Open AccessDropout in online higher education: A scoping review from 2014 to 2018
Author(s) -
Julio Meneses,
Marlon Xavier
Publication year - 2020
Language(s) - English
DOI - 10.7238/uoc.dropout.factors_4.2020
Subject(s) - dropout (neural networks) , terminology , consistency (knowledge bases) , psychology , intervention (counseling) , field (mathematics) , higher education , knowledge management , data science , computer science , political science , philosophy , linguistics , mathematics , artificial intelligence , machine learning , psychiatry , pure mathematics , law
Online higher education continues to grow, yet its high dropout rates remain a pressing and complex problem. This article presents a scoping review of the recent literature on the theme, focusing on dropout definitions, concepts, and models, study domains and themes, methodological approaches, and findings. A search of relevant databases yielded 138 articles and dissertations. Findings reveal a complex yet disorganized field, lacking standard definitions and models. The bulk of current research is focused on risk factors; the most important ones were course and program factors (student support), student factors (motivation, time management skills, and satisfaction), and environmental factors (time- and financial-related issues). Future research should strive to achieve greater consistency in terminology, methods, and measurement, develop new intervention strategies and produce reliable effectiveness information. Further implications of these findings for future dropout research and the limitations of the study are discussed.