Analyzing Learning Patterns Based on Log Data from Digital Textbooks

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 attenti...

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Bibliographic Details
Published in:International journal of distance education technologies 2019-01, Vol.17 (1), p.1-14
Main Authors: Yin, Chengjiu, Mouri, Kousuke, Ren, Zhuo, Uosaki, Noriko
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Cognitive Style
Correlation
Data Collection
Educational Improvement
Educational Research
Educational Technology
Electronic Publishing
Learning
Learning Processes
Pretests Posttests
Student Journals
Teaching Methods
Textbooks
Undergraduate Students
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Summary: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
ISSN:1539-3100
1539-3119
DOI:10.4018/IJDET.2019010101