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http://dx.doi.org/10.32431/kace.2020.23.1.007

Investigating Learning Type in Online Problem-Based Learning: Applying Learning Analysis Techniques  

Lee, Sunghye (KAIST 과학영재교육연구원)
Choi, Kyoungae (중부대학교)
Park, Minseo (KAIST 과학영재교육연구원)
Han, Jeongyun (서울대학교 스마트 휴머니티 융합 사업단)
Publication Information
The Journal of Korean Association of Computer Education / v.23, no.1, 2020 , pp. 77-90 More about this Journal
Abstract
The purpose of the study is to provide educational implications for more effective Problem-based learning(PBL) by investigating students' learning types based on their online learning behaviors. A total of 1,341 students participated in the study, and they engaged in a six-week-long PBL program run by K University. For the study, participants' online activity data were collected. From the data, a total of 48 variables that represent their various online learning behaviors were extracted. Based on the variables, hierarchical cluster analysis was conducted to analyze learning types. Also, the differences in learning characteristics and achievements were investigated by considering types of learning. As a result, the learning types in online PBL were classified as 'high-level participation (cluster 1)', 'medium-level participation (cluster 2)', and 'low-level participation (cluster 3)'. In addition, the achievement level was found to be highest in 'high-level participation (cluster 1)' and lowest in 'low-level participation (cluster 3)'. Based on the results, the implications for improving online PBL were suggested.
Keywords
Problem-based learning; Learning Process; Learning Analytics; Online Learning;
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Times Cited By KSCI : 3  (Citation Analysis)
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