research title about drop out students

Student Engagement and School Dropout: Theories, Evidence, and Future Directions

• First Online: 20 October 2022

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• Isabelle Archambault 3 ,
• Michel Janosz 3 ,
• Elizabeth Olivier 3 &
• Véronique Dupéré 3  

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School dropout is a major preoccupation in all countries. Several factors contribute to this outcome, but research suggests that dropouts mostly have gone through a process of disengaging from school. This chapter aims to present a synthesis of this process according to the major theories in the field and review empirical research linking student disengagement and school dropout. This chapter also presents the common risk and protective factors associated with these two issues, the profiles of students who drop out as well as the disengagement trajectories they follow and leading to their decision to quit school. Finally, it highlights the main challenges as well as the future directions that research should prioritize in the study of student engagement and school dropout.

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Archambault, I., Janosz, M., Olivier, E., Dupéré, V. (2022). Student Engagement and School Dropout: Theories, Evidence, and Future Directions. In: Reschly, A.L., Christenson, S.L. (eds) Handbook of Research on Student Engagement. Springer, Cham. https://doi.org/10.1007/978-3-031-07853-8_16

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Systematic review article, dropout in rural higher education: a systematic review.

• 1 School of Economic and Administrative Sciences, Corporación Universitaria de Asturias, Bogotá, Colombia
• 2 Faculty of Natural Sciences and Engineering, Universidad de Bogotá Jorge Tadeo Lozano, Bogotá, Colombia

Student dropout in higher education has been of great interest to the academic community, state and social actors over the last three decades, due to the various effects that this event has on the student, the family, higher education institutions, and the state itself. It is recognised that dropout at this level of education is extremely complex due to its multi-causality which is expressed in the existing relationship in its explanatory variables associated with the students, their socioeconomic and academic conditions, as well as the characteristics of the educational institutions. Thus, the aim of this article was to identify the individual, socioeconomic, academic, and institutional explanatory variables involved in student dropout in rural populations, based on a synthesis of the evidence available in the SCOPUS database. In order to achieve it, a mixed systematic review was defined under the PRISMA 2020 method. The analysis was approached in two stages; the first concerned the identification of the documents and the conformation of the sample, where 21 documents were distinguished for effectively dealing with dropout in rural higher education; and the second corresponded to the procedures defined for the development of the bibliometric analysis and synthesis of the information found in the documents. The results showed the distribution of studies by country, years of publication, the categorisation of the documents in SCOPUS, their classification by type and the methodologies used in the development of the studies analysed, as well as the variables that have been addressed in previous research. In this way, it is concluded that the results of the studies are not generalisable, either because of the size of the sample or because of the marked social asymmetries that exist in some countries, which can make the findings lack significance; on the other hand, the interest in research on variables associated with individual and academic determinants to explain rural student dropout is highlighted. In addition, some future research lines which can be addressed as a complement to the current view of the dropout event in rural higher education were identified.

Introduction

In the last three decades, the study of student dropout in higher education has become one of the lines of research of greatest interest for the academic community, state and social actors due to the high rates of this event, its multi-causality and the effects or consequences it has for the individual, the family, Higher Education Institutions (HEIs), society in general and the state. Considering what has been stated, it is also recognised that dropout rates worldwide have not been controlled and, on the contrary, have increased from an aggregate perspective, being sharpened by the health, economic and social crisis derived from COVID-19, which indicates the ineffectiveness of the actions of governments and HEIs, represented in public policies, the establishment of retention and graduation plans (P&GO for its Spanish acronym) and early warning systems (SAT for its Spanish acronym) ( Marquez-Vera et al., 2013 ; Orellana et al., 2021 ; UNESCO, 2021 ). An example of this is the situation in OECD countries where the dropout rate rose from 35% in 2005 to 64.5% in 2018, and in countries such as Luxembourg, Hungary, Sweden, Czech Republic and Slovakia this rate was higher than 70% ( OECD, 2018 ); or the particular case of Latin America, which has historically had high levels of dropout in higher education, close to 54%, and which are expected to increase as an effect of COVID-19 due to the strong social asymmetries that exist in the region ( Becerra et al., 2020 ; UNESCO, 2021 ).

Faced with the multi-causality of dropout, efforts have been made to establish the variables that explain current dropout rates and the causes that lead students not to complete their higher education studies, which has resulted in various perspectives and the development of tools that allow decision-makers to have a holistic view of dropout prevention and mitigation ( Kehm et al., 2019 ). That said, research has focused on analysing the influence of specific variables on the materialisation of dropout based on individual student conditions such as age, gender, marital status, family environment, intrinsic motivations and academic self-regulation (e.g., Ghignoni, 2017 ; Arias-Velandia et al., 2018 ; Behr et al., 2020 ); the student’s socioeconomic background exemplified by the socioeconomic stratum of the dwelling he or she lives in, family income, economic dependency and the macro-economic environment of the country (e.g., Contreras, 2017 ; Behr et al., 2020 ; Palacio et al., 2020 ; Schmitt et al., 2020 ); the academic factors represented in the development of competencies prior to entry into higher education, secondary school graduation tests, levels of satisfaction in the training programme and the number of courses concurrently taken (e.g., Guzmán, et al., 2020a ; Heidrich, 2018 ); and finally, institutional circumstances in relation to HEI policies, the technological and pedagogical resources provided by the educational institution, the level of interaction with teachers and students and the pedagogical model (e.g., Armstrong et al., 2018 ; Choi and Kim, 2018 ).

On the other hand, the analysis of the multi-causality of dropout has been widely linked to the construction of qualitative, quantitative and mixed models, with the aim of explaining the event in terms of multiple variables; of such studies, the developments made by Spady (1970) , Tinto and Cullen (1973) , Fishbein and Ajzen (1975) , as well as Tinto (1975 ; 1987) , which formed the basis of subsequent studies, and, more recently, Barragán and González (2017) , Pérez et al. (2019) , Venegas-Muggli (2020) , Kilian et al. (2020) , Segovia-Garcia and Said-Hung (2021) , among others.

However, the consequences of dropout for the actors in the tertiary education subsystem are usually varied. Thus, in the case of students, dropout represents the affectation of learning factors related to emotion, cognition, motivation, among others ( Hällsten, 2017 ), which has long-term repercussions on various difficulties, especially in terms of their work performance ( Hällsten, 2017 ; Sosu and Pheunpha, 2019 ). For the family, the student’s dropout symbolises a sunk cost, due to the expenses were incurred to cover the studies which will never be recovered, ( Moreno et al., 2019 ), as well as the destruction or impossibility of building long-term social capital that allows changing the family’s future conditions in both educational and socioeconomic aspects ( Ghignoni, 2017 ). As far as HEIs are concerned, the materialisation of this event means a difficulty in fulfilling their substantive functions ( Voelkle and Sander, 2008 ) by affecting the quality conditions of the training programmes and the reputation of the institutions ( Ortiz and Dehon, 2013 ), as well as impacting the income of HEIs in terms of student enrolments, since dropout represents an opportunity cost that translates into the loss of financial support ( Barragán and Rodríguez, 2015 ).

Finally, in the case of the state, the consequences of dropping out can be categorised as financial and social. In this sense, the materialisation of student dropout represents a damage to the resources made available by the State, since " (...) students who do not graduate on time (or at all) when they receive public funding consume valuable fiscal resources, which in many cases are not recoverable" ( The World Bank, 2017 , p. 14); and, on the other hand, dropout prevents the consolidation of the benefits of higher education by making it impossible to improve the average income of the population ( Cristia and Pulido, 2020 ), increase the productivity of the economy ( Cristia and Pulido, 2020 ), consolidate democratic processes ( Lance, 2011 ) and reduce crime ( Chalfin and Deza, 2019 ). In brief, student dropout in higher education can slow down the development and social transformation sought by implementing public policies related to access to higher education, hence the importance of its prevention and mitigation ( Guzmán et al., 2021 ).

Under the widespread interest of the academic community, state, and social actors in the study of dropout at the higher education level, multiple opportunities have been identified for understanding the event, especially in student groups such as those from or located in rural areas, ethnic minorities and those displaced by armed conflict, which have not been widely studied. This has been evidenced in literature reviews focused on identifying the variables that influence dropout by educational modality, (e.g., Kara et al., 2019 ; Guzmán et al., 2020b ; Orellana et al., 2021 ), the role of the intrinsic and extrinsic context to the student ( Broadbent and Poon, 2015 ), the methodological approach to the study of dropout ( Rodriguez Urrego, 2019 ) and the organisational perspective of the effects of dropout ( Fonseca and Garcia, 2016 ). Based on what has been previously stated, a holistic view of this event in rural higher education is required, due to the efforts made in recent years by states and HEIs to link a population that was marginalised, especially in developing countries, to the educational subsystem and to materialise the direct and indirect benefits of a higher level of education for the population, which are mitigated by the high dropout rates in rural areas. In addition, the lack of such a holistic view makes it difficult for decision-makers to develop effective and efficient public and institutional policies by governments and HEIs to deal with the event of dropout. Thus, the aim of this article was to identify the individual, socioeconomic, academic, and institutional explanatory variables involved in student dropout in rural populations, based on a synthesis of the evidence available in the SCOPUS database. Hence, student dropout in rural higher education merits a comprehensive view of the explanatory variables which affect it, in order to move towards its prevention and mitigation by the various actors in the tertiary education subsystem, especially the State and HEIs ( Gibbs, 1998 ; Byun et al., 2012 ; Guzmán et al., 2021 ; Snyder and Dillow, 2021 ). To guide the systematised review presented here, the following research questions were proposed:

RQ1: What trends have been followed in the study of student dropout in rural higher education in terms of the characteristics of publications and methodologies?

RQ2: What progress has been made in the study of student dropout in rural higher education, based on the determinants of study (individual, socio-economic, academic, and institutional)?

Accordingly, this article is structured in four main sections. The first section describes the conceptualisation of dropout and the theoretical reference model; the second, the methodology used to achieve the objective; the third, the main findings obtained with the implementation of the methodology; and the fourth, the discussions, conclusions, and final considerations.

Dropout and the Theoretical Reference Model

When referring to student dropout, multiple meanings have been developed both by the academic community and by state and social agents, which generates diverse points of view and an enrichment of the discussion around it ( Kehm et al., 2019 ), in other words, these perceptions are not mutually exclusive. As Guzmán et al. (2021) expressed it, the multiplicity of definitions derives from specific purposes of analysis, and they have the capacity to complement each other in order to give a broader view of dropping out. In relation with the wide variety of conceptual and operational definitions of this event, this article is based on the one given by the Alpha Guidance Project. Thus, drop-out is defined as "the cessation of the relationship between the student and the training programme leading to a higher education degree before the degree is achieved. An event of a complex, multidimensional and systemic nature, which can be understood as cause or effect, failure or reorientation of a training process, choice or obligatory response, or as an indicator of the quality of the education system" (ALFA GUIA Project DCI-ALA/2010/94, 2013, p. 6).

This definition encompasses both the analysis of specific variables and of the models developed. Consequently, its use allows the theoretical framework of student dropout to be understood from a holistic viewpoint, integrating the perspectives of the academic community, state and social agents. With this integration of perspectives, the study of dropout has been carried out from a multidisciplinary orientation in which the sociological, interactionist, organisational, psychological and economic approach are highlighted ( Lázaro Alvarez et al., 2020 ), and have resulted in the analysis of variables intrinsic and extrinsic to the student, categorising them into four determinants: individual, socioeconomic, academic and institutional ( Fonseca and García, 2016 ; Barragán and González, 2017 ; Donoso and Schiefelbein, 2021 ; Guzmán et al., 2021 ).

Thus, the sociological approach sets the basis for the study of dropout in higher education, assessing the influence of external factors on the student. The contributions made by Spady (1970) explained the event in terms of Durkheim’s theory of suicide, in which it is argued that this action is the result of the subject’s disconnection from the social system, and therefore, dropout is explained as the lack of social integration of the student into the higher education environment. In addition, this approach considered variables other than social integration in the HEI such as family, expectations and demands that affect the student’s academic potential and performance.

Subsequently, the mainstream study of drop-out emerged with its basis on the interactionist and organisational approaches in which this event is explained by the student’s academic and social interaction in the HEI. An example of this approach was the model developed by Tinto (1975 ; 1987) in which the student’s emotional and intellectual background was taken as a point of reference, also involving various individual, academic, and family characteristics that directly affect the student’s permanence in the HEI. Later mainstream models, such as Bean (1986) or Heublein et al. (2010) , incorporated other related variables such as funding opportunities for tuition and other costs associated with the level of education, organisational characteristics of the HEI and student effort, thus providing a broader picture of the drop-out event.

From the psychological perspective, the student’s own characteristics and attributes were incorporated, considering aspirations, values, personality, motivation, and expectations of success, so that the individual and his or her variables associated with dropout were observed ( Ethington, 1990 ). This approach has grouped studies that include psychological aspects of the student from the perspective of the educational sciences. Prior to the research carried out by Ethington (1990) , the explanatory variables of dropout associated with the student’s psyche were not incorporated into the models, which is why the variables categorised in the individual determinant have been incorporated from his analysis. Recent studies have shown the influence of variables such as self-determination ( Jeno et al., 2018 ), personality ( Alkan, 2014 ), introversion ( Migali and Zucchelli, 2017 ) and neuroticism ( Migali and Zucchelli, 2017 ).

Finally, the economic approach has privileged the socioeconomic context of the student and his or her family by evaluating the cost-benefit ratio of staying or dropping out of higher education ( Palacio et al., 2020 ), the influence of family income on the probability of not completing the educational process ( Adrogué and García de Fanelli, 2018 ), social class as a constraint to the creation of social capital ( Palacio et al., 2020 ), among others. While most studies have focused on the student and the family, they have also assessed the impact of the drop-out event on the operational income of HEIs and their financial sustainability ( Barragán and Rodríguez, 2015 ).

Based on this interdisciplinary orientation of the study of dropout and the categorisation of the explanatory variables into the four determinants, this article is linked to the conceptual model described in Figure 1 , which has been widely used in previous research (e.g., Barragán and González, 2017 ; Klein, 2019 ; Radovan, 2019 ; Kemper et al., 2020 ; Vera et al., 2020 ; Guzmán et al., 2021 ) as well as in the development of public policies such as in the Colombian case ( Ministry of National Education, 2009 ), because it is adaptive to the educational modality or type of student population, the new realities of the higher education context, as well as allowing the development of explanatory and predictive models of dropout in higher education ( Guzmán et al., 2021 ).

FIGURE 1 . Conceptual model of determinants of dropout. Note: Each determinant groups n variables v1, v2,...,vn as exemplified in the individual determinant. A variable can have an impact on other variables in the same or a different determinant.

Thus, the determinants are conceptualised as follows:

• Individual: describes the characteristics associated with the student and his/her personal environment that have a direct influence on the decision to leave the study process unfinished. Examples of the variables related to this determinant are age, gender, marital status, position in the number of siblings, health problems at the time of enrolment at HEI, family environment, fulfilled expectations, family and personal obligations, motivation in relation to the teaching and learning process, self-regulation, and time management.

• Socioeconomic: this refers to the influence of the social and economic context in which the student is immersed, and which may lead him/her not to complete the higher education process. Among the variables related to this determinant are the stratum, the employment situation, the economic income of the family nucleus and of the student, the economic dependence, and the macroeconomic environment of the country.

• Academic: these are all those variables related to the teaching and learning process both in previous levels of education and in higher education that may lead students to drop out. Among the variables of this determinant, the following stand out: previous academic performance, courses taken before higher education, secondary school graduation tests, results of admission exams to higher education, teaching qualifications and levels of satisfaction with the academic programme.

• Institutional: refers to all the characteristics of the HEIs that allow for the correct development of the learning process and others associated with the student, which, if they generate dissatisfaction in the student, may lead him/her not to complete the learning process. Examples of explanatory variables associated with the determinant are institutional policies, funding services, pedagogical resources, the level of interaction between teachers and students, as well as academic support.

Methodology

To carry out the systematic review developed in this article, and in order to achieve the proposed objective, a mixed study was defined under the PRISMA 2020 method. This method was intended for use in reviews that include syntheses of quantitative and qualitative information ( Page et al., 2021 ). Thus, under this approach, two stages were carried out. The first related to the identification of the documents (records); and the second to the analysis and synthesis of the findings.

Stage One: Identification of the Documents and Sample Formation

In order to identify the literature with the greatest impact on higher education dropout in the rural student population, documents were searched in SCOPUS, which is a curated database of abstracts and citations of scientific documents (e.g., articles, books and conference proceedings), whose content is generally considered of the highest quality by the academic community, since each of the grouped documents is reviewed by peer reviewers and published under rigorous editorial processes ( Schotten et al., 2017 ). Thus, the equations presented in Table 1 were used to determine the search for the documents. The search was conducted in English, as SCOPUS lists titles, abstracts, and keywords in that language. In addition, other filters were not used in the search for information such as: the period of publication, the geographical area of the study and the quartile of categorisation of the journals determined by SCOPUS. This was not considered relevant because previous empirical research (e.g., Byun et al., 2012 ; Guzmán et al., 2021 ) highlighted the lack of studies in a generalized manner, for that reason it was sought to include as many studies as possible with the purpose of avoiding the loss of information. In addition, the search for documents was limited to articles, books, book chapters and conference proceedings. On the other hand, for the selection of search keywords, reference to those used in previous systematic literature reviews was made such as Orellana et al. (2021) , Guzmán et al. (2020a) , Rodriguez Urrego (2019) and Kara et al. (2019) , as well as recent empirical studies such as Guzmán et al. (2021) , Behr et al. (2020) , Kehm et al. (2019) , Barragán and González (2017) , as well as Vera et al. (2020) .

TABLE 1 . Ratio of records found by search equation.

As a result of the SCOPUS search, a total of 183 documents possibly related to the event of dropout in rural higher education were detected, which were registered in a database composed of the following data: type of document, year, authors, title of the document, journal, name of the book or conference proceedings, quartile of citation classification (only applied to journals), ISSN or ISBN, and keywords. From the documents found, a total of 69 were eliminated because they were duplicate records. Thus, with the remaining 114 records, the titles, abstracts, and keywords were read, with the intention of purging those documents not related to the topic of study, consolidating the documentary analysis sample consisting of 17 articles, one book chapter resulting from research and three conference proceedings. It is important to highlight that in the screening phase, and in order to eliminate bias in the selection of the documents, an independent review was carried out by each of the authors, evaluating the full text in the case of those documents in which the concepts were not unanimous. In addition, the PRISMA 2020 checklist was completed for each of the documents. Figure 2 shows the flow diagram of the PRISMA 2020 method.

FIGURE 2 . PRISMA 2020 method flow chart. Adapted from ( Page et al., 2021 ).

Stage Two: Analysis and Synthesis

This stage sought to analyse and synthesise the findings to fulfil the objective of this article. In this way, two phases were carried out: the first was related to the bibliometric analysis of the documents included for review using descriptive statistics and data visualisation in accordance with the parameters established by Nightingale (2009) . This phase sought to respond to RQ1. In this way, the country of origin in which the research was carried out, the frequency of publication per year, the categorisation of the articles according to SCOPUS ranking, methodologies used in the development of the studies, among others, were determined. The second phase corresponded to the content analysis of the documents, which answered RQ2, in which the explanatory variables of dropout in rural higher education were sought and associated with each of the determinants of the model described in Figure 1 . Thus, each of the sample documents was loaded into the Atlas. ti software and the open coding technique was carried out, as it allows the researcher to establish categories or variables from the reading of the documents, so it is not limited to a pre-established theoretical framework, which results in the possibility of providing answers to questions of a general nature ( Flick, 2012 ). After coding the variables, the findings were synthesised using an inductive approach.

Bibliometric Analysis

The review of the sample of papers showed that research had been carried out in ten countries of origin. Thus, seven papers related to rural people in higher education were published in the United States, two in Finland, two in Australia, and, in the case of Bangladesh, Brazil, China, Colombia, Ecuador, Norway and South Africa, one publication each. On the other hand, three of the papers in the sample did not specify the countries in which the research took place.

However, regarding the distribution of the sample by year of publication, no trend was evident, although it was observed that after 2010 the academic community’s interest in the study of the event of dropout in the population under study at the higher level has grown, accounting for 52.38% of the documents analysed since that year (see Table 2 and Supplementary Figure S1 ).

TABLE 2 . Documents in the sample under analysis.

In relation to the 19 published articles that are susceptible to categorisation by the SCOPUS indicators, only 18 of the sample had such categorisation. Of the categorised articles, 6.25% were in quartile one, 43.75% in quartile two, 31.25% in quartile three and 18.75% in quartile four. Table 2 summarises the papers in the sample, showing that by journal or conference there is no preference in the publication of research related to rural dropout at higher education level.

About the methodological approach used in previous research, it was found that 71.41% of the studies were characterised by a quantitative approach, 14.29% by a qualitative approach and 14.3% by a mixed approach. Thus, the quantitative studies, and as presented in Supplementary Table S1 there is a tendency to use the survey as the main data collection technique. In the case of qualitative studies, data collection techniques focus on interviews (in-depth or semi-structured), focal groups and workshops, and finally, in mixed studies, both surveys, in-depth interviews and focal groups are used. Regarding the sample size, most of the studies are characterised by being relatively small in comparison to the country’s population, and more specifically, those students linked to higher education in rural areas. Thus, only 29% of the studies had samples larger than 1,000 students, 62% had samples smaller than 1,000 students and 10%, being academic experiences, did not reflect a sample in their methodological section.

Variables Influencing Rural Student Dropout in Higher Education

Corresponding to the model described in Figure 1 , the results of previous research by determinant are presented below. In this sense, a total of 59 variables that have been the object of study were coded. Supplementary Table S2 presents the explanatory variables found in each of the documents. According to Supplementary Table S2 , 35% of the explanatory variables studied for dropout in rural higher education corresponded to the individual determinant, 27% to the academic, 25% to the socio-economic and 13% to the individual. Thus, in the case of the most studied variables of dropout in rural higher education in the studies analysed, they correspond to: 1. the P&GO programmes, this variable has been analysed in 10 case studies; 2. Previous academic experience, being addressed in eight case studies; 3. the state support, the family income and the labour obligations, each of these was analysed in five case studies.

However, the explanatory variables that were only identified once in the documents studied, were: adaptation to the HEI, self-learning, communication, course contents, family dysfunction, ethnicity, lack of job opportunities, academic failure, absences from classes, dissatisfaction with the programme, slow academic progress, Learning Management System, personal goals, fear of failure, motivation, death of relatives, parents' educational level, poverty, nutrition problems, scheduling problems, relationship problems with parents, racism, knowledge recognition, transfer to another university and use of ICTs.

Individual Determinant

With regards to the gender variable, it is evident in the documents analysed that rural women are more likely to drop out of higher education, a situation that has been constant over time, as evidenced by Meisalo et al. (2002) in a population of students in virtual programmes, as well as ( De Hart and Venter, 2013 ) in face-to-face education. The latter authors emphasise that gender is a good predictor of rural students' intention to drop out of higher education because women tend to be more vulnerable as a result of housework and raising children, while men who drop out tend to do so because of work obligations or because they receive material in a second language, the last variable was analysed in the rural South African population, which is characterised by a large linguistic variety.

In relation to personal obligations represented in domestic and household chores, unemployed adults tend to drop out due to the need to provide basic goods and services to their houses, leading them to limit their spending to cover these needs, reducing or eliminating investments in education, so that if the chief member of the family or any of his relatives is the one who studies, he has to drop out, due to the economic insecurity that exists in rural areas ( Nishat et al., 2020 ; De Hart and Venter, 2013 ). In the case of the work obligations of rural students, research generally agrees that the hours allocated to work compete with study hours. This was reflected by Pérez et al. (2019) when analysing the causes of desertion of a group of rural nursing students, where the greatest number of absences were due to work-related causes, affecting the academic average and influencing the student’s decision to abandon their academic process. The same situation is described by ( De Hart and Venter, 2013 ) in rural students employed in the finance sector. On the other hand, it has been established that having partial work obligations such as part-time or service jobs are related to sources of stress for the student as they do not secure sufficient resources to cover their educational and personal expenses, leading them to prioritise seeking full-time employment and sacrificing their professional career ( Pillay and Ngcobo, 2010 ).

In terms of age, research has indicated that both younger and older students located in or coming from rural areas are at risk of dropping out, however, the causes are different. In this regard, Pillay and Ngcobo (2010) identified that arguments and conflicts with and between parents led to young students not completing their academic process. On the other hand, ( De Hart and Venter, 2013 ), established that, in developing countries, young students were the first generation to enter HEIs, so that support structures such as parents, close social references and HEIs' own support structures such as SATs and P&GOs could fail to effectively address the counselling needs of those students. In the case of older students, it was observed that the main reason for dropping out of education was due to work and personal obligations ( De Hart and Venter, 2013 ; Pillay and Ngcobo, 2010 ).

Following with the support structures, especially with parents, it became clear that the educational level of the parents is significantly related to the student’s intention to continue their educational process. Bania and Kvernmo (2016) found that for rural women a higher level of parental education had an influence on the completion of pre-higher education, while for men the level of parental education was related to the completion of higher education. However, the same study argues that the educational level of parents does not have an impact on the completion of higher education among young students.

Another variable related to rural dropout in higher education is the ethnicity or social group to which the student belongs. In this sense, the language in which the study material is designed has a direct impact on the continuity of the academic process, as argued by ( De Hart and Venter, 2013 ) in identifying this case in the Nguni community in South Africa, where unfamiliarity with the learner’s culture is propitious to the materialisation of the event. Another phenomenon related to this variable is the racism that students from social groups that have historically been considered minorities may suffer at the educational level, as is the case of Afro-descendants in the United States or illegal immigrants, in which social pressure can lead to a process of demotivation and end up in desertion ( Muñoz, 2013 ; Hines et al., 2015 ).

Regarding health as an explanatory variable of dropout, studies have focused on the psychological aspects of the student, finding that rural youth with behavioural problems tend to limit the number of years of study they take, which leads them to drop out of the education system or to choose less demanding training programmes, in which the risk of dropping out is greater for students who do not have behavioural problems ( Bania and Kvernmo, 2016 ). In this scenario, it should be recognised that male rural students with particular mental health conditions are more likely to fail to complete their training programme; this is related to the lack of search for HEI support structures ( Bania and Kvernmo, 2016 ). In addition to what has been stated, Hines et al. (2015) found in their research that student mental health affects academic and social processes, being a determinant of non-completion of their studies.

What is more, it has been documented that rural students have a variety of difficulties in adapting to HEIs ( Castleman and Meyer 2020 ). This is due to the change of educational environment involving commuting, creation of new personal relationships, conflict with the size of the educational institution and new academic demands, thus leading, in the words of Castleman and Meyer (2020) , to a "shock" that may end in student dropout. This was exemplified in the study by Ramírez et al. (2020) in which they segmented rural students who dropped out of a Colombian university, and who had in common the lack of adaptation to the HEI as the main reason for the materialisation of the desertion event.

Regarding other variables, Ramírez et al. (2020) identified that the type of family can influence the non-continuation of the educational process. Students with single-parent or extended nuclear families (parents, siblings, grandparents and aunts and uncles who live together in the same house) have a greater risk of not concluding their educational process, as explained in the case of those students with work and personal obligations ( Nishat et al., 2020 ) and in the case of the latter to sources of pressure and stress derived from the family environment ( Pillay and Ngcobo, 2010 ). The death of family members or close relatives as an explanatory variable of dropout is related as a source of stress which, in conjunction with other psychological problems of the student, leads him/her to not complete the training process ( Pillay and Ngcobo, 2010 ).

In relation to individual student variables related to the learning process, Meisalo et al. (2002) found an inversely proportional relationship between rural students' dropout and their attitude towards their academic process. Similarly, the lack of student autonomy in the development of academic activities, specifically in virtual programmes, was considered a persistent contributor to the occurrence of dropout ( Meisalo et al., 2002 ), hence, P&GOs focused on strengthening student autonomy in order to mitigate dropout rates in both virtual and face-to-face training programmes ( Gildehaus et al., 2019 ). Similarly, rural students in the study developed by Lewine et al. (2019) showed higher levels of motivation leading them to complete their higher education studies, explaining this phenomenon in the equivalence of effort, thus stipulating a curvilinear hypothesis of resilience in those who face more obstacles in their higher education, as is the case of rural students, seek to have better results in their formative process due to the additional effort they have to do in order to stay linked to the HEI ( Lewine et al., 2019 ). However, fear of failure can mitigate the resilience curve, especially in the first year of study ( Pillay and Ngcobo, 2010 ). Finally, rural students' procrastination affects their academic performance and may lead them to drop out due to loss of purpose ( Warner 1993 ).

Socioeconomic Determinant

Regarding family income, research has shown that rural families are vulnerable compared to their urban counterparts, which makes this variable a predictor of student attendance at HEIs, as well as of dropout. Castleman and Meyer’s work (2020) found that students tend to come from low-income families and adverse social backgrounds, which results in high drop-out rates due to the influence of variables such as work obligations, personal obligations and high costs associated with study. This was corroborated by Ramírez et al. (2020) . In this context, and considering the family’s economic difficulties, students often take part-time or full-time jobs to cover their personal and educational expenses, however, as related by Lewine et al. (2019) paradoxically this can generate conflicts because having an additional income, the family may begin to demand the student to share their money to cover non-academic expenses, which worsens the student’s financial condition and may influence the student’s dropout. Otherwise, if the student is unable to find a job or has lost his or her job for various reasons, he or she is more likely to drop out of school. ( Muñoz, 2013 ; ( De Hart and Venter, 2013 ). However, it is necessary to recognise that in countries where social asymmetries are not so marked, as is the case in the Nordic countries, or with efficient educational policies (e.g., free tuition). that allow rural students to be linked to the higher education sub-system, the results of studies indicate that family income does not have a significant impact on student permanence ( Bania and Kvernmo, 2016 ).

On the other hand, low family income affects the student's experience at HEIs. Thus, Hines et al. (2015) noted that African American students from rural areas of the United States tended not to participate in pre- and extra-curricular paid activities which made it difficult for them to adapt to the higher education environment.

To compensate for the economic hardships faced by families, states have designed a series of public policies in the form of financial support that seek to eliminate the effect of these hardships in the event of dropout. Thus, the most common is related to the payment of tuition fees, either in the form of a scholarship or an educational credit ( Lewine et al., 2019 ). In this way, the study by Qu (2009) showed through a mathematical model that this type of support is efficient in the rural population when the financing of tuition is close to or lower than the family’s semester income, reducing the probability of dropping out, especially in the form of credit, while the opposite effect occurs when the cost of tuition is very high compared to the family’s semester income. Despite the efforts of states to link state support to students based on their legal framework, not everyone can access this type of support, such as in the case of illegal migrants located in rural areas ( Muñoz, 2013 ) or because of the student’s lack of knowledge regarding access to this support due to a lack of information ( Hines et al., 2015 ).

On the other hand, state support has only focused on economic aspects, which has meant that no other strategies have been developed to reduce dropout among rural students. An example of this was the study developed by ( Rashid and Sarker, 2008 ) in which students who worked in state entities did not find it meaningful to finish their academic programme because it did not represent a better job position or economic income, hence the authors raised the suggestion to develop new supports not concentrated on academic level tuition.

In addition to the variables described above, it was identified that rural students have problems related to finding accommodation for their on-campus studies, due to the fact that HEIs are usually located far from rural areas and when institutions have student residences they do not prioritise this type of student ( Pillay and Ngcobo, 2010 ), therefore, they are located on the outer periphery of cities where rent is usually cheaper, increasing their transport and mobilisation costs and longer distances, which results in the student’s demotivation to continue their academic programme, as well as generating greater financial pressure for them and their families ( Lewine et al., 2019 ). Similarly, rural students moving to urban areas often have nutrition problems, which is why some HEIs have developed food security plans, as expressed by Troester-Trate (2020) .

Academic Determinant

Findings related to previous academic experience can be divided into two subcategories. The first concerns the academic performance of rural students at pre-higher education levels, where a relationship has been widely established between academic performance and higher education performance in terms of average grades ( Bania and Kvernmo, 2016 ). As such, students who are better qualified in secondary school have a lower risk of dropping out at the tertiary level ( Rapley et al., 2008 ; Faizullina et al., 2013 ; Hines et al., 2015 ; Lewine et al., 2019 ; De Hart and Venter, 2013 ), as well as those with high performance in specific subjects, as was the case for natural sciences in the medical school students analysed in the study by Faizullina et al. (2013) . The second subcategory is related to disciplinary knowledge prior to the training programme, where student desertion in the rural population is directly related to the knowledge acquired in secondary school in specific undergraduate subjects. This was evidenced in the work of Meisalo et al. (2003) and Meisalo et al. (2002) in a group of engineering students, where those who had never seen programming ended their training process early.

However, with regard to the social capital acquired by rural students through their family and relatives, the literature has established that this capital is usually low due to the fact that they are the first generation to enter an HEI ( Castleman and Meyer, 2020 ), this has repercussions on various academic aspects such as performance in the absence of a rigid support structure ( Hines et al., 2015 ; Castleman and Meyer, 2020 ), or on motivational aspects ( Hines et al., 2015 ) that can lead to students dropping out of the training programme. In line with the above, rural students often have difficulties in learning due to poor academic performance at previous levels and the lack of specialised support structures for them. That said, Meisalo et al. (2002) found in a rural population in Finland that the complexity of programming course content in an engineering faculty, combined with problems of student comprehension, led to the dropout of part of the student population in the first year of training. Similarly, Nishat et al. (2020) found that class difficulty expressed in content is often one of the reasons why rural students drop out.

Regarding university average for rural students, research by Castleman and Meyer (2020) , Lewine et al. (2019) , Meisalo et al. (2003) , Meisalo et al. (2002) found that the higher the university average, the lower the likelihood of dropout. However, Castleman and Meyer (2020) noted that students in rural areas tend to enrol for fewer academic credits, which represents a lower number of courses taken per semester, resulting in a lower probability of timely graduation. On the other hand, Nishat et al. (2020) recognise that GPA can be positively influenced by P&GO when the student actively participates in additional tutoring and other services provided by these types of programmes within HEIs.

In relation to other variables, the selection of the training programme has a direct impact on rural student desertion, given that a poor choice results in a lack of motivation to continue their training process, leading them to drop out of the programme ( Pillay and Ngcobo, 2010 ; Nishat et al., 2020 ). This is due to a lack of information prior to the selection of the academic programme or family pressures ( Pillay and Ngcobo, 2010 ). Faizullina et al. (2013) reported that this variable is one of the main causes of dropout in medical schools in Kazakhstan. On the other hand, excessive academic work can lead to the phenomenon of dropout, as it competes in time with other student activities such as work and personal obligations imposed by their socioeconomic reality ( Pillay and Ngcobo, 2010 ; Pérez et al., 2021 ). In addition, some of the academic activities are not adjusted to the realities of rural students, such as the use of hardware, software, and internet to which rural students often do not have access ( Meisalo et al., 2002 ; Pérez et al., 2019 ).

On the other hand, the size of the school from which students graduated has an impact on dropout in the rural population, as observed by Wheat et al. (2003) ; students from small schools tended to leave school early. This is explained by Pillay and Ngcobo (2010) who point out that teachers in rural schools tend to have less training than urban teachers, and that the subjects taught do not cover the whole curriculum, which puts rural students at a disadvantage when entering HEIs and can lead to problems with students' academic progress ( Warner, 1993 ). Finally, absence from class due to problems with work obligations or long commutes, as well as the crossing of subject timetables, can lead to students dropping out ( Ramírez et al., 2020 ).

Institutional Determinant

The P&GO programmes have become one of the central axes to prevent and mitigate the dropout of rural students by HEIs. Thus, Warner (1993) identified how these programmes strengthen the student’s self-learning skills and autonomy to carry out their training process, which according to the author helps to reduce dropout rates. Similarly, Nishat et al. (2020) found that these programmes not only strengthened students' specific skills, but also significantly increased their GPA compared to students who did not participate in these programmes. However, the opposite effect was recorded for students who did not participate in such programmes. This may be due to a lack of student interest in participating, or to the limitations of these programmes in HEIs, which may define activities that do not fit the profile of the rural student ( Castleman and Meyer, 2020 ), or have limited channels of communication and participation ( Meisalo et al., 2002 ). However, positive results are not achieved in all areas, as demonstrated by Troester-Trate (2020) in which activities developed in P&GO programmes such as the assisted meal plans did not have an impact on student retention in HEIs. Finally, and because of the evolution of information and communication technologies in the framework of this type of programme, multiple software applications have been implemented in favour of student retention. This is reflected in the work of Oliveira et al. (2018) who documented the use of the mobile application "MobilMonitor", in addition to the use of SAT in the Learning Management System to identify students in rural areas, in a Brazilian state, who require individualised pedagogical support to make an early intervention and achieve their permanence.

In terms of communication between rural students and HEIs, the diversification of channels allows for permanence and retention, as described in Castleman and Meyer’s work (2020) in which the use of text messages was implemented in order to inform students about administrative and academic procedures to be carried out before and during the semester of study. On the other hand, in the case of virtual programmes, the absence of communication with the teacher is a predictor of desertion, since, as this academic model is based on self-learning, contact would be expected to focus on reinforcing the contents and clarifying doubts, hence HEIs with this type of training programmes seek various channels to facilitate communication between the teacher and the student ( Meisalo et al., 2002 ).

In terms of content language, some HEIs neglect the linguistic variety of rural students, especially in developing countries, which hampers the learning process ( Rashid and Sarker, 2008 ). Additionally, the requirement of a second language as a graduation requirement creates difficulties for some rural students, due to the limited competences developed at previous academic levels ( De Hart and Venter, 2013 ; Rashid and Sarker, 2008 ). Finally, it was found that the recognition of knowledge acquired by students at previous educational levels or through work experience by HEIs encourages academic retention ( Bania and Kvernmo, 2016 ).

Discussion and Conclusion

As presented in the results section, based on the systematic review, important findings were made about dropout in rural higher education. The first relates to the countries that have led research on this event in the rural student population, where the United States, Finland, Australia, and Norway stand out, which shows the interest of developed countries in understanding and determining the causes of non-continuation of studies in the rural population, and, to a lesser extent, in developing countries. In this sense, it should be noted that the results of these studies are not generalisable, since, beyond the size of the sample, in which it is evident that most of the studies are characterised by very small samples (see Supplementary Table S1 ), such as Troester-Trate (2020) , Gildehaus et al. (2019) , Hines et al. (2015) , among others; or, the type of study, there are strong social asymmetries between the economies of developing countries, which may render the findings meaningless outside the context in which the research was carried out, as stated by Guzmán et al. (2021) . On the other hand, after searching for documents in SCOPUS it was determined that dropout in the rural student population has not been of great interest to academic actors, despite the growing number of publications since 2010, as evidenced by the limited number of studies found in the period from 1993 to 2020, and that, in comparison with other systematic reviews that addressed various perspectives of dropout and where the period of analysis was shorter than the present study, fewer documents were found, as presented in the reviews by ( Orellana et al., 2021 ) ( n = 72) and ( Guzmán et al., 2020b ) ( n = 31).

The second finding concerns the variables that have been studied in the framework of determinants, in which, of the 55 variables coded, 35% corresponded to the individual, 25% to the socioeconomic, 27% to the academic and 13% to the institutional. Having said that, the studies that made up the sample concentrated their main interest on the explanatory variables of the individual and academic determinants. Moreover, the multi-causality of dropout in the rural population is recognised, since its explanation is derived from the influence of multiple variables which influence those that make up the same determinant or those of others, as was detected in the case of the variables of gender, age, work obligations, personal obligations, family income, ethnicity or social group, state support, among others. This is in line with the theoretical approach proposed in this article (see Figure 1 ) and which has been used in previous research such as those developed by Guzmán et al. (2021) , Kemper et al. (2020) and Barragán and González (2017) . On the other hand, it is necessary to recognise that there are variables that have been analysed in rural populations and not so intensively in other student populations in higher education, such as: cultural context, family dysfunction, ethnicity, the language of content, death of relatives, nutritional problems, racism, and migration status.

However, due to the limited number of studies identified in the high-impact literature related to dropout in rural higher education, there are future lines of research that can be addressed to establish explanatory or predictive models that account for the causes and high rates that occur in the rural population at the higher education level. An example of this is the study of the variables and causes that lead rural students in virtual mode to drop out, since the studies found are more than a decade old, in discordance with the evolution of this educational modality, in addition to its consolidation as one of the possibilities for access to higher education for the rural student population within the framework of public policies ( Guzmán et al., 2021 ); or, the study of variables identified in other contexts that may influence dropout at higher education level in the rural student population, and which have not been analysed, such as: armed conflict and the legal status of students ( Muñoz, 2013 ), the effectiveness of financial support ( Qayyum et al., 2019 ), learning preferences ( Aragon and Johnson, 2008 ), the level of student resilience ( Packham et al., 2004 ), commitment to the academic goal ( Choi and Kim, 2018 ; Morris and Finnegan, 2008 ), level of engagement in pedagogical teaching strategies and classroom learning ( Choi and kim, 2018 ).

Finally, both the results presented in this article and their discussion should be understood within the manifest scope of the study, such as the search limited to SCOPUS which, although it lists high-impact literature, it is necessary for future reviews to include other databases and search engines in which other documents can be found to enrich the analysis of the identified variables that indicate student dropout in rural higher education. On the other hand, the systematic review showed the relevance of prospective work on mathematical and statistical modelling that links the variables together, detecting the direct and indirect influence of the variables on the decision to drop out or persist in higher education and identifying intermediate variables that affect permanence in the education system and whose consequences are slow to manifest themselves. However, other limitations related to the method selected for the literature review include the heterogeneity of studies, the inductive analysis carried out in each of the documents, among others.

Consequently, the objective of this article was achieved, which was to identify the individual, socioeconomic, academic, and institutional explanatory variables involved in student dropout in rural populations, based on a synthesis of the evidence available in the SCOPUS database. A complementary contribution of this article is to provide a comprehensive view of dropout in the rural student population at the higher education level, which constitutes an advance for the strengthening of public and private policies of HEIs in order to prevent and mitigate the event in an effective and efficient manner, and thus consolidate the tangible and intangible benefits of higher education in the rural student population.

Data Availability Statement

The original contributions presented in the study are included in the Supplementary Material . The Supplementary Material , further inquiries can be directed to the corresponding author.

Author Contributions

AG and SB contributed to conception and design of the study. AG organized the database. AG performed quantitative and qualitative analysis. AG and SB wrote the first draft of the manuscript. AG, SB and FCV reviewed and edited. SB and FCV supervised both the development of the research and the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The University Corporation of Asturias, whose support covered the cost of the publication, and to Cecilia Carabajal who, with her unconditional support, made the style correction and translation of this article.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2021.727833/full#supplementary-material

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Keywords: dropout, higher education, rural students, systematic review, scopus, bibliometric analysis

Citation: Guzmán A, Barragán S and Cala Vitery F (2021) Dropout in Rural Higher Education: A Systematic Review. Front. Educ. 6:727833. doi: 10.3389/feduc.2021.727833

Received: 19 June 2021; Accepted: 27 August 2021; Published: 08 September 2021.

Reviewed by:

Copyright © 2021 Guzmán, Barragán and Cala Vitery. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Alfredo Guzmán Rincón, [email protected]

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Student Dropout as a Never-Ending Evergreen Phenomenon of Online Distance Education

Associated data.

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

The research on student dropout demonstrates that there is no consensus on its definition and scope. Although there is an expanding collection of research on the topic, student dropout remains a significant issue, characterized by numerous uncertainties and ambiguous aspects. The primary aim of this investigation is to assess the research trends of student dropout within the distance education literature by employing data mining and analytic approaches. To identify these patterns, a total of 164 publications were examined by applying text mining and social network analysis. The study revealed some intriguing facts, such as the misinterpretation of the term “dropout” in different settings and the inadequacy of nonhuman analytics to explain the phenomenon, and promising implications on how to lessen dropout rates in open and distance learning environments. Based on the findings of the study, this article proposes possible directions for future research, including the need to provide a precise definition of the term “dropout” in the context of distance learning, to develop ethical principles, policies, and frameworks for the use of algorithmic approaches to predict student dropout, and finally, to adopt a human-centered approach aimed at fostering learners’ motivation, satisfaction, and independence to reduce the rate of dropout in distance education.

1. Introduction

Education has always been viewed as the key to the growth, development, and well-being of a society, and it has become even more of a prerequisite for citizens since the world is continuously evolving and the global challenges are getting more and more difficult to handle in everyday life. Recent advances in technology have made continued education and lifelong learning easier by allowing institutions to reach millions of students in a quicker and more resourceful way. This notion is also articulated in the Sustainable Development Goals, Number 4: Quality Education to provide “quality education for all is fundamental to creating a peaceful and prosperous world” [ 1 ]. Consequently, educational institutions globally have begun providing distance education courses to address this demand. Distance education has revolutionized learning by filling the geographical gap between institutions and learners [ 2 ] and, thanks to its flexible nature, offered people who have job or family commitments the opportunity to achieve their educational goals since its emergence [ 3 ].

However, despite the growing demand for distance learning, institutions suffer low retention rates within their distance learning environments [ 4 ], and it is not uncommon for learners to drop out, whether willingly or unwillingly, from these programs at some point. Furthermore, there is no single agreed-upon definition of the term “dropout”. For instance, Tinto [ 5 ] defines “dropout” as any person leaving their institution, while Kaplan et al. [ 6 ] state that dropouts are those who leave their departments voluntarily after the payment of the tuition fee is completed and/or the add/drop period is over. Conversely, Levy [ 7 ] presents a differing perspective, positing that students who choose to withdraw from a course within the “add/drop period” should not be classified as dropout students. This is due to the fact that they either receive a full refund for their tuition or do not face any financial repercussions for discontinuing the course during this specific time.

Although dropouts pose a major concern for all types of education, the dropout rate in distance education is much higher than that in traditional education, as Moore and Kearsley [ 8 ] have made clear. This, however, comes as no surprise when the spatial, temporal, and transactional distances separating the common target learner populations of distance education are considered. Providing educational opportunities to a relatively higher number of learners from any social, academic, or economic background, then, can become a downside, as high rates of dropout can be linked to a variety of issues in such a diverse context, which might prevent institutions from fully understanding and addressing the causes of dropout. Within the above-mentioned context, the overall purpose of this study is to examine the research trends and patterns of student dropout in distance education systems and to identify the emerging thematic research patterns.

Despite student dropout being a central concern for educational stakeholders since the inception of formal education systems [ 9 ], it was not until the early 1970s that theoretical frameworks began to emerge. The initial research aimed at understanding student dropout primarily centered on students’ physiological characteristics, including aspects such as personality, capabilities, and motivation [ 9 ], as well as personal deficiencies [ 9 , 10 , 11 ]. These investigations, categorized as psychological studies [ 9 , 12 ], were succeeded by additional theoretical models and research efforts that sought to elucidate the phenomenon from various angles, including psychological, environmental, sociological, economic, and organizational perspectives [ 9 , 10 ].

Since 1970, the phenomenon has been widely investigated, and various conceptual and theoretical models [ 5 , 11 , 13 , 14 , 15 , 16 , 17 , 18 ] have been put forward to explain the issue. These models aimed to help institutions identify at-risk students and present some implications for early interventions that practitioners and curriculum designers should be mindful of. Except for these models, there have been many other preliminary studies, most of which focus primarily on factors affecting student dropout.

In an attempt to reveal what has been said about the issue at a glance and provide a robust interpretation of all the relevant empirical evidence, there have also been several literature review studies. The first systematic review of dropout studies in the context of online learning was conducted by Lee and Choi [ 19 ]. In their seminal study, Lee and Choi [ 19 ] conducted a comprehensive analysis of 35 empirical research articles, exclusively from peer-reviewed academic publications, spanning the decade between 2000 and 2010. In order to find related studies, Lee and Choi [ 19 ] both searched for the keywords that might relate to relevant studies in three major educational databases: Education Research Complete, ERIC, and PsycINFO, and employed a snowball method. In their investigation, Lee and Choi [ 19 ] utilized the research classification frameworks established by Creswell, [ 20 ] and initially presented the authors, publication years, study samples, contexts, and dropout definitions pertaining to each individual study. Later Lee and Choi [ 19 ] focused on the factors affecting dropout and explained the phenomenon under categories such as student factors (academic background, relevant experiences, skills, and psychological attributes), course/program factors (course design, institutional support and interactions), and environment factors (work commitments and supportive environments).

Hart [ 21 ] presented a pioneering literature review that examined the factors potentially influencing students’ persistence in online courses. To select the relevant articles, Hart [ 21 ] employed the following criteria: (a) publication after 1999, (b) appearance in a peer-reviewed journal, and (c) the discussion of student factors promoting persistence. After evaluating 11 papers, Hart [ 21 ] offered a deeper understanding of persistence as a phenomenon, as well as identifying factors that encourage persistence and obstacles that hinder it.

In a more contemporary examination of the subject, Muljana and Luo [ 22 ] conducted a comprehensive review of the existing literature. Their investigation involved an analysis of 40 empirical studies published between 2010 and 2018, utilizing extensive database searches, abstract screenings, full-text evaluations, and synthesis procedures. Acknowledging the multifaceted and intricate nature of student dropout, Muljana and Luo [ 22 ] identified a plethora of contributing factors, such as institutional support, program difficulty, fostering a sense of belonging, enhancing learning, course structure, student behavior traits, demographic aspects, and other personal variables. Additionally, they found that prior academic performance, age, gender, personal circumstances, and abilities in online and self-directed learning emerged as the most prominent determinants of online dropout [ 22 ].

Pant et al. [ 23 ] were among those who aimed to provide a comprehensive picture of the literature on student dropout in online learning environments. However, Pant et al. [ 23 ] examined studies that focused on persistence specifically in MOOCs and were published between the years 2011 and 2020. In their analysis, Pant et al. [ 23 ] selected 50 research papers that investigated factors increasing student motivation and thus contributing to their persistence in MOOCs. The findings of this study demonstrated that learner-related factors, classified as personal, social, educational, and industrial, played a vital role in determining student enrollment in MOOCs.

Although not exactly the focus of this study, dropout has become more evident as an important problem in higher education during the COVID-19 pandemic. During the emergency remote teaching and learning practices implemented during the pandemic [ 24 , 25 ], it was observed that learners chose to drop out due to psychological, pedagogical, and socio-economic reasons. Among these reasons, psychological issues such as engagement and motivation were salient [ 26 , 27 ].

As can be seen in the literature, there have been many attempts to investigate students’ dropping out from online learning environments. Some of these studies were systematic reviews and contributed greatly to the existing literature since they delivered a rigorous summary and comparison of several primary studies and, therefore, addressed research questions better than a single study might do. Nevertheless, a key problem with much of the literature on the phenomenon is that none of the studies that tend to review the past literature have applied data mining and analytics methods. Employing such methods would help a study identify patterns and relationships in large volumes of data extracted from a great number of sources and represent them in a more meaningful way.

When conducting research in any discipline, it is important to identify the gaps and trends, determine the scholarly ground, and act accordingly. As reported in the literature review section, the existing research indicates that the concept of dropout in distance education is extensive, multifaceted, and multidimensional. Consequently, it is vital to comprehend the dropout phenomenon in distance education by exploring what has been previously discussed in order to gain deeper insights into potential new perspectives. In light of these considerations, the objective of the current study is to scrutinize dropout trends in the realm of distance education research. To achieve this, the study aims to address the subsequent research inquiry:

• What are the thematic research patterns for dropout studies in the field of distance education?

2. Materials and Methods

2.1. research design.

This study employed data mining and analytics approaches [ 28 ] to examine the articles on dropout from the perspective of distance education. In addition to descriptive statistics, tSNE analysis [ 29 ], social network analysis (SNA) [ 30 ], and text mining approaches [ 31 ] were used. Titles of the articles were analyzed using t-distributed stochastic neighbor embedding (t-SNE) to visualize “high-dimensional data by giving each datapoint [words in titles and abstracts] a location in a two [sic] or three-dimensional map” [ 29 ]. SNA [ 30 ] of the keywords was performed to better identify thematic clusters and significant nodes with strategic positions in the keyword network. In this analysis, each keyword identified by the authors of the article was considered a node, and its co-occurrences were considered relationships. By adopting text mining [ 31 ] through the lexical analysis of the titles and abstracts, the researchers were able to visualize a thematic concept map and identify major themes emerging from the research corpus. By applying different data mining and analytics approaches, the data could be triangulated, thereby increasing the validity and reliability of the study [ 32 ].

2.2. Inclusion Criteria and Sampling

In order to obtain a broad view, Scopus, which is the largest scholarly database, was selected for the sampling of the articles and proceedings. The research corpus was created by sampling publications that met the following inclusion criteria: (1) indexed in the Scopus database, (2) written in English or translated into English, and (3) presence of search query items in the title of the articles and proceedings ( Table 1 ). The search yielded a total of 164 publications (80 articles and 84 proceedings).

Search queries used for the inclusion criteria.

Throughout the sampling and research corpus creation processes, this study employed the PRISMA protocol, which pertains to the preferred reporting items for systematic reviews and meta-analyses ( Figure 1 ) [ 33 ]. Ultimately, a total of 164 publications were encompassed in the final phase.

The PRISMA Protocol.

2.3. Data Collection Tools and Data Analysis Procedures

The data were crawled from the Scopus database, analyzed, and visualized by applying different data visualization tools. The study included a total of four phases. In the first phase of the study, time trends of the sampled studies were identified using descriptive statistics. In the second phase, the patterns in the titles were visualized by applying tSNE analysis [ 29 ]. In the third phase, the abstracts of the studies were analyzed using SNA [ 30 ] and text mining approaches [ 31 ], followed by identification of patterns and visualization of these patterns on a thematic concept map. In the final phase, using SNA [ 30 ], the keywords were analyzed based on their centrality metrics and then visualized on a network graphic.

2.4. Strengths and Limitations of the Study

The main strength of this study is its ability to use different data mining analytics approaches, which allowed for the visualization of the findings and identification of the research trends and patterns in an easily observable manner to facilitate analysis of the large volume of textual data. On the other hand, the study also has some limitations. First, the study examines publications that are only indexed in the Scopus database. However, publications not indexed in Scopus could have provided further insights into the research in question. Second, the study only examines peer-reviewed articles and proceedings, under the assumption that these provide the most robust and reliable findings. However, different types of publications (e.g., books, book chapters, reports, etc.) could have provided invaluable complementary findings. Third, while data mining techniques are considered a strength, they also have some limitations, and we acknowledge that our findings can only provide a partial view.

In the subsequent section, the findings from various analyses are presented, including time trend examination, tSNE evaluation of the titles, text mining of abstracts, and social network analysis (SNA) of the keywords.

3.1. Time Trend

When the time trends of the publications are examined, it can be seen that, while there is a slow but steady increase by the first decade of the 2000s, the number of the publications increases from 2010 onward and reaches its peak by 2020 ( Figure 2 ).

Time trends of the publications on dropout in distance education.

3.2. tSNE Analysis of the Titles

To identify the general focal point of the publications on dropout in distance education, a tSNE analysis, an unsupervised “nonlinear dimensionality reduction technique that aims to preserve the local structure of data” [ 29 ], was conducted. The analysis indicated that the publications distinctly focused on dropout in MOOCs (see the lower left corner in Figure 3 ), with a specific emphasis on predicting dropouts using different machine-based statistical models. The initial findings that emerged in tSNE analysis align with the insights gained from the time trends analysis.

tSNE analysis of the titles in the publications on dropout in distance education.

3.3. Text Mining of the Abstracts and SNA of the Keywords

In order to discern the research trends within the 164 publications addressing dropout in distance education, text mining techniques were employed to reveal patterns in the abstracts (see Figure 4 ), and SNA was utilized to uncover patterns in the keywords (see Figure 5 ). As a result, three overarching themes were identified through the triangulation of text mining and SNA findings. In this section, each theme was presented with evidence from text mining and SNA, and the entitled themes manifested themselves. Following that, we have explained these themes and discussed them in the next section by comparing and contrasting the related literature.

Thematic concept map of the abstracts in the publications on dropout in distance education.

Network graph of the keywords in the publications on dropout in distance education.

Theme 1: On defining dropout in MOOCs (see path in Figure 4 : flexibility, MOOC, problem, completion, rate, massive open challenges, attrition, online, and massive; see nodes in Figure 5 : lifelong learning, dropout rates, and MOOCs).

Theme 2: Non-human analytical data mining approaches to predict dropout (see path in Figure 4 : learner, MOOC, behavior, prediction, data, model, and methods; see nodes in Figure 5 : distance education, open and distance learning, dropout, learning analytics, educational data mining, machine learning, predictive analytics, data mining, and student dropout prediction).

Theme 3: Interaction, satisfaction, engagement, and personalization to reduce dropout rates (see path in Figure 4 : design, interaction, dropout, learning and support, student, distance, academic, e-learning, persistence and approach, learning, dropout, and at-risk; see nodes in Figure 5 : success, personalization, intention, design, academic locus of control, students satisfaction, e-learning, online learning, instructional design, students attrition, engagement, online education, distance learning, and higher education).

4. Discussion

4.1. time trend.

The first paper in the research corpus, written by Thompson [ 34 ] and published in 1984, addressed dropout in distance education from the perspective of the cognitive style of field dependence. Other earlier papers also used an exploratory research design. For instance, Sweet [ 35 ] examined dropout to validate Tinto’s model [ 5 ] for adult distance education students. The third paper in the research corpus, which was written by Garrison [ 36 ], noted that the field needs to go beyond simply descriptive studies and focus more on comprehensive and advanced research to better understand dropout in distance education. The first publications were explorative, and they highlighted the significance of the dropout phenomenon in distance education. In the 1990s, research on dropout began to attract some attention [ 37 ], which continued at a slow but steady pace until 2010. After 2013, there was a pronounced increase in the number of studies on dropout, and by 2017, the amount of research on dropout in distance education doubled before reaching its peak in 2020. Examination of the research corpus further showed that following the emergence of the first generation cMOOCs, the second generation of xMOOCs gained a lot of popularity [ 38 ], with the high dropout rates in MOOCs turning the spotlight on MOOC research [ 39 ]. For instance, publications on MOOCs and dropout [ 40 , 41 ] that focused on predictions using educational data mining [ 42 , 43 , 44 ] and publications that specifically focused on dropouts in online distance education [ 45 , 46 ] drew much attention. The findings indicate that the emergence of MOOCs and their popularity along with the emergence of online distance learning as a frequently used model in mainstream education further triggered the research on dropouts from the perspective of distance education. Another interesting finding is that most of the publications focused on predicting dropout behavior using educational data mining.

4.2. Research Patterns

This section provides a discussion of the research patterns. More specifically, it identifies the research themes through text mining of the abstracts and SNA of the keywords.

4.2.1. On Defining Dropout in MOOCs

A major flaw in the studies conducted to explore the reasons for students’ dropout from online environments is the lack of a proper definition of the term dropout. Previous attempts to come up with a precise definition have fallen short in many cases. In their comprehensive review, Lee and Choi [ 19 ] noted that many of the studies examined provided no general agreement on the definition of dropout from online courses, which made it quite challenging to compare dropout factors across different learning environments. Thus far, dropout students have been characterized in a variety of ways, including those who did the following:

• Departed from their institution for some reason [ 5 ];
• Voluntarily left their departments after finalizing tuition fee payments and the conclusion of the drop/add period [ 6 ];
• Did not register following three consecutive terms of non-enrollment [ 47 ];
• Earned a grade of F or formally withdrew from the course [ 48 ];
• Enrolled in a minimum of one module but failed to submit a single project [ 49 ];
• Were unable to complete a course during a semester [ 50 ];
• Went through the official withdrawal procedure [ 51 ];
• Opted to withdraw from e-learning, incurring financial penalties [ 7 ];
• Either withdrew or were dismissed from the program [ 52 ];
• Failed to meet the program requirement of completing two courses per year [ 53 ].

As for MOOCs, which globally offer open and flexible learning experiences for a large body of learners [ 54 ], the challenge to answer the question of whether every dropout is an actual dropout becomes more prominent [ 55 ]. According to Zheng et al. [ 56 ], some MOOC learners may only be interested in understanding particular concepts or some content rather than passing exams or achieving certificates, which may result in their leaving after acquiring the corresponding knowledge. Hence, previous attempts to define dropout seem to be inadequate and inconsistent due to the variety of ways that students join and leave MOOCs [ 57 ]. Furthermore, as Astin [ 58 ] stated, drawing a sharp distinction between dropouts and non-dropouts could be problematic in many ways because as long as the students in question are alive, they may return to college.

4.2.2. Non-Human Analytical Data Mining Approaches to Predict Dropout

As MOOCs have grown to include a massive number of students, effective machine learning models of complex student behavior patterns that identify at-risk students are needed [ 59 ]. The benefits of a diagnosis at an early stage include the ability to provide immediate intervention for at-risk students and determine in advance whether at-risk students lack interest in the teacher, the course, or MOOC itself [ 60 ]. Existing studies have placed great emphasis on supervised learning algorithms to build discriminative models that are capable of predicting dropout [ 61 , 62 ]. As Henrie et al. [ 63 ] suggested, the log data of learning behaviors collected by MOOC platforms where viewing is the basic learning behavior have been used to analyze dropout rates. Nonetheless, research predominantly utilizing clickstream data to construct a flow network model of collective attention in order to examine dropout learning patterns prompts numerous inquiries concerning the sufficiency of non-human analytics for exclusively explaining this phenomenon.

The literature reveals that the reasons for student dropout may vary. Previous studies explaining dropout reasons on personal grounds have highlighted the significance of psychological attributes, such as motivation [ 52 , 64 , 65 , 66 , 67 ], self-efficacy [ 52 , 67 ], and satisfaction [ 7 , 48 ]. Moreover, Perry et al. [ 53 ] point out that unexpected life events, such as health problems encountered by the student or a family member, death of a family member, or unplanned financial pressures, may also lead to dropout. In such unanticipated circumstances, misleading or inadequate data can emerge when learning algorithms rely too heavily on learner logs.

4.2.3. Interaction, Satisfaction, Engagement, and Personalization to Reduce Dropout Rates

The results emphatically indicate the need for implementing strategies to minimize learning obstacles and tackle the potential causes for withdrawal. The creation of supportive environments and provision of encouragement are deemed essential factors [ 4 ], irrespective of their origin. Support from various sources, such as family or friends [ 18 , 21 , 52 , 65 , 67 ] or the institution itself [ 64 , 68 ], considerably impacts students’ perseverance. Another crucial element is course satisfaction, as evidenced by multiple studies [ 7 , 48 ]. The level of satisfaction derived from e-learning plays a pivotal role in students’ decisions to either complete or withdraw from online courses [ 7 ]. Although benefitting from its most prominent characteristic of no time and space boundaries [ 69 ], distance education suffers from the negative impacts it has on transactional distance. Transactional distance, described as the degree of psychological distance between the learner and teacher by Moore [ 70 ], needs to be considered to improve learner satisfaction. Another dimension to consider is motivation. Motivated, goal-oriented, and engaged students tend to show better persistence in online learning [ 52 , 65 , 66 , 71 ]. Furthermore, previous studies have shown that the decision behind dropout in some cases lies in students’ locus of control [ 51 , 66 , 72 , 73 ]. However, there is no single factor on its own that can be held responsible for student dropout. Rather, it is a combination of different factors in different settings that results in non-persistence. Therefore, this brings attention to the significance of instructional design as an aspect of early intervention. According to Chyung et al. [ 74 ], an effective online learning environment, where student participation is high, involves well-designed curriculum and course materials that meet expectations and contribute to student satisfaction and motivation, accordingly. When the course is tailored to specific career goals and individual learning styles, as Perry et al. [ 53 ] suggest, the students will be less likely to drop out.

In all, the focal point of the aforementioned three themes is that dropout is a multidimensional and multilayered issue that has contextualized traits ranging from the way it is defined, the learning design (learner agency and personalized learning), the learning needs (e.g., formal learning requirements as in the higher education or lifelong learning needs as in the case of MOOCs), and socio-economic background (e.g., time needed, working status, family responsibilities, etc.) along with psychological status (e.g., satisfaction, motivation, engagement, etc.). This implies that the dropout equation has many variables and, to identify these variables and provide possible solution scenarios, future research can approach the research in question from different perspectives.

5. Conclusions and Suggestions

The issue of student dropout in distance education systems presents significant challenges for educators, administrators, and policy-making communities. By exploring the research trends and patterns of dropout in distance education systems using data mining and analytics methodologies, this study offers valuable theoretical and practical insights to enhance the current understanding of learner retention in online networked learning environments.

Dropout is a relative term, interpreted differently by different researchers, institutions, and educational systems. In most cases, intervention strategies that work for one system will not necessarily be applicable for another. Likewise, due to the sui generis feature of each online learning system, the reasons given for dropout may be unique to the program. Therefore, in order to avoid misinterpretation and to be able to come up with a proper diagnosis, a precise definition of the term “dropout” in the context of distance education is needed.

Another thing to consider is the excessive emphasis on supervised learning algorithms in building discriminative models to predict dropout. Considering the massive target populations of distance education programs, which include learners from any social, academic, or economic background, prediction models based only on learning algorithms may fall short because of their neglect of unanticipated circumstances of human nature. What is more, these models may lead to the exclusion of learners believed to be prone to dropout and leave them with no choice but failure. Thus, there is a need to develop ethical principles, policies, and frameworks regarding the use of algorithms to predict dropout in distance education. It should also be kept in mind that there may be other psychological and sociological reasons for dropping out of school, and in this context, it should be highlighted that algorithmic solutions cannot identify such reasons.

The findings of this study correlate fairly well with previous works and further support the idea that dropout from distance learning may arise from various factors. What seems to be fundamental to note here is that neither a single factor can be held responsible for student dropout, nor can a single formula ensure persistence. Rather, educators, institutions, and designers of online programs should adopt a human-centered approach that can foster learners’ motivation, satisfaction, and independence. In the end, learning is supposed to be a social process that requires giving more agency to learners and supporting them socially and academically to minimize the dropout rates.

Motivation can be introduced as an umbrella term for many factors, as it can be linked to many other factors to some extent. When learners are more motivated, it can eliminate both environmental- and student-related factors, since the first would better help them balance their family, work, and study life, and the latter would assist them in gaining self-regulation and self-efficacy skills. In order to increase motivation, learners should receive a considerable amount of encouragement, support, user-friendly learning instructions/interface, relevant content, hands-on experience, and guidance. Lastly, one of the most outstanding features of distance education, “learning at one’s own pace”, should be exploited to improve learner independence and autonomy. By doing so, learners can master their time management and workload.

Funding Statement

This research was funded by Anadolu University, grant number 2208E119 and 2207E099.

Author Contributions

Conceptualization, S.E. and A.B.; methodology, A.B.; software, A.B.; formal analysis, A.B.; investigation, S.E. and A.B.; data curation, A.B.; writing—original draft preparation, S.E. and A.B.; writing—review and editing, S.E. and A.B.; visualization, A.B.; supervision, A.B.; project administration, A.B.; funding acquisition, S.E. and A.B. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Trump Trial Illustrates Jury Challenges in the Social Media Age

FILE PHOTO: Former U.S. President and current Republican presidential candidate Donald Trump enters his trial after a lunch break at Manhattan Criminal Court in New York, New York, USA, 19 April 2024. Trump is facing 34 felony counts of falsifying business records related to payments made to adult film star Stormy Daniels during his 2016 presidential campaign. SARAH YENESEL/Pool via REUTERS/File Photo

By Jody Godoy

NEW YORK (Reuters) - Donald Trump's criminal trial in New York has barely begun but one of the highest-profile court cases in U.S. history has already highlighted the challenges of insulating a jury from social media.

As opening statements are set to begin in New York on Monday, the salacious case involving a hush-money payment to a porn star -- the first criminal trial of a former U.S. president -- will test the limits of what a judge can control.

To keep the trial fair and jurors safe from intimidation or influence schemes, the court aims to maintain the secrecy of their identities, shield them from online attacks and ensure they are not swayed by coverage and social media comments. 

But Justice Juan Merchan has virtually no ability to police what is posted by most users on social media. According to

Pew Research in 2023, 90% of U.S. adults own smartphones, and the same percentage say they are online every day.

"In some ways the social media aspect of the case makes those concerns even more serious," Manhattan criminal defense attorney Michael Bachner said.

Trump has millions of online followers, and some were behind death threats to election workers after he lost the White House in 2020.

Merchan sought to keep prospective jurors' identities concealed during jury selection. Their names were not disclosed except to Trump, his lawyers and prosecutors.

Merchan soon prohibited media outlets from reporting the potential jurors' employment after excusing a juror who said she felt intimidated because some personal details were made public.

One person whose online speech Merchan believes he should be able to control is the defendant himself. He has ordered Trump not to make public statements about jurors, prosecutors and court staff or their families, though he is free to air his thoughts on the judge and district attorney. 

Prosecutors have accused Trump of violating the order multiple times, including in a Truth Social post on the jury pool. "They are catching undercover Liberal Activists lying to the Judge in order to get on the Trump Jury," Trump posted.

Merchan has scheduled a Tuesday hearing on those claims. Trump has said it would be an "honor" to be jailed for violating the order.

AVOIDING COVERAGE

As the trial progresses, jurors also must try to comply with a court order to avoid coverage of the case, including on social media and mobile devices.

"To be honest, my generation is on social media a lot," one potential juror told Merchan. "So, if I'm scrolling, I usually see it." But she assured the judge that she could avoid reading headlines about the case that popped up on her phone.

In a recent civil case involving former Republican vice presidential candidate Sarah Palin, jurors learned of a decision by the judge to dismiss the case from news alerts.

Christina Marinakis, chief executive at trial consulting firm Immersion Legal, said the barrage of headlines and social media notifications on jurors' phones has been a huge problem in her cases.

"This is another reason we have alternates, because somebody is going to see something during the course of the trial that may cause them to get dismissed," she said.

Six alternate jurors were selected for the Trump criminal trial.

The court could turn to a tactic most famously used in trials of organized crime figures to prevent jury tampering: sequestering jurors. 

While technically an option in this case, it's not one judges invoke lightly given the extreme disruption it would mean for jurors to live in a hotel under court supervision. 

"It's kind of a dire thing to do. You have to really have a good reason to do it," Bachner said.

(Reporting by Jody Godoy in New York; editing by Tom Hals and Cynthia Osterman)

Copyright 2024 Thomson Reuters .

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