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A Review of Online Course Dropout Research: Implications for Practice and Future Research
Youngju Lee,Jaeho Choi +1 more
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In this paper, the authors identified 69 factors that influence students' decisions to dropout and classified them into three main categories: (a) Student factors, (b) Course/Program factors, and (c) Environmental factors.Abstract:
Although online learning is expanding in availability and popularity, the high dropout rates remain a challenging problem. In this paper, we reviewed the existing empirical studies on online course dropouts in post-secondary education that were published during the last 10 years. We identified 69 factors that influence students’ decisions to dropout and classified them into three main categories: (a) Student factors, (b) Course/Program factors, and (c) Environmental factors. We then examined the strategies proposed to overcome these dropout factors: (a) understanding each student’s challenges and potential, (b) providing quality course activities and well-structured supports, and (c) handling environmental issues and emotional challenges. Finally, we discussed issues regarding dropout factors and strategies for addressing these factors and offered recommendations for future research.read more
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Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction
TL;DR: Demographic variables, such as the number of online courses taken, gender, and academic status were found to predict online learning self-efficacy and it was found that online learning Selfefficacy predicted students' online learning satisfaction.
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Self-regulation in online learning
Moon-Heum Cho,Demei Shen +1 more
TL;DR: This article examined the role of goal orientation and academic self-efficacy in student achievement mediated by effort regulation, metacognitive regulation, and interaction regulation in an online course and showed the importance of individual students' intrinsic goal orientation in academic achievement.
Dropout Prediction in MOOCs using Learner Activity Features
TL;DR: In this paper, a dropout predictor that uses student activity features to predict which students have a high risk of dropout is presented, and the predictor succeeds in red flagting 40% - 50% of dropouts while they are still active.
References
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Journal ArticleDOI
Generalized expectancies for internal versus external control of reinforcement.
TL;DR: In this paper, the effects of reward or reinforcement on preceding behavior depend in part on whether the person perceives the reward as contingent on his own behavior or independent of it, and individuals may also differ in generalized expectancies for internal versus external control of reinforcement.
Journal ArticleDOI
Dropout from Higher Education: A Theoretical Synthesis of Recent Research
TL;DR: The failure of past research to delineate more clearly the multiple characteristics of dropout can be traced to two major shortcomings as mentioned in this paper, namely, inadequate attention given to questions of definition and to the development of theoretical models that seek to explain, not simply to describe, the processes that bring individuals to leave institutions of higher education.
Research design: Qualitative, quantitative, and mixed methods approaches, 3rd ed.
TL;DR: A Framework for Design Three Elements of Inquiry Alternative Knowledge Claims Strategies of Inquiry Research Methods Three Approaches to Research Criteria for Selecting an Approach Personal Experiences Audience Summary Writing Exercises Additional Readings.
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Factors Influencing Adult Learners' Decision to Drop Out or Persist in Online Learning
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