Learning Analytics for Educational Big Data (2014-)


We are proceeding Educational Big Data research project at Kyushu University, Japan. This project uses an e-book system called BookLooper, which allows students to browse e-books in Web browser, PC, mobile devices such as smartphone.

Research fund

NICT Social Bigdata Application
Principal Researcher: Hiroaki Ogata


Prediction of browsing time


We proposes a method to analyze preview behaviors of students using a learning management system (LMS) and an e-book system. We collected a large number of operation logs from e-books to analyze the process of learning. In addition, we conducted a quiz to test the level of understanding. This study especially focuses on an analysis of the relationship between learning behavior in preview and its effectiveness in the corresponding quiz. We apply a machine learning and classification methodology for behavior analysis. Experimental results report that students who undertake good preview achieve better scores in quizzes..

International Conferences


Relationship among psychometric data, learning behaviors, and learning performance

This research aims to investigate the relationship between learningpsychometric data, learning behaviors, and the learning performance in ubiquitous learning environments. Since learning analytics projectstarted, we focus on psychometric data about self-regulated learning(SRL), and we investigate the relationship among SRL, log data such as marker, annotation, accessing device types stored the learning management system. The results up to now indicated that self-efficacy, internal value and the number of read slide had significant influence on the final score, and the awareness of cognitive learning strategy use has slightly significant power to predict the final score. We will consider the concrete learning support solutions, based on the our research findings.