Learning Engagement and Persistence in Massive Open Online Courses (MOOCS)

By Yeonji Jung, Jeongmin Lee. Comput. Educ., 2018

471 citations recorded by the source corpus.

Abstract The purpose of this study was to investigate how to facilitate learners' engagement and persistence in massive open online courses (MOOCs). Specifically, this study used structural equation modeling to examine the structural relationships among academic self-efficacy, teaching presence, perceived usefulness, and perceived ease of use, learning engagement, and learning persistence in MOOCs. For the data analysis, we selected as the research subjects 306 learners who were taking MOOCs in South Korea. The results indicated that academic self-efficacy, teaching presence, and perceived usefulness had significant direct effects on learning engagement. Furthermore, teaching presence and perceived ease of use had direct effects on learning persistence. Finally, learning engagement had indirect effects on the relationships between academic self-efficacy, teaching presence, perceived usefulness, and learning persistence. These findings suggest implications for designing and developing effective instructional and learning strategies in MOOCs in terms of learners’ perceptions of themselves, instructors, and learning support systems.

Subfields: Computer Science Applications, Education, Information Systems

View Paper: https://www.semanticscholar.org/paper/36d091480cd40b608e25833d86c0c8d5c9950b21

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In Computer Science Applications

M. Yağcı

In Information Systems

Citations in the corpus

Predicting academic performance of students from VLE big data using deep learning models (2020, background): “Assessment engagement pattern is considered to be another parameter that effectively captures the behavior of students and induces a positive impact on their performance (Hussain et al., 2018; Jung & Lee, 2018).”
Trends in Educational Research about e-Learning: A Systematic Literature Review (2009–2018) (2020, background): “The themes approached in the articles are the following (Figure 12): MOOC learners, where researchers explore demographic factors of online students, as well as barriers, potentiators, and psychological profiles [96,97]; MOOC participation, with studies that reveal the importance of expectations and”
Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores (2018, background): “As student participation in course activities increases, the experiences become more engaging, and the probability of a student achieving a high assessment score and completing the e-learning course increases [7, 8].”