Analyzing Learning Patterns Based on Log Data from Digital Textbooks

Analyzing Learning Patterns Based on Log Data from Digital Textbooks

Kousuke Mouri (Tokyo University of Agriculture and Technology, Fuchu, Japan), Zhuo Ren (Jinan University, Guangzhou, China), Noriko Uosaki (Osaka University, Suita, Japan) and Chengjiu Yin (Kobe University, Kobe, Japan)
Copyright: © 2019 |Pages: 14
DOI: 10.4018/IJDET.2019010101

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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Introduction

With the development of e-publishing technologies and standards, it is easy to obtain digital books, such as “living books,” “talking books,” and “CD-ROM books,” from the Internet (Yin et al., 2015). Digital books have become a potentially effective pedagogic tool (Hezroni, 2004; Reinking, 1997; Snyder, 2002), indicated by the fact that digital book reading has increased significantly in the United States (Lee et al., 2012). Consequently, traditional textbooks are being increasingly replaced by digital textbooks (Ren, Uosaki, Kumamoto, Liu, & Yin, 2017).

Many researchers have been paying attention to the development of digital books to support teaching, learning, and scholarship. By using digital books, large bodies of log data can be accumulated, the analysis of which can be used to perform learning analytics.

Learning analytics can be used in making suggestions to policy makers, instructors, and learners (Baker & Inventado, 2014; Hwang, Hsu, Lai, & Hsueh, 2017). Therefore, learning analytics have become an important issue in education (Hwang, Chu, & Yin, 2017) that have entailed important changes in educational research.

The objective of learning analytics is to provide helpful information to optimize or improve learning designs, learning outcomes, and learning environments based on the analysis results (Greller & Drachsler, 2012; Hwang, Chu, & Yin, 2017).

In the analysis of learning behaviors in this study, we used a digital textbook system to collect students’ learning logs. Learning log is defined as a digital record of what learners have learned in a formal and an informal setting (Ogata, Hou, Uosaki, Mouri, & Liu., 2014; Mouri, Ogata, & Uosaki, 2015). The system was used in a commercial law course for undergraduate students, which was conducted in entirety in English: The students were assigned readings in English, and the teacher spoke in English. We also used questionnaires to collect data on students’ learning styles and cognitive styles.

Using these data, we applied the association analysis method with the Apriori algorithm to analyze students’ learning patterns or rules. One of the advantages of analyzing the learning patterns or rules is the preemptive prediction of students’ final grade and progresses in the future. As a result, teachers can improve their teaching strategies and support students’ learning behaviors. From the analysis, this study found three meaningful student types by considering the detected association rules.

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