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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

Abstract. Nowadays, it is easy to create digital books. However, through many university teachers create their own digital books as their teaching materials, it is difficult to evaluate and improve. In this paper, based on our previous research on the learning behavior analysis results, we prototype a digital book reading system and analyze the student reading logs and show how teachers can use the results to improve their teaching.

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