Ogata, H. et al. (Eds.) (2015). Proceedings of the 23 rd International Conference on Computers in Education. China: Asia-Pacific Society for Computers in Education Identifying and Analyzing the Learning Behaviors of Students using e-Books Chengjiu YIN a* , Fumiya OKUBO a , Atsushi SHIMADA a , Misato OI a , Sachio HIROKAWA b , Hiroaki OGATA a a Faculty of Arts and Science, Kyushu University, Japan b Research Institute for Information Technology, Kyushu University, Japan *yinchengjiu@gmail.com Abstract: Analyses on students’ learning behaviors comprise an important thrust in education research. This study focused on e-books system used in the classroom and this system recorded students’ learning logs in their daily academic life. These learning logs can be used to analysis students’ learning behaviors. By performing partial correlation analysis, the study found that a number of learning behaviors have a significant relation with students’ test scores. Keywords: Learning analytics, Learning behavior, Learning log, E-books 1. Introduction By 2020, the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan is scheduled to replace all of the textbooks for elementary, middle, and high schools with e-books (MEXT, 2011). Such a move will usher “Educational Big Data,” which will comprise learning logs. As a forerunner to this institutional effort, Kyushu University has supported this work in using BookLooper for e-books beginning in April 2014. BookLooper is a document viewer system provided by a partner to this research, Kyocera Communication Systems Co., Ltd., and can be used on personal computers and smart phones. Thus, students can use it as desired, and their learning log will be collected continuously. Students can use BookLooper to preview their lessons before class, such as writing questions. They can also take note and mark part of a page as important content during class. After classes, they can review the learning content. All of these learning behaviors will be recorded. Meanwhile, such records will create a large volume of data. Using these records, educational effectiveness can be verified, and the features of students’ learning behaviors analyzed. The present work will analyze learning behaviors and identify students’ learning styles by analyzing their learning logs, continuing the work of Yin et al. (2014). By performing partial correlation analysis, the study found that a number of learning behaviors have a significant relation with students’ test scores. 2. Related Works Analyzing learning behaviors is a critical topic in learning analysis. Collecting data is the first step in learning analysis (Yin et al., 2013). Based on the data source, studies on learning behaviors can be classified into three categories: A) Analysis using a questionnaire: In this category, data are collected using a pre-designed questionnaire, such as Ho et al. (2013) used a questionnaire to investigate the teacher behavior of adopting mobile phone messages as a parent–teacher communication medium. B) Manual collection data: In this category, a crowdsourced data collection system is opened to users. Users use the system and consciously leave data on their learning behavior. For 118