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http://dx.doi.org/10.32431/kace.2019.22.5.005

Investigating Online Learning Types Based on self-regulated learning in Online Software Education: Applying Hierarchical Cluster Analysis  

Han, Jeongyun (서울대학교 스마트 휴머니티 융합 사업단)
Lee, Sunghye (KAIST 과학영재교육연구원)
Publication Information
The Journal of Korean Association of Computer Education / v.22, no.5, 2019 , pp. 51-65 More about this Journal
Abstract
This study aims to provide educational implications for more strategic online software education by the types of online learning according to learners' self-regulated learning characteristics in the online software education environment and examining the characteristics of each type. For this, variables related to self-regulated learning characteristic were extracted from the log data of 809 students participating in the online software learning program of K University, and then analyzed using hierarchical cluster analysis. Based on hierarchical cluster analysis learner clusters according to the characteristics of self-regulated learning were derived and the differences between learners' learning characteristics and learning results according to cluster types were examined. As a result, the types of self-regulated learning of online software learners were classified as 'high level self-regulated learning type (group 1)', 'medium level self-regulated learning type (group 2)', and 'low level self-regulated learning type (group 3)'. The achievement level was found to be highest in 'high-level self-regulated learning type (group 1)' and 'low-level self-regulated learning type (group 3)' was the lowest. Based on these results, the implications for effective online software education were suggested.
Keywords
Software Education; Self-regulated Learning; Cluster Analysis; Learning Analytics; 4th Industrial Revolution; Online Learning;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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