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Analyzing Log Data of Students Who Have Achieved Scores Adjacent to the Minimum Passing Grade for a K-MOOC Completion in the Context of Learning Analytics

Author

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  • Sunyoung Kim

    (Faculty of Liberal Education, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea)

  • Taejung Park

    (Department of Lifelong Education and Counseling, College of Future Convergence, Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Korea)

Abstract

Collecting and analyzing log data can provide students with individualized learning to maintain their motivation and engagement in learning activities and reduce dropout in Massive Open Online Courses (MOOCs). As online learning becomes more and more important, the demand for learning analytics is surging to design a variety of interventions that can achieve learning success and achieve individual learning goals and targets. In response to significant demand, we intended to derive data standards for learning analytics by specifying more the factors influencing MOOC completion suggested in previous research results. Therefore, this study aims to compare the event logs of students who have achieved scores adjacent to the minimum passing score of Korean Massive Open Online Course (K-MOOC) completion by dividing them into the completion (C) group and the non-completion (NC) group. As a result of analyzing the log data accumulated on the 60 K-MOOCs, what is interesting in the results of this study is that there was no significant difference between the C group and the NC group in video viewing, which is considered the main learning activity on the MOOC platform. On the other hand, there was a statistically significant difference between the C group and the NC group for textbook interactions in the percentage of learners who performed and the average number of logs per learner, as well as problem interactions in the average number of logs per learner. Students’ assertive activities such as textbook interaction and problem interaction might have greater value for MOOC completion than passive activities such as video watching. Therefore, MOOC instructors and developers should explore more specific design guidelines on how to provide problems with individualized hints and feedback and offer effective digital textbooks or reference materials for the large number of students. The results suggest that collecting and analyzing MOOC students’ log data on interactions, for understanding their motivation and engagement, should be investigated to create an individualized learning environment and increase their learning persistence in completing MOOCs. Future studies should focus on investigating meaningful patterns of the event logs on learning activities in massive quantitative and qualitative data sets.

Suggested Citation

  • Sunyoung Kim & Taejung Park, 2022. "Analyzing Log Data of Students Who Have Achieved Scores Adjacent to the Minimum Passing Grade for a K-MOOC Completion in the Context of Learning Analytics," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11136-:d:907866
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    References listed on IDEAS

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    1. Ling Wang & Gongliang Hu & Tiehua Zhou, 2018. "Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education," Sustainability, MDPI, vol. 10(6), pages 1-19, June.
    2. Jeimmy Carrera & Darinka Ramírez-Hernández, 2018. "Innovative Education in MOOC for Sustainability: Learnings and Motivations," Sustainability, MDPI, vol. 10(9), pages 1-17, August.
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