Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women's university in South Korea
- 주제(키워드) Learning analytics , Self-regulated learning , Asynchronous online courses , Education data mining , Instructional strategies
- 주제(기타) Computer Science, Interdisciplinary Applications; Education & Educational Research
- 설명문(일반) [Kim, Dongho] Univ Florida, Coll Educ, Sch Teaching & Learning, Gainesville, FL 32611 USA; [Yoon, Meehyun; Branch, Robert Maribe] Univ Georgia, Coll Educ, Dept Career & Informat Studies, Athens, GA 30602 USA; [Jo, Il-Hyun] Ehwa Womans Univ, Dept Educ Technol, Coll Educ, Seoul, South Korea
- 등재 SCIE, SSCI, SCOPUS
- 발행기관 PERGAMON-ELSEVIER SCIENCE LTD
- 발행년도 2018
- URI http://www.dcollection.net/handler/ewha/000000156312
- 본문언어 영어
- Published As http://dx.doi.org/10.1016/j.compedu.2018.08.023
초록/요약
With the recognition of the importance of self-regulated learning (SRL) in asynchronous online courses, recent research has explored how SRL strategies impact student learning in these learning environments. However, little has been done to examine different patterns of students with different SRI, profiles over time, which precludes providing optimal on-going instructional support for individual students. To address the gap in research, we applied learning analytics to analyze log data from 284 undergraduate students enrolled in an asynchronous online statistics course. Specifically, we identified student SRI, profiles, and examined the actual student SRI learning patterns. The k-medoids clustering identified three self-regulated learning profiles: self-regulation, partial self-regulation, and non-self-regulation. Self-regulated students showed more study regularity and help-seeking, than did the other two groups of students. The partially self-regulated students showed high study regularity but inactive help-seeking, while the non-self-regulated students exhibited less study regularity and less frequent help-seeking than the other two groups; their self-reported time management scores were significantly lower. The analysis of each week's log variables using the random forest algorithm revealed that self-regulated students studied course content early before exams and sought help during the general exam period, whereas non self-regulated students studied the course content during the general exam period. Based on our findings, we provide instructional strategies that can be used to support student SRL. We also discuss implications of this study for advanced learning analytics research, and the design of effective asynchronous online courses.
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