large numbers of students access simultaneously. 

How much power does the AI Makerspace require?

The new servers will draw about 140kW of power, compared to the 800kW PACE’s five existing clusters draw.

The theoretical 64-bit performance of the new hardware is 5.5 PF (petaflops, a measurement of computer speed of performing calculations). The existing PACE clusters altogether have about 4-4.5 PF of performance. This means that the new servers are significantly more energy efficient for the same computational capability than older systems.

Related Content

student and faculty member looking at computer

Minor Degree in AI and Machine Learning Available Summer 2024

The new minor degree program is a partnership between the College of Engineering and the Ivan Allen College of Liberal Arts, teaching AI technical skills alongside ethics and policy considerations.

students talking near a robot

College Adds, Reimagines AI Courses for Undergraduates

In response to demand from its students, initiatives within faculty research, and increasing needs from industry, the College has created and reimagined more than a dozen courses to strengthen its AI and machine learning education.

Read our research on: Gun Policy | International Conflict | Election 2024

Regions & Countries

What’s it like to be a teacher in america today, public k-12 teachers are stressed about their jobs and few are optimistic about the future of education; many say poverty, absenteeism and mental health are major problems at their school.

A teacher leads an English class at a high school in Richmond, Virginia. (Parker Michels-Boyce/The Washington Post via Getty Images)

Pew Research Center conducted this study to better understand the views and experiences of public K-12 school teachers. The analysis in this report is based on an online survey of 2,531 U.S. public K-12 teachers conducted from Oct. 17 to Nov. 14, 2023. The teachers surveyed are members of RAND’s American Teacher Panel, a nationally representative panel of public K-12 school teachers recruited through MDR Education. Survey data is weighted to state and national teacher characteristics to account for differences in sampling and response to ensure they are representative of the target population.

Here are the questions used for this report , along with responses, and the survey methodology .

Low-poverty , medium-poverty and high-poverty schools are based on the percentage of students eligible for free and reduced-price lunch, as reported by the National Center for Education Statistics (less than 40%, 40%-59% and 60% or more, respectively).

Secondary schools include both middle schools and high schools.

All references to party affiliation include those who lean toward that party. Republicans include those who identify as Republicans and those who say they lean toward the Republican Party. Democrats include those who identify as Democrats and those who say they lean toward the Democratic Party.

Public K-12 schools in the United States face a host of challenges these days – from teacher shortages to the lingering effects of COVID-19 learning loss to political battles over curriculum .

A horizontal stacked bar chart showing that teachers are less satisfied with their jobs than U.S. workers overall.

In the midst of all this, teachers express low levels of satisfaction with their jobs. In fact, they’re much less satisfied than U.S. workers overall.

Here’s how public K-12 teachers are feeling about their jobs:

  • 77% say their job is frequently stressful.
  • 68% say it’s overwhelming.
  • 70% say their school is understaffed.
  • 52% say they would not advise a young person starting out today to become a teacher.

When it comes to how their students are doing in school, teachers are relatively downbeat about both academic performance and behavior.

Here’s how public K-12 teachers rate academic performance and behavior at their school:

A horizontal stacked bar chart showing that about half of teachers give students at their school low marks for academic performance and behavior.

  • 48% say the academic performance of most students at their school is fair or poor. A third say it’s good, and only 17% describe it as excellent or very good.
  • 49% say the behavior of most students at their school is fair or poor; 35% say it’s good and 13% say it’s excellent or very good.

The COVID-19 pandemic likely compounded these issues. About eight-in-ten teachers (among those who have been teaching for at least a year) say the lasting impact of the pandemic on students’ behavior, academic performance and emotional well-being has been very or somewhat negative.

Assessments of student performance and behavior differ widely by school poverty level. 1 Teachers in high-poverty schools have a much more negative outlook. But feelings of stress and dissatisfaction among teachers are fairly universal, regardless of where they teach.

Related: What Public K-12 Teachers Want Americans To Know About Teaching

A bar chart showing that most teachers see parents’ involvement as insufficient.

As they navigate these challenges, teachers don’t feel they’re getting the support or reinforcement they need from parents.

Majorities of teachers say parents are doing too little when it comes to holding their children accountable if they misbehave in school, helping them with their schoolwork and ensuring their attendance.

Teachers in high- and medium-poverty schools are more likely than those in low-poverty schools to say parents are doing too little in each of these areas.

These findings are based on a survey of 2,531 U.S. public K-12 teachers conducted Oct. 17-Nov. 14, 2023, using the RAND American Teacher Panel. 2 The survey looks at the following aspects of teachers’ experiences:

  • Teachers’ job satisfaction (Chapter 1)
  • How teachers manage their workload (Chapter 2)
  • Problems students are facing at public K-12 schools (Chapter 3)
  • Challenges in the classroom (Chapter 4)
  • Teachers’ views of parent involvement (Chapter 5)
  • Teachers’ views on the state of public K-12 education (Chapter 6)

Problems students are facing

A horizontal stacked bar chart showing that poverty, chronic absenteeism and mental health stand out as major problems at public K-12 schools.

We asked teachers about some of the challenges students at their school are facing. Three problems topped the list:

  • Poverty (53% say this is a major problem among students who attend their school)
  • Chronic absenteeism (49%)
  • Anxiety and depression (48%)

Chronic absenteeism (that is, students missing a substantial number of school days) is a particular challenge at high schools, with 61% of high school teachers saying this is a major problem where they teach. By comparison, 46% of middle school teachers and 43% of elementary school teachers say the same.

Anxiety and depression are viewed as a more serious problem at the secondary school level: 69% of high school teachers and 57% of middle school teachers say this is a major problem among their students, compared with 29% of elementary school teachers.

Fewer teachers (20%) view bullying as a major problem at their school, though the share is significantly higher among middle school teachers (34%).

A look inside the classroom

We also asked teachers how things are going in their classroom and specifically about some of the issues that may get in the way of teaching.

  • 47% of teachers say students showing little or no interest in learning is a major problem in their classroom. The share rises to 58% among high school teachers.
  • 33% say students being distracted by their cellphones is a major problem. This is particularly an issue for high school teachers, with 72% saying this is a major problem.
  • About one-in-five teachers say students getting up and walking around when they’re not supposed to and being disrespectful toward them (21% each) are major problems. Teachers in elementary and middle schools are more likely than those in high schools to see these as challenges.

A majority of teachers (68%) say they’ve experienced verbal abuse from a student – such as being yelled at or threatened. Some 21% say this happens at least a few times a month.

Physical violence is less common. Even so, 40% of teachers say a student has been violent toward them , with 9% saying this happens at least a few times a month.

About two-thirds of teachers (66%) say that the current discipline practices at their school are very or somewhat mild. Only 2% say the discipline practices at their school are very or somewhat harsh, while 31% say they are neither harsh nor mild. Most teachers (67%) say teachers themselves don’t have enough influence in determining discipline practices at their school.

Behavioral issues and mental health challenges

A bar chart showing that two-thirds of teachers in high-poverty schools say they have to address students’ behavioral issues daily.

In addition to their teaching duties, a majority of teachers (58%) say they have to address behavioral issues in their classroom every day. About three-in-ten teachers (28%) say they have to help students with mental health challenges daily.

In each of these areas, elementary and middle school teachers are more likely than those at the high school level to say they do these things on a daily basis.

And teachers in high-poverty schools are more likely than those in medium- and low-poverty schools to say they deal with these issues each day.

Cellphone policies and enforcement

A diverging bar chart showing that most high school teachers say cellphone policies are hard to enforce.

Most teachers (82%) say their school or district has policies regarding cellphone use in the classroom.

Of those, 56% say these policies are at least somewhat easy to enforce, 30% say they’re difficult to enforce, and 14% say they’re neither easy nor difficult to enforce.

Experiences with cellphone policies vary widely across school levels. High school teachers (60%) are much more likely than middle school (30%) and elementary school teachers (12%) to say the policies are difficult to enforce (among those who say their school or district has a cellphone policy).

How teachers are experiencing their jobs

Thinking about the various aspects of their jobs, teachers are most satisfied with their relationship with other teachers at their school (71% are extremely or very satisfied).

They’re least satisfied with how much they’re paid – only 15% are extremely or very satisfied with their pay, while 51% are not too or not at all satisfied.

Among teachers who don’t plan to retire or stop working this year, 29% say it’s at least somewhat likely they will look for a new job in the 2023-24 school year. Within that group, 40% say they would look for a job outside of education, 29% say they’d seek a non-teaching job in education, and only 18% say they’d look for a teaching job at another public K-12 school.

Do teachers find their work fulfilling and enjoyable?

Overall, 56% of teachers say they find their job to be fulfilling extremely often or often; 53% say their job is enjoyable. These are significantly lower than the shares who say their job is frequently stressful (77%) or overwhelming (68%).

Positive experiences are more common among newer teachers. Two-thirds of those who’ve been teaching less than six years say their work is fulfilling extremely often or often, and 62% of this group says their work is frequently enjoyable.

Teachers with longer tenures are somewhat less likely to feel this way. For example, 48% of those who’ve been teaching for six to 10 years say their work is frequently enjoyable.

Balancing the workload

Most teachers (84%) say there’s not enough time during their regular work hours to do tasks like grading, lesson planning, paperwork and answering work emails.

Among those who feel this way, 81% say simply having too much work is a major reason.

Many also point to having to spend time helping students outside the classroom, performing non-teaching duties like lunch duty, and covering other teachers’ classrooms as at least minor reasons they don’t have enough time to get all their work done.

A diverging bar chart showing that a majority of teachers say it’s difficult for them to achieve work-life balance.

A majority of teachers (54%) say it’s very or somewhat difficult for them to balance work and their personal life. About one-in-four (26%) say it’s very or somewhat easy for them to balance these things, and 20% say it’s neither easy nor difficult.

Among teachers, women are more likely than men to say work-life balance is difficult for them (57% vs. 43%). Women teachers are also more likely to say they often find their job stressful or overwhelming.

How teachers view the education system

A large majority of teachers (82%) say the overall state of public K-12 education has gotten worse in the past five years.

Pie charts showing that most teachers say public K-12 education has gotten worse over the past 5 years.

And very few are optimistic about the next five years: Only 20% of teachers say public K-12 education will be a lot or somewhat better five years from now. A narrow majority (53%) say it will be worse.

Among teachers who think things have gotten worse in recent years, majorities say the current political climate (60%) and the lasting effects of the COVID-19 pandemic (57%) are major reasons. A sizable share (46%) also point to changes in the availability of funding and resources.

Related:  About half of Americans say public K-12 education is going in the wrong direction

Which political party do teachers trust more to deal with educational challenges?

On balance, more teachers say they trust the Democratic Party than say they trust the Republican Party to do a better job handling key issues facing the K-12 education system. But three-in-ten or more across the following issues say they don’t trust either party:

  • Shaping school curriculum (42% say they trust neither party)
  • Ensuring teachers have adequate pay and benefits (35%)
  • Making schools safer (35%)
  • Ensuring adequate funding for schools (33%)
  • Ensuring all students have equal access to high-quality K-12 education (31%)

A majority of public K-12 teachers (58%) identify or lean toward the Democratic Party. This is higher than the share among the general public (47%).

  • Poverty levels are based on the percentage of students in the school who are eligible for free and reduced-price lunch. ↩
  • For details, refer to the Methodology section of the report. ↩
  • Urban, suburban and rural schools are based on the location of the school as reported by the National Center for Education Statistics (rural includes town). Definitions match those used by the U.S. Census Bureau. ↩

Social Trends Monthly Newsletter

Sign up to to receive a monthly digest of the Center's latest research on the attitudes and behaviors of Americans in key realms of daily life

Report Materials

Table of contents, ‘back to school’ means anytime from late july to after labor day, depending on where in the u.s. you live, among many u.s. children, reading for fun has become less common, federal data shows, most european students learn english in school, for u.s. teens today, summer means more schooling and less leisure time than in the past, about one-in-six u.s. teachers work second jobs – and not just in the summer, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

  • Advanced Search
  • Journal List
  • Springer Nature - PMC COVID-19 Collection

Logo of phenaturepg

Identification and evaluation of technology trends in K-12 education from 2011 to 2021

Adam kenneth dubé.

Department of Educational & Counselling Psychology, McGill University, 3700 McTavish Street, Montreal, QC H3A 1Y2 Canada

Associated Data

Source data used for this study can be found on the authors’ website.

Educational technologies have captured the attention of researchers, policy makers, and parents. Each year, considerable effort and money are invested into new technologies, hoping to find the next effective learning tool. However, technology changes rapidly and little attention is paid to the changes after they occur. This paper provides an overall picture of the changing trends in educational technology by analyzing the Horizon Reports’ predictions of the most influential educational technologies from 2011 to 2021, identifying larger trends across these yearly predictions, and by using bibliometric analysis to evaluate the accuracy of the identified trends. The results suggest that mobile and analytics technologies trended consistently across the period, there was a trend towards maker technologies and games in the early part of the decade, and emerging technologies (e.g., VR, AI) are predicted to trend in the future. Overall, the specific technologies focused on by the HRs’ predictions and by educational researchers’ publications seem to coincide with the availability of consumer grade technologies, suggesting that the marketplace and technology industry is driving trends (cf., pedagogy or theory).


Due to the perception that new technologies can facilitate and improve learning, there has been a longstanding societal push from policy makers and parents to adopt technology into education (Artym et al., 2016 ; Nevski & Siibak, 2016 ; Reiser & Ely, 1997 ; Skinner, 1954 , 1958 , 1968 ; Vanderlinde et al., 2010 ). Based on the hope that technology will improve teaching and learning, schools are investing in information and communication technology (Machin et al., 2006 ); teachers are implementing technology into classrooms (Hutchison & Woodward, 2014 ); and parents are ensuring students have internet access at home (83.9% in Canada, 73.4% in the US, OECD, 2018 ). However, the specific technology that schools, teachers, and parents are expected to adopt changes rapidly and the inclusion of new technologies can change how learning occurs in classrooms. Therefore, it is critical to understand the changing trends in educational technology and how these changes affect the role of technology in classrooms.

Technologies have many affordances in education. The interactivity of Web 2.0 was supposed to enhance student’s comprehension and interest of online information (Karvounidis et al., 2018 ); social networking may develop writing and collaboration skills (Voivonta, & Avraamidou, 2018 ); mobile devices enable anytime, anyplace learning; augmented reality increases student’s learning attitudes and learning efficiency (Teng et al., 2018 ); and digital games increase engagement and hence improve academic achievement (Kiili et al., 2014 ; Outhwaite et al., 2017 ). From these few examples, it is clear that the mass adoption of any one technology could shift the focus in a classroom. Adopting social networking into education would emphasize collaboration as being central to learning in today’s classrooms. In contrast, adopting digital games may place more of an emphasis on whether or not students are engaged as a means to increase their understanding. These different foci could affect how teachers evaluate the effectiveness of their instruction and the types of activities they have students complete (e.g., online group work vs individual game progression). Thus, a better understanding of previous and current trends in educational technology use helps paint a picture of the present and future classroom.

Literature review

Technology trend reports.

A few reports provide technological or pedagogical predictions that could be used to paint the aforementioned picture. These include Innovative Pedagogy (produced by Institution of Educational Technology), the Institute for Prospective Technological Studies Reports (made by Joint Research Centre of the European Commission), and the Horizon Reports (produced by New Media Consortium). Innovative Pedagogy is a series of reports starting from 2012 that cover some technology uses in education (e.g. learning with robots) but mainly focus on new forms of pedagogy (e.g., such as learning through wonder, student-led analytics, and intergroup empathy). The Institute for Prospective Technological Studies Reports also contain technology predictions, but the report’s main goal is to facilitate policy making from an economic perspective rather than an educational perspective. In contrast, the Horizon Report Project predicts and explores technology developments that may potentially impact education. The Horizon Reports (HRs) have continuously been issued since 2002 and have typically reached more than 500,000 downloads per year across 195 countries. Thus, the HRs are a unique source of information on technology trends in classrooms.

More specifically, the HRs are a global ongoing research report exploring technology trends and developments that are likely to have an impact on formal education. Each year, an advisory board consisting of a broad spectrum of experts in education, technology, and other fields engage in a comprehensive review and analysis of educational technologies based on current research and educational practice. The board finalizes six technologies they believe will influence K-12 teaching and learning across three periods: near-term (the year of the report), mid-term (2–3 years), and far-term (4–5 years). The HRs are argued to provide a link between current societal interest, research, educational practice, and future educational community’s mainstream technology practice. It should be noted that the potential technologies chosen in each report are selected based on the Delphi Technique. The Delphi technique is a deliberation method which involves collaborative decision making among advisory board members who ultimately come up with the final six high ranking technologies each year (Harold et al., 2011 ). Importantly, the selection of technologies in the HRs were due to their supposed popularity in research and educational practice rather than specific evidence-based benefits on teaching and learning. The goal is to identify technologies that are likely to influence education not necessarily to identify technologies that are the best learning tools, as determined by research or theory.

As mentioned earlier, technology changes rapidly and researchers, policy makers, educators, and parents should be aware of greater trends. Proper awareness allows policy makes and the public to spend their educational capital more wisely by avoiding the adoption of devices that are unlikely to persist (e.g., Mobile VR; Robertson, 2019 ). Awareness also allows researchers to better understand those technologies that are likely to impact a broad range of classrooms rather than those adopted by the few techno-enthusiasts. There is some previous work that has tried to identify technology trends in education, but the predictions in these works are now outdated and were never validated. Ely did a content analysis of journals, dissertations, conferences, and documents from ERIC and other sources and highlighted eight technology trends from 1988 to 1995, which included televisions, desktop computing, and early networking ( 1996 ). In a more recent report, Bonk ( 2009 ) stated that web-based technology was changing education by generating new forms of learning and listed ten trends: e-books, blended e-learning, open sources, learning objects, e-collaboration, mobile learning, and personalized learning. Importantly, these studies only described technologies that the authors thought were most likely to be adopted in the near future but there was no examination of whether the predictions came to pass.

Martin et al.’s ( 2011 ) work is the first and the only study to identify trends in educational technology in K-12 education and also evaluate the accuracy of the predicted trends. Marin et al. ( 2011 ) provided an evaluation of the most important technology trends in K-12 education across 2004 to 2010 by comparing the technology adoption rates predicted by the Horizon Reports with published articles in Google Scholar using bibliometric analysis. Specifically, they collated the six technologies predicted by each yearly report, clustered these individual predictions into larger trends, and then looked at whether the trends were supported by an increased level of scholarly discourse (i.e., publications). They originally conducted a market survey to see if technology purchasing rates correlated with the predictions but found that the buying power of the education sector was insufficient to affect greater societal buying trends. Alternatively, Martin et al. ( 2018 ) attempted to use Relative Search Volume (i.e., the number of times a term is searched in google) as a metric of societal impact in addition to bibliometric analysis but found this approach to be uninformative when they narrowed down the search to education related results. Outside Martin’s work, several studies have argued in support of the use of bibliometrics analysis as a means to evaluate the effect of emerging technologies (Daim et al., 2006 ; Han & Shin, 2014 ; Huang et al., 2014 ; Morom et al., 2018 ; Stelzer et al., 2015 ; Yeo et al., 2015 ). While bibliometrics does not directly reflect the use of technologies in society, it does provide insights into which technologies researchers believe are affecting society and the analysis can help guide future studies using more direct measures.

The goal was to see if the predicted technologies actually influenced education. The study concluded that the social web and mobile devices held the most influence on education and predicted that video games would have a bigger impact after 2010. Since Martin et al. ( 2011 ) original work, they used a similar methodology and evaluated the technology trends in higher education from 2010 to 2015 (Martin et al., 2018 ). No recent study has identified and evaluated the educational technology trends in K-12 after 2010 using the HRs. Further, Marten et al.’s work somewhat ignored an assumption underlying the HRs about the connection between society and education, that broader societal trends in research and practice determine the educational community’s mainstream technology usage. To address this gap, we use the same methodology to provide an updated overview of educational technology trends in K-12 education from 2011 to 2021 by collating the yearly predictions from the 2011 to 2017 HRs, identifying larger trends across these yearly predictions, and using bibliometric analysis to evaluate the accuracy of the identified trends.

The following research questions guided the work:

Research questions

Data sources.

Martin et al.’s ( 2011 ) work suggests that the HRs can be used as a basis for analyzing the influence of technology on education. Therefore, we chose the Horizon Reports on primary and secondary education from 2011 to 2017 to be used as the sole source of technology predictions for this analysis. To test the accuracy of Horizon Report’s prediction, Google Scholar was chosen as the bibliometric database due to it providing metadata of scholarly literature across disciplines and it connecting repositories of articles stored worldwide. Further, Google Scholar is considered to provide a broader coverage of publications as compared to Scopus and Web of Science (Bergman, 2012 ; Harzing, 2010 ).

This paper adopted Martin et al.’s ( 2011 ) methodology but with the latest HRs from 2011 to 2017. The methodology involved the following stages:

  • Seven Horizon Reports were gathered from 2011 to 2017 and the six technologies predicted in each report were recorded according to their time frame (near, mid, far).
  • Based on the records, a visual representation of the HRs’ predictions was made. These visualisations use different colors to differentiate the technologies from each report and provide a clear picture of all the technologies predicted across the seven reports.
  • Similar technologies across all the reports were grouped into clusters and visual representations (same method as mentioned in step 2) were created for each cluster. These clusters are used in the subsequent bibliometric analysis.
  • Using the newly created clusters, the evolution of educational technologies across 2011–2017 were analyzed and discussed for each group.
  • Keyword selection. Technology related keywords were generated based on the clusters identified in stage 3. The technology specific keywords were derived from the technologies predicted by the HRs. Taking ‘mobile technology’ for example, the technologies identified in this cluster by the HRs were mobile, tablet, App, Bring Your Own Devices, and wearable technology. These specific technologies and their derivatives were entered as keywords in sequential searches (e.g., the technology ‘App’ included searches using the keywords Application, App, Apps) along with keywords representing schools (e.g., classroom, school). To limit redundancy, keywords used in a previous search were entered as an exclusion criteria in subsequent searches (e.g., search 1 = ‘App’, search 2 = ‘Applications’, -‘App’). Thus, the keywords used in the search were based on the specific technologies mentioned by the reports.
  • Year of publication. The number of publications for each keyword in each year from 2011 to 2018 was obtained, as well as the total number of occurrences across all years.
  • Title search. To limit the search to education publications, the words “learning” or “education” have to appear in the title together with the cluster keywords.
  • Result confirmation. Each individual search was conducted three times, across separate days and computers, to ensure that Google Scholar was returning consistent metrics.

p ¯ the mean of papers published in education from 2010 to 2018, p i  = the number of papers published in year i, i = the year from 2010 to 2018, 2010 , 2011 , 2012 … 2018 , N = total number of years.

  • To assess the accuracy of the HRs predictions, the trends predicted by HRs were compared to their weighted impact from step 5-d. Note* technologies predicted to trend in a given year would likely have a delayed impact on publications (i.e., predicted 2011, publications increase 2012).

Visual representation of predicted technologies

A visual representation of the technology predictions from step 1 and 2 can been seen in Fig.  1 . The vertical line depicts the year of prediction (year the HR was released), and the horizontal line depicts the year(s) in which technologies were predicted to have an impact.

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10689_Fig1_HTML.jpg

Technologies predicted to impact education according to the Horizon Reports from 2011 to 2017

Technology clusters and trends

Following the approach by Martin et al. ( 2011 ), a visual analysis of Fig.  1 and a thematic analysis of the specific predictions made in the HRs were used to identify seven clusters of technology predictions and named them according to their common theme: mobile technology, maker technology, analytics technology, games, simulation technology, artificial intelligence (AI), and other technologies. The thematic analysis involved reading the HRs explanation for each prediction (e.g., mobile, Apps, BYOD) and identifying commonalities based on the type of technology involved (i.e., portable, personal computing devices). The themed clusters accounted for 30 of the total 42 predictions (71%) made by the HRs from 2011 to 2017. The ‘other’ cluster contains predictions that did not clearly cluster around a specific technology (e.g., cloud computing, open content, internet of things, natural user interface, digital badges, online learning, personal learning environments). In the following section, the seven clusters will be expanded upon by identifying how these technologies are proposed to affect education according to both the HRs (i.e., prediction justifications) and recent reviews of educational technology research. Understanding why each prediction was made by the HRs will also aid in the later evaluation of their prediction accuracy.

Mobile technology

This cluster (see Fig.  2 ) included every technology and practice related to mobile learning technologies in the HRs’ predictions, such as mobile devices, Apps, tablet computing, bring your own device (BYOD), and wearable technology.

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10689_Fig2_HTML.jpg

Mobile technologies predicted to impact education according to the Horizon Reports from 2011 to 2017

The 2011 HR forecasted the importance of mobile on teaching and learning signifying a shift in how students and educators connect to the internet, from computers to mobile devices. Especially when tablets began to join the family of mobile technology, enabling the immediate and easy access to thousands of Apps all at once. The seamless access to the third-party applications is proposed to open the door to multiple resources for education (McEwen & Dubé, 2017 ). The 2012 HR also predicted mobile devices and Apps to be influential on education since mobile devices were reported to be one of the most common ways for youth to access educational software (Hirsh-Pasek et al., 2015 ). Meanwhile, teachers started to use Apps in their classrooms as supplementary tools to engage students with complex learning content (e.g., Zhang et al., 2015 ). In the same year’s report, tablets were separately predicted and emphasized to have impact on education, due to their larger screens and a richer range of gestures that may provide a more hands-on learning experience (Dubé & McEwen, 2015 , 2017 ). The 2013 HR gave attention to mobile learning again, as it was widely adopted in school’s one-to-one learning initiatives and educational Apps became the second most downloaded category in the Apple App Store (Shuler, 2012 ). Similarly, the 2014 and 2015 HRs forecasted a new form of mobile learning—Bring Your Own Device (BYOD). BYOD is argued to facilitate student-centered learning and provided a more seamless learning experience between learning at home with the device and learning in the classroom with the same tool (e.g., McLean, 2016 ).

The 2014, 2015 and 2016 HR all predicted that wearable technology (e.g., smart watches, fitness bands) would be increasingly adopted in daily-life and education. However, the application of wearable technology to education was still emerging and was predicted to produce an impact on learning in the far-term. During this time, researchers were similarly predicting that wearables would be increasingly adopted into education, with a focus on their use as collaborative fieldwork tools in STEM subjects (e.g., taking pictures with Google Glass while collecting field samples in a biology course, see Sapargalivev, 2015 for a review of uses).

To sum up, in the HRs, mobile technology was continuously predicted to have an impact on learning. From 2011 to 2015, mobiles, tablets, Apps, and BYOD were predicted to have impact in near term (one year or less). From year 2014 to 2016, wearable technology was forecasted to have a far-term impact (4 to 5 years). All six reports (2011–2016) emphasized the importance of mobile technology from 2011 to 2021 and predicted a change in focus from mobile, Apps, tablets, and BYOD to wearable technology. The shift in focus may reflect the waning impact of currently pervasive mobile devices and tablets and the increasing impact of emerging wearable technologies, which were relatively new at a societal level.

Maker technology

The Maker movement and associated technologies (see Fig.  3 ) aim to promote authentic learning through hands-on design and construction (Loy, 2014 ). The specific maker technologies predicted by the HRs consist of 3D printing, robotics, and makerspaces.

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10689_Fig3_HTML.jpg

Maker technologies predicted to impact education according to the Horizon Reports from 2011 to 2017

3D printing was forecasted to be influential on teaching and learning in both the 2013 and 2015 HRs; Due to the high cost and teacher training needed for inclusion in classrooms, 3D printing was only predicted to have an impact on education in the mid- and far-term. These critiques of 3D printing in education are echoed by both researchers and educators (e.g., Eisenberg, 2013 ; Turner et al., 2017 ), and have since been somewhat mitigated by the development of free, child-friendly 3D printing software and tutorials (e.g.,

The robotics industry witnessed a significant growth in this decade (Ford, 2015 ; Ross, 2016 ) and were predicted in both the 2016 and 2017’s HRs to have an impact on education in the mid-to-far-term. Robotics were generally predicted to have a positive influence on the development of children’s twenty-first century skills. Contemporaneous reviews of research on robotics in education (Toh et al., 2016 ) suggests that students building and reasoning about robotics is said to contribute to problem-solving, collaboration, overall school achievement, STEM skills, and language ability (due to coding), and produce more participation from both students and parents in school activities through after-school workshops.

Makerspaces are a created workshop environment for learners to collective practice hand-on construction with technologies and to share resources and knowledge (Fourie & Meyer, 2015 ). Makerspaces appear in the 2015, 2016, and 2017 HRs all with predictions of near-term impacts, as makerspaces gained considerable attention worldwide. The makerspace movement can be considered as central to both the 3D printing and robotics movement, but is sometimes deemed technology agnostic (i.e., can build with Legos or robots). The original movement was focused on sharing knowledge and resources in a joint workspace whereas the later educational makerspace movement aims to promote learning through building (e.g., constructionism, Papert, 1980 ) and to promote the 4Cs of twenty-first century skills (i.e., critical thinking, collaboration, creativity, and communication, Fourie & Meyer, 2015 ).

In total, four HRs emphasized the impacts of maker technology on learning from 2015 to 2018. The predicted impact of maker technology gradually moved from long-term predictions, to mid-term, and then near-term, which suggests an adoption of this technology into education across the period. Of note, this pattern of prediction coincides with the increased availability and quality of consumer-grade maker technologies across this period (Li et al., 2017 ).

Analytics technology

Analytics technology or learning analytics (see Fig.  4 ) uses individualized data to provide adaptive instruction and assessment tailored to each student’s needs (Yu & Jo, 2014 ). Learning analytics is argued to improve existing assessment practices by providing continuous, formative assessment that can be used to both identify a learner’s strengths and weaknesses and subsequently adapt instruction (Johnson et al., 2011 ).

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10689_Fig4_HTML.jpg

Analytics technologies predicted to impact education according to the Horizon Reports from 2011 to 2017

In total, five HRs predicted that analytics technology would influence education from 2014 to 2019. Though continuously predicted to be important, the impact was always predicted in the future and never moves to the near-term. This might occur because the technology was first applied in higher education, primarily on at-risk students (Johnson et al., 2011 ), and the application to elementary education was deemed more difficult. The implementation in K-12 settings has been stymied due to the inherently qualitative nature of elementary assessment that is not amenable to the big data approach needed for learning analytics (cf., university grading systems, Zhang et al., 2018 ). By the 2017 HR, the authors noted that the delayed impact of learning analytics on K-12 education could partially be caused by the enterprise market driving investment in analytics technologies and causing the development of technologies that meet the needs of enterprise and not education. The noted exception being the development of learning dashboards that track and visualize student performance. The growing interest in learning analytics coincides with the larger societal interest in ‘big-data’ and its uses across business and public policy (e.g., Kim, 2017 ; McGregor et al., 2013 ).

Gaming technologies (see Fig.  5 ) focus on how digital games can be used to facilitate learning, such as game-based learning and gamification. Game-based learning involves the creation of educational experiences in which content knowledge or procedures are imbedded into the mechanics of the game such that playing the game and learning occur simultaneously (Dubé & Keenan, 2016 ). Gamification involves incorporating reward and leveling systems from video games into traditional academic tasks (e.g., complete math problems and receive a star, for reviews of games in education see Landers, 2014 ; Plass et al., 2020 ; Young et al., 2012 ).

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10689_Fig5_HTML.jpg

Game technologies predicted to impact education according to the Horizon Reports from 2011 to 2017

In the 2011 HR, game-based learning was predicted to affect education due to schools integrating online games into classrooms. Online learning games provide free access to educational software that previously required download and installation. The 2012 HR again focused attention on games citing that serious games helped students engage with learning content (Boyle, 2016 ); role-playing games offered students the opportunity to see the world from a different perspective (Annetta et al., 2009 ); online social games developed student’s communication and collaboration skills (Paraskeva et al., 2010 ); and game-designing classes fostered learners to creatively construct knowledge (Games, 2010 ). The 2014 HR specifically highlighted the importance of gamification and discussed how game-like elements could be applied to daily learning and produce a more engaging and motivating classroom experience.

From 2004 to 2017, the HRs made more predication about gaming than any other educational technology. Gaming appeared in six of the seven HRs from 2004 to 2010 (except 2009, Martin et al., 2011 ) and appeared in three of the seven reports from 2011 to 2017. As such, games were predicted to have an impact almost every year from 2006 to 2014. Across all of the reports, most of the predictions were mid-term impacts. These repeated predictions suggest a sustained interest in games but with an impact that was perpetually two to three years away. Overall, games show promise as learning tools and have captured steady attention from the HRs, but the continuous mid-term predictions suggest that it is taking more time to implement games in the classroom than earlier HRs foresaw.

Simulation technologies

Simulation technologies (see Fig.  6 ) provide an immersive and interactive learning environment for learners by placing them in virtual reality (VR) or by blending virtual data or visualizations into the real world using augmented reality (AR).

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10689_Fig6_HTML.jpg

Simulation technologies predicted to impact education according to the Horizon Reports from 2011 to 2017

In the 2012 HR, AR was forecasted to have a long-term effect on education by 2016 with mention of how advancements in both the Apple iOS and Android operating systems were allowing for augmented reality applications to be developed for mobile. The report also mentions how augmented reality will move beyond mobile with the announcement of Google’s ‘Project Glass’, an AR system that provided a heads-up display in the user’s line-of-sight. In the 2013 HR, virtual and remote laboratories (e.g., virtual frog dissection) were predicted to have a long-term effect on secondary education by 2017. In the 2016 HR, VR was forecasted to permeate the mainstream of K-12 education in the mid-term citing the recent successful application of VR to other areas (e.g., entertainment) and the availability of affordable mobile VR (e.g., Google Cardboard). The 2016 HR highlighted the potential benefits of simulation in education, specifically the ability for lower income schools to create virtual science labs and go on virtual fieldtrips. In the 2017 HR, mobile VR was again cited as contributing to interest in the technology along with a financial prediction by Goldman Sachs stating that the VR industry would ‘reach 15 million learners by 2025’ (Freeman et al., 2017 , p. 46). However, the HR did note that the impact of VR would be in mid-to-far-term due to time required to develop educational software for the mobile VR market. The 2017 HR report highlighted the potential to foster other soft-skills like collaboration, language development, and empathy. The two most recent HRs predicted virtual reality to have an impact across 2017 to 2019 and this prediction coincided with the development of more affordable consumer grade VR systems (e.g., Oculus Rift, Playstation VR).

The HRs’ focus on the potential of VR to simulate learning environments and support soft-skill development was supported by early reviews of VR education research (see Hew & Cheung, 2010 ). The potential of consumer-grade mobile VR systems to foster educational use has also been cited by recent researchers (see reviews by Jensen & Konradsen, 2018 ; Kavanagh et al., 2017 ). These reviews concluded that the head-mounted displays used in these consumer grade systems can engage students (e.g., Loup et al., 2016 ), improve spatial reasoning (e.g., Rasheed et al., 2015 ), and train emotional responses to adverse situations (Anderson et al., 2013 ); but HMDs could also distract from learning due to the frequent occurrence of both motion sickness (e.g., Madrigal et al., 2016 ) and technological problems using the devices in an educational setting (e.g., space requirements).

Artificial intelligence

AI technologies (see Fig.  7 ) support live adaptive learning with tailored content (cf., analytics-based lesson planning based on historical records).

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10689_Fig7_HTML.jpg

Artificial intelligence predicted to impact education according to the Horizon Reports from 2011 to 2017

The 2016 HR predicted that AI would have an impact on education in the far-term. The report cited an influential milestone in the AI field that occurred in March of 2016, when Google’s AI program AlphaGo defeated the world Go champion. Following this event, both the 2016 and 2017 HRs predicted AI to be influential in the Far-Term. The 2016 HR identified the existence of imbedded AI that students already use but are not aware of (e.g., Digital assistants like Siri, Google Search) as current influences of AI on education and the existence of Chatbots that interact with learners to facilitate second-language acquisition (e.g., Duolingo). The 2017 HRs placed greater emphasis on the potential of AI to perform ‘administrative’ tasks like grading as to allow teachers more time for individualized instruction. Roll and Wylie’s ( 2016 ) review of AI education research proposes that AI is developing along two co-existing tracks in education. One track is enhancing current practices (e.g., cognitive tutoring systems combining AI technology and curriculum) whereas the other track is redefining educational practice (e.g., AI driven formative assessment via consistent feedback). Both the HRs and researchers argue for these potentials but acknowledge that these changes are not likely to occur anytime soon.

Other technologies

Several individual technologies that did not meaningfully cluster together were predicted by the horizon report, many of the them repeatedly (see Fig.  8 ). Technologies or practices that received multiple predictions include cloud computing (e.g., Google Classrooms) with near-term predictions in the 2011, 2013, and 2014 HRs; open content (e.g., Kahn Academy) with mid-term predictions in the 2011 and 2013 HRs; internet of things (e.g., Smart Televisions) with far-term predictions in the 2014 and 2017 HRs; and personal learning environments (i.e., technologies and practices that enable and foster self-directed learning) with a far-term prediction in the 2011 HR and a mid-term prediction in the 2012 HR. Technologies or practices that received a single prediction include far-term predictions for natural user interfaces in the 2012 HR and digital badges in the 2015 HR and a near-term prediction for online learning in the 2016 HR.

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10689_Fig8_HTML.jpg

Other technologies predicted to impact education according to the Horizon Reports from 2011 to 2017

Bibliometric analysis

In the previous section, a brief discussion of the HRs predictions alongside educational researchers’ interests in these different technologies shows some alignment between the HRs and the educational technology field at large. To further evaluate the accuracy of the HRs predictions, a bibliometric analysis was conducted based on step 5. Table ​ Table1 1 shows the total number of educational publications available in Google Scholar from 2011 to 2018 along with their weighting factor (WFi), as calculated using the same equation as Martin et al. ( 2011 ) and explained in step 5. Table ​ Table2 2 shows the total number of publications and the weighted number of publications available for each of the analyzed years in each technology cluster.

The number of educational papers available in Google Scholar from 2011 to 2018 and their corresponding weighting factor

The raw and weighted number of educational papers available in Google Scholar from 2011 to 2018

As in Martin et al. ( 2011 ), the results from Table ​ Table2 2 are graphically represented in Fig.  9 and depict the publishing evolution for each technology cluster. Figures  10 , ​ ,11, 11 , ​ ,12, 12 , ​ ,13, 13 , ​ ,14, 14 , and ​ and15 15 show the weighted number of publications for each individual cluster and provide detailed information on the contribution of each specific technology within the cluster (e.g., contribution of tablets to the total number of mobile publications). The following section will discuss the accuracy of the HRs predictions for each technology cluster.

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10689_Fig9_HTML.jpg

The weighted number of publications in Google Scholar by technology cluster from 2011 to 2018

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10689_Fig10_HTML.jpg

The weighted number of publications in Google Scholar for the mobile technology cluster from 2011 to 2018

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10689_Fig11_HTML.jpg

The weighted number of publications in Google Scholar for the maker technology cluster from 2011 to 2018

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10689_Fig12_HTML.jpg

The weighted number of publications in Google Scholar for the analytics technology cluster from 2011 to 2018

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10689_Fig13_HTML.jpg

The weighted number of publications in Google Scholar for the games cluster from 2011 to 2018

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10689_Fig14_HTML.jpg

The weighted number of publications in Google Scholar for the simulation technology cluster from 2011 to 2018

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10689_Fig15_HTML.jpg

The weighted number of publications in Google Scholar for other technology cluster from 2011 to 2018

The HRs from 2011 to 2017 contained a total of 42 predictions (6 per report, 7 reports) and mobile technologies accounted for the largest percentage of overall predictions (i.e., 9 predictions or 21%). The bibliometric results revealed that compared to other themed clusters, mobile technology had the biggest impact on educational research from 2011 to 2018 (see Fig.  10 ). Both the raw and weighted number of mobile technology publications in Google Scholar increased steadily across 2011 to 2018. with a 269% increase in the weighted number of publications across the period. This pattern matches the HRs’ short-term and long-term predictions for mobile technologies, especially when considering the specific technologies within the cluster. Figure  11 shows the proportion of publications for each technology in the mobile cluster. Mobile technology was the most published topic in this group, followed by tablets, Apps, wearables, and BYOD. The number of articles on APPS shows an increased focus on this aspect of mobile technology that is not predicted by the HRs. However, the increased number of articles on wearables in 2017 and 2018 corresponds well with the HRs’ predictions.

Maker technologies accounted for the second largest percentage of overall predictions (i.e., 7 or 16%) but had the fifth highest level of publications (see Fig.  8 ). Both the raw and weighted number of publications increased across 2011 to 2018, with a 408% increase in the number of weighted publications. Within the maker technology cluster (see Fig.  11 ), robotics had the highest number of publications followed by 3D printing and makerspaces. Robotics largely accounted for the considerable growth in the cluster, despite it only receiving two of the seven predictions. The HRs predicted both 3D printing and makerspaces to have an impact starting in 2015 and this is somewhat reflected by an increased number of publications in that year. Given the discrepancy between the number of predications and publications, it seems that the HRs overemphasized the impact of maker technology on education overall and underpredicted the relative contribution of robotics to the maker movement.

Analytics technologies accounted for the third largest percentage of overall predictions (i.e., 5 or 12%) and had the fourth highest level of publications (see Fig.  8 ). Both the raw and weighted number of publications grew across 2011 to 2018, with a 1130% increase in weighted publications across the period. The HRs predicted an increased impact starting in 2015 and this is reflected by the 191% increase in weighted publications across 2012 to 2014 but a 250% growth across 2015 to 2018 (see Fig.  12 ). Within this cluster, the majority of publications are on learning analytics (cf., adaptive learning technology) and this too aligns with the HRs’ emphasis.

Gaming technologies accounted for the fifth largest percentage of overall predictions (i.e., 3 or 7%) but had the second largest impact on educational publications (see Fig.  10 ). Both the raw and weighted number of game publications increased steadily across 2011 to 2018, with a 255% increase in the weighted number of publications across the period. Within the games cluster (see Fig.  13 ), there were far more articles on games than the more specific gamification or game-based learning topics, but interest in gamification rose notably across the period by 2687%. Despite the HRs not predicting a major impact of games on education past 2015, the growth rate of game publications actually increased in this period. Overall, the data suggests that the HRs grossly underestimated the continued impact of games on education during this period.

Simulation technologies accounted for the fourth largest percentage of overall predictions (i.e., 4 or 9%) and had the third highest number of publications (see Fig.  10 ). Both the number of actual and weighted articles decreased from 2011 to 2012 but then steadily increased from 2013 to 2018, with a 554% increase in the weighted number of publications across the period. This pattern reflects the HR predictions, in that no predictions were made prior to 2012. Figure  14 shows the proportion of publications for each technology in the simulation cluster. Augmented reality generated the most publications followed closely by virtual reality, with virtual and remote laboratories in a distant third. This aligns with the HRs in that AR was predicted to have an effect on education earlier than VR and that VR’s effect on education was predicted to occur starting in 2018, which is when the number of VR articles matched the number of AR articles.

AI and other technologies

Artificial intelligence accounted for the lowest percentage of predictions from any of the clusters (i.e., 2 or 5%) and had the lowest number of publications (see Fig.  9 ). Both the raw and weighted number of publications decreased from 2011 to 2014 by 45% (weighted) but then increased from 2015 to 2018 by 841% (weighted). This increase is reflected in both the 2016 and 2017 HRs including AI in their far-term predictions. Given that only one technology populated the AI cluster, a figure of its individual publications is not included. Figure  15 shows the proportion of publications for each technology in the cluster ‘other’. Online learning consistently generated the highest number of publications across 2011 to 2018, such that it accounted for 84% of all publications in 2018 and is responsible for the other cluster ranking near the games and mobile technology cluster. This high level of impact is not reflected by the HRs, which only made one, rather general, near-term prediction for online learning in the 2016 HRs. In contrast, cloud computing and internet of things were the subject of more HR predictions but generated far fewer publications. Yet, the HRs predictions that cloud computing would have an early impact while internet of things would have a later impact is somewhat supported by the bibliometric results.

HR predications: accuracy and limitations

The preceding bibliometric analysis highlights how the HRs predictions are not always successful. To further illustrate this and to facilitate comparison between the present HRs’ predictions and the ones from Martin et al. ( 2011 ), Table ​ Table3 3 categorizes individual predictions across both studies according to their accuracy. The categorization reflects the accuracy evaluations made in the discussions by Martin et al. ( 2011 ) and in the preceding results. Martin et al. concluded that 37% of the individual HRs’ predictions were accurately predicted or slightly delayed whereas 41% of HRs’ predictions were deemed accurate or delayed in the present study. In both studies, a considerable number of individual predictions were deemed overestimations. These results further support the importance of evaluating the HRs’ predictions using bibliometric analysis and not just accepting them as pure reflections of actual technology trends.

HRs prediction accuracy across Martin et al. ( 2011 ) and the current study

Despite the evaluative value bibliometric analysis provides, using the number of publications on a given educational technology is not a perfect indicator of that technology’s influence on actual educational practice and is an imperfect substitute for directly observing technology use in classrooms. However, more direct data on educational technology adoption (e.g., school technology purchase rates) is largely not obtainable or limited to specific geographic regions. Further, studying broader technology adoption rates (e.g., overall purchase rates of tablets) runs the risk of assuming technology trends outside of schools are mirrored within them. This being said, the result of the bibliometric analysis should not be interpreted as directly reflecting the impact any one educational technology has on practice. Further, the extant body of educational technology research is often criticized for focusing on what is emerging (cf., pervasive) and on English speaking, developed nations. A similar critique can be made of the HRs themselves. As such, the present results and discussions should be interpreted with this limitation in mind.

These results identify the K-12 educational technology trends predicted by the HRs from 2011 to 2021 and evaluate the accuracy of these predictions against the number of academic publications on these technologies. The HRs are an influential document with 500,000 downloads per year across 195 countries that are the product of deliberations among technology and education experts on how they see the future of educational technologies developing. Should teacher training and technology purchases be informed by the HRs? That is a difficult question to answer, but evaluating these reports provides a useful calibration for the numerous policy makers and educators who use them. Further, the HRs predictions are only a description of future potentials (i.e., models) and evaluating which predictions come to pass provides information on both what has occurred and on the prediction process itself.

Over seven-years of forecasts, the reports predicted that mobile technology would be the most influential educational technology from 2011 into the near future. Given that mobile technologies were the most impactful in the HRs from 2002 to 2010, this further reinforces the influence of mobile devices on education. Maker technology and games were predicted to impact education from 2015 to 2018 and 2012 to 2016, respectively. Analytics technologies’ impact was predicted to increase and would continue to influence learning along with other emerging technologies like VR and AI. Thus, the HRs predictions continue to highlight both pervasive (mobile) and emerging technologies (VR, AI, Maker) while recognizing the social webs’ declining influence on education.

The bibliometric analysis suggests that the HRs’ accurately predicted the most influential educational technology (i.e., mobile) and was fairly accurate for the fourth most influential technology (i.e., analytics technology). Predictions for maker technologies (i.e., 3D printing and robotics) were somewhat overstated and placed too great an emphasis on 3D printing and maker spaces over robotics. In contrast, the HRs’ predictions around games were far too conservative but did accurately foresee an increased interest in gamification. Thus, the prediction accuracy of the HRs was mixed. Some of these mixed results could be due to a fundamental assumption underlying the HRs; that the future of educational technology depends on larger societal trends. However, this assumption fails to consider the pedagogical value of a given educational technology and, perhaps more importantly, the additional barriers that prevent technologies from being adopted into K-12 classrooms. Mobile technologies are ubiquitous in society and are increasingly affordable. As such, it makes sense that the horizon reports accurately predicted their impact on education. In contrast, maker technologies are receiving a lot of attention at a societal level (e.g., news stories, featured in popular TV shows like Grey’s Anatomy) but they require considerable training to use and are relatively expensive to purchase and maintain. This may reflect how the HRs may ‘listen’ to popular discourse around technology more so than practitioners’ concerns. While evaluating the pedagogical merit and impact of each technology identified in this study would be beyond the scope of the present endeavor, Table ​ Table4 4 in Appendix contains a listing of recent systematic reviews for each technology cluster along with a brief overview for each paper. Having identified the technologies predicted to trend across 2011–2021, these systematic reviews will help evaluate their supposed merits and impact.

Systematic reviews for each technology cluster

The tendency for educational technology adoption to follow societal factors is not limited to the HRs’ predictions. For example, both the year of prediction and the publication rates for emerging technologies seem to coincide with availability of the technology at a consumer level (i.e., affordable). Consumer level maker and VR technologies became available the same year they were included in the HRs and their publications rates increased in the two years following their commercial availability. This suggests that both predicted and actual trends in educational technologies are driven more by their availability than their educational affordances and exemplifies the longstanding criticism of the educational technology field as placing an overemphasis on ‘stuff’ (i.e., devices) at cost to pedagogical practice and theory building (Richey, 2008 ). Finally, the COVID-19 pandemic (which occurred during the revision of this paper) brings to light another factor affecting the educational technology industry, historical events and societal shifts. Predictions are based on the assumption that past and current behavior's determine future ones, but they cannot take into account unforeseen events (e.g., a global pandemic that moves education online). While the pandemic and the rise of online learning are an extreme example, more minor ones include societal shifts to and away from technologies for reasons unrelated to education (e.g., current disillusion with social media).

Allowing industry to direct educators and researchers’ gaze towards specific technologies is particularly problematic considering that many technology companies are just as quick to invest as to divest in a given technology. For example, Google entered the mobile VR market in October of 2017 with the affordable Daydream headset but abandoned the product line entirely in October of 2019 (Robertson, 2019 ). Researchers or educators turning to mobile VR because of Google’s investment would be left with devices that are now wholly unsupported. Thus, the trends identified in this study indicate a worrisome practice of researchers and educators following the investment whims of technology companies (i.e., a marketplace effect) but arguably being less able to course correct as quickly as the companies they follow. Interestingly, this marketplace effect on the HRs was not identified in the previous works by Martin et al. ( 2011 , 2018 ). A lesson to be learned from this, for prognosticators, users, and researchers of educational technology, is to be less swayed by technologies that are cheap and available today (VR, 3D printing) and more focused on technologies that show signs of permanence (e.g., mobile).

The bibliometric analyses indicate that educational technology continues to be a growing field and topic within the greater educational research discourse and whether or not this growing interest is a net positive for education is up for debate. Both the actual and weighted number of publications on educational technology increased from 2011 to 2018, representing an approximately 300% increase in the amount of researcher discourse on educational technology across the period. While an increased interest in educational technology is warranted, given the influence of technology on society generally across this period, it does raise questions about the impact this increased level of research discourse will have on students. Tawfik et al. ( 2016 ) discussion on the consequences of technology in education made a strong case that an unmindful adoption of technology runs the risk of unintentionally increasing societal inequities in the classroom. Thus, the meteoric increase in educational technology discourse seen here could benefit students but only if the discussion considers who is included and who is excluded. For example, the largest trend in terms of predictions and research discourse was for mobile technologies. Much of this discourse within this trend assumes that students not only have a device (i.e., BYOD) but that they can access the internet on the device outside of school (i.e., anywhere learning). Discussions about the impact of mobile technologies on education thus run the risk of excluding or ignoring students who do not have devices or unlimited mobile internet access. Similar issues of equality of access likely exist for many of the technologies identified in this study and future works should use the approach forwarded by Tawfik et al. ( 2016 ) to critically examine each of the major trends identified herein.


This work provides an updated picture of K-12 educational technology trends in the past and near future by collating individual technologies predictions across seven Horizon Reports, identifying larger trends from these individual predictions, and evaluating the prediction accuracy using bibliometrics. The previous trend analysis by Martin et al. ( 2011 ) identified 7 technologies believed to affect educational practice from 2004 to 2010; including the social web, mobile, games, semantic web, human computer interaction, learning objects, and augmented reality (in order of impact). The present work identifies 6 technologies believed to affect education practice from 2011 to 2017; including mobile, games, analytics technologies, simulation technology, maker technology, and AI (in order of impact). A direct comparison between the two studies shows a deemphasis on social networks as an emerging educational technology, a continued influence of both mobile and game technologies, and an emerging influence of learning analytics and AI. Looking at both studies also highlights the importance of not relying on any one year of HR predictions but rather the long-term trends that arise from multiple reports, as reports in individual years are overly swayed by the availability of new technologies. Taken together, the present study and Marten et al.’s study provide a continuous tracking of major educational technology trends from 2004 to 2021, which can serve as a state of the field for researchers, policy makers, and educators interested in how technology has and continues to influence educational practice in the twenty-first century.

See Table ​ Table4 4 .

No funding was used in support of this work.

Data availability


There is no potential conflict of interest in the working being described here.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Adam Kenneth Dubé, Email: [email protected] .

Run Wen, Email: [email protected] .

  • Anderson PL, Price M, Edwards SM, Obasaju MA, Schmertz SK, Zimand E, Calamaras MR. Virtual reality exposure therapy for social anxiety disorder: A randomized controlled trial. Journal of Consulting and Clinical Psychology. 2013; 81 (5):751. doi: 10.1037/a0033559. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Annetta LA, Minogue J, Holmes SY, Cheng M-T. Investigating the impact of video games on high school students’ engagement and learning about genetics. Computers & Education. 2009; 53 :74–85. doi: 10.1016/j.compedu.2008.12.020. [ CrossRef ] [ Google Scholar ]
  • Artym, C., Carbonaro, M., & Boechler, P. (2016). Pre-service teachers designing and constructing “good digital games”. Australian Educational Computing, 31 (1).
  • Benitti FBV. Exploring the educational potential of robotics in schools: A systematic review. Computers & Education. 2012; 58 (3):978–988. doi: 10.1016/j.compedu.2011.10.006. [ CrossRef ] [ Google Scholar ]
  • Bergman EML. Finding citations to social work literature: The relative benefits of using Web of Science, Scopus, or Google Scholar. The Journal of Academic Librarianship. 2012; 38 (6):370–379. doi: 10.1016/j.acalib.2012.08.002. [ CrossRef ] [ Google Scholar ]
  • Bodily R, Verbert K. Review of research on student-facing learning analytics dashboards and educational recommender systems. IEEE Transactions on Learning Technologies. 2017; 10 (4):405–418. doi: 10.1109/TLT.2017.2740172. [ CrossRef ] [ Google Scholar ]
  • Bonk CJ. The world is open: How web technology is revolutionizing education. Association for the Advancement of Computing in Education (AACE); 2009. pp. 3371–3380. [ Google Scholar ]
  • Boyle EA, Hainey T, Connolly TM, Gray G, Earp J, Ott M, Lim T, Ninaus M, Ribeiro C, Pereira J. An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games. Computers and Education. 2016; 94 :178–192. doi: 10.1016/j.compedu.2015.11.003. [ CrossRef ] [ Google Scholar ]
  • Byun J, Joung E. Digital game-based learning for K–12 mathematics education: A meta-analysis. School Science and Mathematics. 2018; 118 (3–4):113–126. doi: 10.1111/ssm.12271. [ CrossRef ] [ Google Scholar ]
  • Crompton H, Burke D, Gregory KH. The use of mobile learning in PK-12 education: A systematic review. Computers & Education. 2017; 110 :51–63. doi: 10.1016/j.compedu.2017.03.013. [ CrossRef ] [ Google Scholar ]
  • Crompton H, Burke D, Gregory KH, Gräbe C. The use of mobile learning in science: A systematic review. Journal of Science Education and Technology. 2016; 25 (2):149–160. doi: 10.1007/s10956-015-9597-x. [ CrossRef ] [ Google Scholar ]
  • Daim TU, Rueda G, Martin H, Gerdsri P. Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change. 2006; 73 (8):981–1012. doi: 10.1016/j.techfore.2006.04.004. [ CrossRef ] [ Google Scholar ]
  • Dubé AK, Keenan A. Are games a viable home numeracy practice? In: Blevins-Knabe B, Austin AMB, editors. Early childhood mathematics skill development in the home environment. Springer; 2016. pp. 165–184. [ Google Scholar ]
  • Dube AK, McEwen RN. Do gestures matter? The implications of using touchscreen devices in mathematics instruction. Learning and Instruction. 2015; 40 :89–98. doi: 10.1016/j.learninstruc.2015.09.002. [ CrossRef ] [ Google Scholar ]
  • Dubé AK, McEwen R. Abilities and affordances: Factors influencing successful child-tablet interactions. Educational Technology Research & Development. 2017; 65 :889–908. doi: 10.1007/s11423-016-9493-y. [ CrossRef ] [ Google Scholar ]
  • Eisenberg M. 3D printing for children: What to build next? International Journal of Child-Computer Interaction. 2013; 1 (1):7–13. doi: 10.1016/j.ijcci.2012.08.004. [ CrossRef ] [ Google Scholar ]
  • Ely, D. P. (1996). Trends in educational technology 1995 . Information Resources Publications.
  • Ford M. Rise of the robots: Technology and the threat of a jobless future. Basic Book; 2015. [ Google Scholar ]
  • Ford S, Minshall T. Invited review article: Where and how 3D printing is used in teaching and education. Additive Manufacturing. 2019; 25 :131–150. doi: 10.1016/j.addma.2018.10.028. [ CrossRef ] [ Google Scholar ]
  • Fourie I, Meyer A. What to make of makerspaces. Library High Tech. 2015; 33 :519–525. doi: 10.1108/LHT-09-2015-0092. [ CrossRef ] [ Google Scholar ]
  • Freeman, A., Becker, S. A., & Cummins, M. (2017). NMC/CoSN Horizon Report: 2017. The New Media Consortium.
  • Games IA. “Gamestar Mechanic”: Learning a designer mindset through communicational competence with the language of games. Learning, Media, and Technology. 2010; 35 :31–52. doi: 10.1080/17439880903567774. [ CrossRef ] [ Google Scholar ]
  • Han K, Shin J. A systematic way of identifying and forecasting technological reverse salients using QFD, bibliometrics, and trend impact analysis: A carbon nanotube biosensor case. Technovation. 2014; 34 (9):559–570. doi: 10.1016/j.technovation.2014.05.009. [ CrossRef ] [ Google Scholar ]
  • Linstone Harold A., Turoff Murray. Delphi: A brief look backward and forward. Technological Forecasting and Social Change. 2011; 78 (9):1712–1719. doi: 10.1016/j.techfore.2010.09.011. [ CrossRef ] [ Google Scholar ]
  • Harzing AW. The publish or perish book. Tarma Software Research Pty Limited; 2010. [ Google Scholar ]
  • Hew KF, Cheung WS. Use of three-dimensional (3-D) immersive virtual worlds in K-12 and higher education settings: A review of the research. British Journal of Educational Technology. 2010; 41 (1):33–55. doi: 10.1111/j.1467-8535.2008.00900.x. [ CrossRef ] [ Google Scholar ]
  • Hirsh Pasek K, Zosh JM, Golinkoff RM, Gray JH, Robb MB, Kaufman J. Putting education in “educational” apps: Lessons from the science of learning . Psychological Science. 2015; 16 (1):3–34. doi: 10.1177/1529100615569721. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Huang L, Zhang Y, Guo Y, Zhu D, Porter AL. Four dimensional science and technology planning: A new approach based on bibliometrics and technology roadmapping. Technological Forecasting and Social Change. 2014; 81 :39–48. doi: 10.1016/j.techfore.2012.09.010. [ CrossRef ] [ Google Scholar ]
  • Hutchison A, Woodward L. A planning cycle for integrating digital technology into literacy instruction. The Reading Teacher. 2014; 67 (6):455–464. doi: 10.1002/trtr.1225. [ CrossRef ] [ Google Scholar ]
  • Ioannou A, Makridou E. Exploring the potentials of educational robotics in the development of computational thinking: A summary of current research and practical proposal for future work. Education and Information Technologies. 2018; 23 (6):2531–2544. doi: 10.1007/s10639-018-9729-z. [ CrossRef ] [ Google Scholar ]
  • Jensen L, Konradsen F. A review of the use of virtual reality head-mounted displays in education and training. Education and Information Technologies. 2018; 23 (4):1515–1529. doi: 10.1007/s10639-017-9676-0. [ CrossRef ] [ Google Scholar ]
  • Johnson L, Adams S, Haywood K. NMC Horizon Report: 2011 K-12 Edition. The New Media Consortium; 2011. [ Google Scholar ]
  • Karvounidis T, Chimos K, Bersimis S, Douligeris C. Factors, issues and interdependencies in the incorporation of a Web 2.0 based learning environment in higher education. Education and Information Technologies. 2018; 23 :935–955. doi: 10.1007/s10639-017-9644-8. [ CrossRef ] [ Google Scholar ]
  • Kavanagh S, Luxton-Reilly A, Wuensche B, Plimmer B. A systematic review of Virtual Reality in education. Themes in Science and Technology Education. 2017; 10 (2):85–119. [ Google Scholar ]
  • Kiili K, Ketamo H, Koivisto A, Finn E. Studying the user experience of a tablet based math game. International Journal of Game-Based Learning. 2014; 4 (1):60–77. doi: 10.4018/IJGBL.2014010104. [ CrossRef ] [ Google Scholar ]
  • Kim J. Big data, health informatics, and the future of cardiovascular medicine. Journal of the American College of Cardiology. 2017; 69 :899–902. doi: 10.1016/j.jacc.2017.01.006. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Landers RN. Developing a theory of gamified learning: Linking serious games and gamification of learning. Simulation & Gaming. 2014; 45 :752–768. doi: 10.1177/1046878114563660. [ CrossRef ] [ Google Scholar ]
  • Li MC, Tsai CC. Game-based learning in science education: A review of relevant research. Journal of Science Education and Technology. 2013; 22 (6):877–898. doi: 10.1007/s10956-013-9436-x. [ CrossRef ] [ Google Scholar ]
  • Li Y, Linke BS, Voet H, Falk B, Schmitt R, Lam M. Cost, sustainability and surface roughness quality—A comprehensive analysis of products made with personal 3D printers. CIRP Journal of Manufacturing Science and Technology. 2017; 16 :1–11. doi: 10.1016/j.cirpj.2016.10.001. [ CrossRef ] [ Google Scholar ]
  • Liu M, Scordino R, Geurtz R, Navarrete C, Ko Y, Lim M. A look at research on mobile learning in K–12 education from 2007 to the present. Journal of Research on Technology in Education. 2014; 46 (4):325–372. doi: 10.1080/15391523.2014.925681. [ CrossRef ] [ Google Scholar ]
  • Loup, G., Serna, A., Iksal, S., & George, S. (2016) Immersion and persistence: Improving learners’ engagement in authentic learning situations. Vol. 9891 LNCS. In 11th European conference on technology enhanced learning, EC-TEL 2016 (pp. 410–415). Springer.
  • Loy J. eLearning and eMaking: 3D printing blurring the digital and the physical. Education Sciences. 2014; 4 :108–121. doi: 10.3390/educsci4010108. [ CrossRef ] [ Google Scholar ]
  • Machin, S. J., McNally, S., & Silva, O. (2006). New technology in schools: Is there a payoff?
  • Madrigal E, Prajapati S, Hernandez-Prera JC. Introducing a virtual reality experience in anatomic pathology education. American Journal of Clinical Pathology. 2016; 146 (4):462–468. doi: 10.1093/ajcp/aqw133. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Magnisalis I, Demetriadis S, Karakostas A. Adaptive and intelligent systems for collaborative learning support: A review of the field. IEEE Transactions on Learning Technologies. 2011; 4 (1):5–20. doi: 10.1109/TLT.2011.2. [ CrossRef ] [ Google Scholar ]
  • Martin S, Diaz G, Sancristobal E, Gil R, Castro M, Peire J. New technology trends in education: Seven years of forecasts and convergence. Computers & Education. 2011; 57 (3):1893–1906. doi: 10.1016/j.compedu.2011.04.003. [ CrossRef ] [ Google Scholar ]
  • Martin S, López-Martín A, Lopez-Rey J, Cubillo A, Moreno-Pulido A, Castro M. Analysis of new technology trends in education: 2010–2015. IEEE Access. 2018; 6 :36840–36848. doi: 10.1109/ACCESS.2018.2851748. [ CrossRef ] [ Google Scholar ]
  • McEwen R, Dubé AK. Understanding tablets from early childhood to adulthood: Encounters with touch technology. Routledge; 2017. [ Google Scholar ]
  • McGregor, V. K., Calderon, S. H., & Tonelli, R. D. (2013). Big data and consumer financial information. Business Law Today, Nov , 1–4.
  • McLean KJ. The implementation of bring your own device (BYOD) in primary [elementary] schools. Frontiers in Psychology. 2016; 7 :24–33. doi: 10.3389/fpsyg.2016.01739. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Merino-Campos C, del Castillo Fernndez H. The benefits of active video games for educational and physical activity approaches: A systematic review. Journal of New Approaches in Educational Research (NAER Journal) 2016; 5 (2):115–122. [ Google Scholar ]
  • Moro A, Boelman E, Joanny G, Garcia JL. A bibliometric-based technique to identify emerging photovoltaic technologies in a comparative assessment with expert review. Renewable Energy. 2018; 123 :407–416. doi: 10.1016/j.renene.2018.02.016. [ CrossRef ] [ Google Scholar ]
  • Morries S. A visualization system for exploring document databases for technology forecasting. Computers & Industrial Engineering. 2002; 43 (4):841–862. doi: 10.1016/S0360-8352(02)00143-2. [ CrossRef ] [ Google Scholar ]
  • Nevski E, Siibak A. The role of parents and parental mediation on 0–3-year olds’ digital play with smart devices: Estonian parents’ attitudes and practices. Early Years. 2016; 36 (3):227–241. doi: 10.1080/09575146.2016.1161601. [ CrossRef ] [ Google Scholar ]
  • Norton MJ. Introductory concepts in information science. Information Processing & Management. 2001; 37 (5):764–766. [ Google Scholar ]
  • OECD. (2018). Internet access (indicator). Retrieved July 6, 2018, from 10.1787/69c2b997-en
  • Outhwaite, L. A., Gulliford, A., & Pitchford, N. J. (2017). Closing the gap: Efficacy of a tablet intervention to support the development of early mathematical skills in UK primary school.
  • Papert S. Mindstorms: Children, computers, and powerful idea. Basic Books; 1980. [ Google Scholar ]
  • Paraskeva F, Mysirlaki S, Papagianni A. Multiplayer online games as educational tools: Facing new challenges in learning. Computers & Education. 2010; 54 :498–505. doi: 10.1016/j.compedu.2009.09.001. [ CrossRef ] [ Google Scholar ]
  • Plass JL, Mayer RE, Homer BD, editors. Handbook of game-based learning. The MIT Press; 2020. [ Google Scholar ]
  • Rasheed, F., Onkar, P., & Narula, M. (2015). Immersive virtual reality to enhance the spatial awareness of students. Paper presented at the 7th international conference on human computer interaction, India.
  • Richey EC. Reflections on the 2008 AECT definitions of the Field. TechTrends. 2008; 52 (1):24–25. doi: 10.1007/s11528-008-0108-2. [ CrossRef ] [ Google Scholar ]
  • Reiser RA, Ely DP. The field of educational technology as reflected through its definitions. Educational Technology Research and Development. 1997; 45 (3):63–72. doi: 10.1007/BF02299730. [ CrossRef ] [ Google Scholar ]
  • Robertson, A. (2019, October). Google is discontinuing the Daydream View VR headset, and the Pixel 4 won’t support Daydream.
  • Roll I, Wylie R. Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education. 2016; 26 (2):582–599. doi: 10.1007/s40593-016-0110-3. [ CrossRef ] [ Google Scholar ]
  • Shuler, C. (2012). iLearn: A content analysis of the iTunes App Store's Education Section. New York
  • Skinner BF. The science of learning and the art of teaching. Harvard Educational Review. 1954; 24 (2):86–97. [ Google Scholar ]
  • Skinner BF. Teaching machines: From the experimental study of learning come devices which arrange optimal conditions for self-instruction. Science. 1958; 128 (3330):969–977. doi: 10.1126/science.128.3330.969. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Skinner BF. The technology of teaching. Appleton-Century-Crofts; 1968. [ Google Scholar ]
  • Stelzer B, Meyer-Brötz F, Schiebel E, Brecht L. Combining the scenario technique with bibliometrics for technology foresight: The case of personalized medicine. Technological Forecasting and Social Change. 2015; 98 :137–156. doi: 10.1016/j.techfore.2015.06.008. [ CrossRef ] [ Google Scholar ]
  • Tawfik AA, Reeves TD, Stich A. Intended and unintended consequences of educational technology on social inequality. TechTrends. 2016; 60 (6):1–8. doi: 10.1007/s11528-016-0109-5. [ CrossRef ] [ Google Scholar ]
  • Teng C, Chen J, Chen Z. Impact of augmented reality on programming language learning: Efficiency and perception. Journal of Educational Computing Research. 2018; 56 (2):254–271. doi: 10.1177/0735633117706109. [ CrossRef ] [ Google Scholar ]
  • Toh LPR, Causo A, Tzuo P-W, Chen I, Yeo SH. A review on the use of robotics in education and young children. Educational Technology & Society. 2016; 19 :148–163. [ Google Scholar ]
  • Turner H, Resch G, Southwick D, McEwen R, Dubé AK, Record I. Using 3D printing to enhance understanding and engagement with young audiences: Lessons from workshops in a museum. Curator: the Museum Journal. 2017; 60 (3):311–333. doi: 10.1111/cura.12224. [ CrossRef ] [ Google Scholar ]
  • Vanderlinde R, Hermans R, van Braak J. ICT research and school performance feedback: A perfect marriage? Educational Studies. 2010; 36 (3):341–344. doi: 10.1080/03055690903425441. [ CrossRef ] [ Google Scholar ]
  • Voivonta T, Avraamidou L. Facebook: A potentially valuable educational tool? Educational Media International. 2018; 55 (1):34–48. doi: 10.1080/09523987.2018.1439708. [ CrossRef ] [ Google Scholar ]
  • Xie J, Basham JD, Marino MT, Rice M. Reviewing research on mobile learning for students with and without disabilities in k-12 educational settings. Journal of Special Education Technology. 2018; 33 :27–39. doi: 10.1177/0162643417732292. [ CrossRef ] [ Google Scholar ]
  • Yeo W, Kim S, Park H, Kang J. A bibliometric method for measuring the degree of technological innovation. Technological Forecasting and Social Change. 2015; 95 :152–162. doi: 10.1016/j.techfore.2015.01.018. [ CrossRef ] [ Google Scholar ]
  • Young MF, Slota S, Cutter AB, Jalette G, Mullin G, Lai B, Simeoni Z, SiTran M, Yukhymenko M. Our princess is in another castle: A review of trends in serious gaming for education. Review of Educational Research. 2012; 82 (1):61–89. doi: 10.3102/0034654312436980. [ CrossRef ] [ Google Scholar ]
  • Yu, T., & Jo, I-H. (2014). Educational technology approach toward learning analytics: Relationship between student online behavior and learning performance in higher education. In Proceedings of the fourth international conference on learning analytics and knowledge (LAK '14), ACM, New York, NY (pp. 269–270).
  • Zhang M, Trussell RP, Gallegos B, Asam RR. Using math apps for improving student learning: An exploratory study in an inclusive fourth grade classroom. TechTrends. 2015; 59 (2):32–39. doi: 10.1007/s11528-015-0837-y. [ CrossRef ] [ Google Scholar ]
  • Zhang J, Zhang X, Jiang S, Ordóñez de Pablos P, Sun Y. Mapping the study of learning analytics in higher education. Behaviour & Information Technology. 2018; 37 (10–11):1142–1155. doi: 10.1080/0144929X.2018.1529198. [ CrossRef ] [ Google Scholar ]

Suggestions or feedback?

MIT News | Massachusetts Institute of Technology

  • Machine learning
  • Social justice
  • Black holes
  • Classes and programs


  • Aeronautics and Astronautics
  • Brain and Cognitive Sciences
  • Architecture
  • Political Science
  • Mechanical Engineering

Centers, Labs, & Programs

  • Abdul Latif Jameel Poverty Action Lab (J-PAL)
  • Picower Institute for Learning and Memory
  • Lincoln Laboratory
  • School of Architecture + Planning
  • School of Engineering
  • School of Humanities, Arts, and Social Sciences
  • Sloan School of Management
  • School of Science
  • MIT Schwarzman College of Computing

A new way to detect radiation involving cheap ceramics

Press contact :.

Jennifer Rupp, Thomas Defferriere, Harry Tuller, and Ju Li pose standing in a lab, with a nuclear radiation warning sign in the background

Previous image Next image

The radiation detectors used today for applications like inspecting cargo ships for smuggled nuclear materials are expensive and cannot operate in harsh environments, among other disadvantages. Now, in work funded largely by the U.S. Department of Homeland Security with early support from the U.S. Department of Energy, MIT engineers have demonstrated a fundamentally new way to detect radiation that could allow much cheaper detectors and a plethora of new applications.

They are working with Radiation Monitoring Devices , a company in Watertown, Massachusetts, to transfer the research as quickly as possible into detector products.

In a 2022 paper in Nature Materials , many of the same engineers reported for the first time how ultraviolet light can significantly improve the performance of fuel cells and other devices based on the movement of charged atoms, rather than those atoms’ constituent electrons.

In the current work, published recently in Advanced Materials , the team shows that the same concept can be extended to a new application: the detection of gamma rays emitted by the radioactive decay of nuclear materials.

“Our approach involves materials and mechanisms very different than those in presently used detectors, with potentially enormous benefits in terms of reduced cost, ability to operate under harsh conditions, and simplified processing,” says Harry L. Tuller, the R.P. Simmons Professor of Ceramics and Electronic Materials in MIT’s Department of Materials Science and Engineering (DMSE).

Tuller leads the work with key collaborators Jennifer L. M. Rupp, a former associate professor of materials science and engineering at MIT who is now a professor of electrochemical materials at Technical University Munich in Germany, and Ju Li, the Battelle Energy Alliance Professor in Nuclear Engineering and a professor of materials science and engineering. All are also affiliated with MIT’s Materials Research Laboratory

“After learning the Nature Materials work, I realized the same underlying principle should work for gamma-ray detection — in fact, may work even better than [UV] light because gamma rays are more penetrating — and proposed some experiments to Harry and Jennifer,” says Li.

Says Rupp, “Employing shorter-range gamma rays enable [us] to extend the opto-ionic to a radio-ionic effect by modulating ionic carriers and defects at material interfaces by photogenerated electronic ones.”

Other authors of the Advanced Materials paper are first author Thomas Defferriere, a DMSE postdoc, and Ahmed Sami Helal, a postdoc in MIT’s Department of Nuclear Science and Engineering.

Modifying barriers

Charge can be carried through a material in different ways. We are most familiar with the charge that is carried by the electrons that help make up an atom. Common applications include solar cells. But there are many devices — like fuel cells and lithium batteries — that depend on the motion of the charged atoms, or ions, themselves rather than just their electrons.

The materials behind applications based on the movement of ions, known as solid electrolytes, are ceramics. Ceramics, in turn, are composed of tiny crystallite grains that are compacted and fired at high temperatures to form a dense structure. The problem is that ions traveling through the material are often stymied at the boundaries between the grains.

In their 2022 paper, the MIT team showed that ultraviolet (UV) light shone on a solid electrolyte essentially causes electronic perturbations at the grain boundaries that ultimately lower the barrier that ions encounter at those boundaries. The result: “We were able to enhance the flow of the ions by a factor of three,” says Tuller, making for a much more efficient system.

Vast potential

At the time, the team was excited about the potential of applying what they’d found to different systems. In the 2022 work, the team used UV light, which is quickly absorbed very near the surface of a material. As a result, that specific technique is only effective in thin films of materials. (Fortunately, many applications of solid electrolytes involve thin films.)

Light can be thought of as particles — photons — with different wavelengths and energies. These range from very low-energy radio waves to the very high-energy gamma rays emitted by the radioactive decay of nuclear materials. Visible light — and UV light — are of intermediate energies, and fit between the two extremes.

The MIT technique reported in 2022 worked with UV light. Would it work with other wavelengths of light, potentially opening up new applications? Yes, the team found. In the current paper they show that gamma rays also modify the grain boundaries resulting in a faster flow of ions that, in turn, can be easily detected. And because the high-energy gamma rays penetrate much more deeply than UV light, “this extends the work to inexpensive bulk ceramics in addition to thin films,” says Tuller. It also allows a new application: an alternative approach to detecting nuclear materials.

Today’s state-of-the-art radiation detectors depend on a completely different mechanism than the one identified in the MIT work. They rely on signals derived from electrons and their counterparts, holes, rather than ions. But these electronic charge carriers must move comparatively great distances to the electrodes that “capture” them to create a signal. And along the way, they can be easily lost as they, for example, hit imperfections in a material. That’s why today’s detectors are made with extremely pure single crystals of material that allow an unimpeded path. They can be made with only certain materials and are difficult to process, making them expensive and hard to scale into large devices.

Using imperfections

In contrast, the new technique works because of the imperfections — grains — in the material. “The difference is that we rely on ionic currents being modulated at grain boundaries versus the state-of-the-art that relies on collecting electronic carriers from long distances,” Defferriere says.

Says Rupp, “It is remarkable that the bulk ‘grains’ of the ceramic materials tested revealed high stabilities of the chemistry and structure towards gamma rays, and solely the grain boundary regions reacted in charge redistribution of majority and minority carriers and defects.”

Comments Li, “This radiation-ionic effect is distinct from the conventional mechanisms for radiation detection where electrons or photons are collected. Here, the ionic current is being collected.”

Igor Lubomirsky, a professor in the Department of Materials and Interfaces at the Weizmann Institute of Science, Israel, who was not involved in the current work, says, “I found the approach followed by the MIT group in utilizing polycrystalline oxygen ion conductors very fruitful given the [materials’] promise for providing reliable operation under irradiation under the harsh conditions expected in nuclear reactors where such detectors often suffer from fatigue and aging. [They also] benefit from much-reduced fabrication costs.”

As a result, the MIT engineers are hopeful that their work could result in new, less expensive detectors. For example, they envision trucks loaded with cargo from container ships driving through a structure that has detectors on both sides as they leave a port. “Ideally, you’d have either an array of detectors or a very large detector, and that’s where [today’s detectors] really don’t scale very well,” Tuller says.

Another potential application involves accessing geothermal energy, or the extreme heat below our feet that is being explored as a carbon-free alternative to fossil fuels. Ceramic sensors at the ends of drill bits could detect pockets of heat — radiation — to drill toward. Ceramics can easily withstand extreme temperatures of more than 800 degrees Fahrenheit and the extreme pressures found deep below the Earth’s surface.

The team is excited about additional applications for their work. “This was a demonstration of principle with just one material,” says Tuller, “but there are thousands of other materials good at conducting ions.”

Concludes Defferriere: “It’s the start of a journey on the development of the technology, so there’s a lot to do and a lot to discover.”

This work is currently supported by the U.S. Department of Homeland Security, Countering Weapons of Mass Destruction Office. This support does not constitute an express or implied endorsement on the part of the government. It was also funded by the U.S. Defense Threat Reduction Agency.

Share this news article on:

Related links.

  • Harry Tuller
  • Tuller Research Group
  • Materials Research Laboratory

Related Topics

  • Nuclear security and policy
  • Materials science and engineering
  • Nuclear science and engineering
  • Department of Energy (DoE)

Related Articles

Harry Tuller and student pose for a photo in a lab, with a computer screen on a table between them showing data

A simple way to significantly increase lifetimes of fuel cells and other devices

Harry L. Tuller sits in a chair in front of a bookcase in his office at MIT.

Harry Tuller honored for career advancing solid-state chemistry and electrochemistry

Photo of two smiling men standing at a lab bench covered with electronic equipment

Light could boost performance of fuel cells, lithium batteries, and other devices

Previous item Next item

More MIT News

Headshot of a woman in a colorful striped dress.

A biomedical engineer pivots from human movement to women’s health

Read full story →

Closeup of someone’s hands holding a stack of U.S. patents. The top page reads “United States of America “ and “Patent” in gold lettering, among other smaller text. They are next to a window that looks down on a city street.

MIT tops among single-campus universities in US patents granted

Photo of the facade of the MIT Schwarzman College of Computing building, which features a shingled glass exterior that reflects its surroundings

A crossroads for computing at MIT

Hammaad Adam poses in front of a window. A brick building with large windows is behind him.

Growing our donated organ supply

Two hands inspect a lung X-ray. One hand is illustrated with nodes and lines creating a neural network. The other is a doctor’s hand. Four “alert” icons appear on the lung X-ray.

New AI method captures uncertainty in medical images

A lab researcher looking through a microscope with human cells in the background

Improving drug development with a vast map of the immune system

  • More news on MIT News homepage →

Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA, USA

  • Map (opens in new window)
  • Events (opens in new window)
  • People (opens in new window)
  • Careers (opens in new window)
  • Accessibility
  • Social Media Hub
  • MIT on Facebook
  • MIT on YouTube
  • MIT on Instagram


  1. The Importance Of Technology In Education Infographic

    research topics on technology in education

  2. Teaching with Digital Technologies Infographic

    research topics on technology in education

  3. Technology In Education: Facts You Must Know

    research topics on technology in education

  4. 55 Brilliant Research Topics For STEM Students

    research topics on technology in education

  5. Technology in Education

    research topics on technology in education

  6. Technology Topics 100 technology topics for research papers

    research topics on technology in education


  1. Top 10 Technology Trends in Education in 2023

  2. Best and Most Recent Research Areas That Make You Success in the Research Arena

  3. 177: D&H on How Gaming Has Grown Up

  4. The Role of Technology in Education

  5. The Importance of Modern Technology in Schools

  6. Technology Research Paper Topics


  1. Trends and Topics in Educational Technology, 2023 Edition

    In this editorial, we present trends and popular topics in educational technology for the year 2022. We used a similar public internet data mining approach (Kimmons & Veletsianos, 2018) to previous years (Kimmons, 2020; Kimmons et al., 2021; Kimmons & Rosenberg, 2022), extracting and analyzing data from three large data sources: the Scopus research article database, the Twitter #EdTech ...

  2. Understanding the role of digital technologies in education: A review

    Students may now learn many topics on their own by using internet resources and digital classrooms. In schools, colour charts, graphs, and models describe the finest instruction of the class. However, they are now considered old-fashioned methods of giving education. ... Educational Technology Research and Development, 55 (3) (2007), pp. 301 ...

  3. Education reform and change driven by digital technology: a

    The study highlights the dual influence of technological factors and historical context on the research topic. Technology is a key factor in enabling education to transform and upgrade, and the ...

  4. AI technologies for education: Recent research & future directions

    5. Conclusion. AI technology is rapidly advancing and its application in education is expected to grow rapidly in the near future. In the USA, for example, education sectors are predicted with an approximate 48% of growth in AI market in the near future, from 2018 to 2022 (, 2018).

  5. 15 EdTech research papers that we share all the time

    This critical review by our own Bjӧrn Haßler, Sara Hennessy, and Louis Major has been cited over 200 times since it was published in 2016. It examines evidence from 23 studies on tablet use at the primary and secondary school levels. It discusses the fragmented nature of the knowledge base and limited rigorous evidence on tablet use in ...

  6. What 126 studies say about education technology

    J-PAL North America's recently released publication summarizes 126 rigorous evaluations of different uses of education technology and their impact on student learning. In recent years, there has been widespread excitement around the transformative potential of technology in education. In the United States alone, spending on education technology ...

  7. Technology in education: GEM Report 2023

    It provides the mid-term assessment of progress towards SDG 4, which was summarized in a brochure and promoted at the 2023 SDG Summit. The 2023 GEM Report and 200 PEER country profiles on technology and education were launched on 26 July. A recording of the global launch event can be watched here and a south-south dialogue between Ministers of ...

  8. New Educational Technologies and Their Impact on Students ...

    In the new millennium, education is rapidly changing due to the more and more pervasive use of technology to support teaching and learning. New Information and Communication Technologies (ICTs), such as internet, wikis, blogs, search engines, emails and instant messaging require new literacy frameworks and new contexts for learning and life. A digital approach to education implies pursuing new ...

  9. Current Trends (and Missing Links) in Educational Technology Research

    Trending Research Topics. To understand the topics educational technology researchers have been studying over the past 5 years, I used the Elsevier Scopus API to collect all articles from the most-highly-cited journals in the field of educational technology as identified by Google Scholar (), which included TechTrends, Computers & Education, Educational Technology Research & Development, and ...

  10. Trends and Topics in Educational Technology, 2023 Edition

    What Were Trending Topics in EdTech Journals in 2022? Research topics in the field of educational technology in 2022 were, with a few exceptions, noticeably consistent with those of previous years (see Table 1; Kimmons et al., 2021; Kimmons & Rosenberg, 2022).We compiled the titles of 2699 articles from top educational technology journals (n = 16) identified by Google Scholar and retrieved ...

  11. Emerging Technologies in K-12 Education: A Future HCI Research Agenda

    In this process, 476 records were excluded because either the target group did not correspond with the inclusion criteria (n = 104), the focus was on educational technology (e.g., using AR to teach geography) rather than technology education (n = 312), or the topic was considered irrelevant (e.g., no reference to emerging technologies ...

  12. The Hottest Topics in Edtech for 2022!

    The focus is on educational strategies and instruction with technology for higher-order thinking — not tools and gadgets. "The pedagogy and learning strategies are rising to the top more than the technology topics," Gagliolo said. "It shows that awareness that learning comes first and tech tools are there to support."

  13. PDF Technology and Its Use in Education: Present Roles and Future Prospects

    Technology and its use in Education: Present Roles and Future Prospects 2 Abstract: (Purpose) This article describes two current trends in Educational Technology: distributed learning and electronic databases. (Findings) Topics addressed in this paper include: (1) distributed learning as a means of professional development; (2) distributed learning for

  14. ISTE

    Topics. The most compelling topics among educators who embrace technology to transform teaching and learning are not about the tech at all, but about the students. Here's a list of the hottest trends in edtech right now. 1. Computational thinking. Computational thinking (CT) is no longer a concept discussed only in computer science or coding ...

  15. PDF 1:1 Technology and its Effect on Student Academic Achievement and ...

    technology with teacher training to establish research-based instructional methods. Again in 2009, President Barack Obama signed the American Recovery and Reinvestment Act, which provided $4.35 billion for the Race to the Top Fund for education innovation and reform (Race to

  16. Impacts of digital technologies on education and factors influencing

    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 ...

  17. How Has Technology Changed Education?

    Technology has also begun to change the roles of teachers and learners. In the traditional classroom, such as what we see depicted in de Voltolina's illustration, the teacher is the primary source of information, and the learners passively receive it. This model of the teacher as the "sage on the stage" has been in education for a long ...

  18. (PDF) Impact of modern technology in education

    Importance of technolog y in education. The role of technology in the field of education is four-. fold: it is included as a part of the curriculum, as an. instructional delivery system, as a ...

  19. Leveraging emerging technology for refugee education and social

    Keywords: Refugee Education, AI discrimination, AI, AI bias . Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements.Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

  20. 7 Examples Of Technology In The Elementary Classroom

    Edtech in Elementary Schools. Take a look at some great examples of technology in the elementary classroom and how these tools are changing the way young children learn. Written by Mae Rice. Image: Shutterstock. UPDATED BY. Sara B.T. Thiel | Oct 09, 2023. 7 Edtech Companies to Know. Great Companies Need Great People.

  21. 6. Teachers' views on the state of public K-12 education

    Overall, teachers have a negative view of the U.S. K-12 education system - both the path it's been on in recent years and what its future might hold. The vast majority of teachers (82%) say that the overall state of public K-12 education has gotten worse in the last five years. Only 5% say it's gotten better, and 11% say it has gotten ...

  22. Georgia Tech AI Makerspace

    The Georgia Institute of Technology, also known as Georgia Tech, is a top-ranked public college and one of the leading research universities in the USA. Georgia Tech provides a technologically focused education to more than 25,000 undergraduate and graduate students in fields ranging from engineering, computing, and sciences, to business, design, and liberal arts.

  23. MIT tops among single-campus universities in US patents granted

    For the 10th consecutive year, MIT ranks No. 2 among all colleges and No. 1 among colleges with one main campus in the number of patents granted. MIT's Technology Licensing Office (TLO) bridges groundbreaking research to societal impact through patents and innovation.

  24. What's It Like To Be a Teacher in America Today?

    How teachers view the education system. A large majority of teachers (82%) say the overall state of public K-12 education has gotten worse in the past five years. And very few are optimistic about the next five years: Only 20% of teachers say public K-12 education will be a lot or somewhat better five years from now.

  25. Identification and evaluation of technology trends in K-12 education

    Mobile technology was the most published topic in this group, followed by tablets, Apps, wearables, and BYOD. The number of articles on APPS shows an increased focus on this aspect of mobile technology that is not predicted by the HRs. ... Further, the extant body of educational technology research is often criticized for focusing on what is ...

  26. A new way to detect radiation involving cheap ceramics

    Concludes Defferriere: "It's the start of a journey on the development of the technology, so there's a lot to do and a lot to discover." This work is currently supported by the U.S. Department of Homeland Security, Countering Weapons of Mass Destruction Office.