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Analyzing Learning Patterns Based on Log Data from Digital Textbooks

Analyzing Learning Patterns Based on Log Data from Digital Textbooks

Kousuke Mouri, Zhuo Ren, Noriko Uosaki, Chengjiu Yin
Copyright: © 2019 |Volume: 17 |Issue: 1 |Pages: 14
ISSN: 1539-3100|EISSN: 1539-3119|EISBN13: 9781522563945|DOI: 10.4018/IJDET.2019010101
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MLA

Mouri, Kousuke, et al. "Analyzing Learning Patterns Based on Log Data from Digital Textbooks." IJDET vol.17, no.1 2019: pp.1-14. http://doi.org/10.4018/IJDET.2019010101

APA

Mouri, K., Ren, Z., Uosaki, N., & Yin, C. (2019). Analyzing Learning Patterns Based on Log Data from Digital Textbooks. International Journal of Distance Education Technologies (IJDET), 17(1), 1-14. http://doi.org/10.4018/IJDET.2019010101

Chicago

Mouri, Kousuke, et al. "Analyzing Learning Patterns Based on Log Data from Digital Textbooks," International Journal of Distance Education Technologies (IJDET) 17, no.1: 1-14. http://doi.org/10.4018/IJDET.2019010101

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Abstract

The analysis of learning behaviors from the log data of digital textbooks is beneficial for improving education systems. The focus of discussion in any analysis of learning behaviors is often on discovering the relationships between learning behavior and learning performance. However, little attention has been paid to investigating and analyzing learning patterns or rules among learning style of index (LSI), cognitive style of index (CSI), and the logs of digital textbooks. In this study, the authors proposed a method to analyze learning patterns or rules of reading digital textbooks. The analysis method used association analysis with the Apriori algorithm. The analysis was conducted using logs of digital textbooks and questionnaires to investigate students' learning and cognitive styles. From the detected meaningful association rules, this study found three student types: poorly motivated, efficient, and diligent. The authors believe that consideration of these student types can contribute to the improvement of learning and teaching

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