Invited Speaker---Prof. Konomu Dobashi


Faculty of Modern Chinese Studies, Aichi University, Japan

Biography: Dr. Konomu Dobashi was awarded his B.A. in Economics from Hosei University, Tokyo, 1979, and experience working as a university librarian. He was awarded his Master of Systems Management from the Graduate School of the University of Tsukuba in 1992. He earned a Ph.D. in Engineering from the Graduate School of Engineering, The University of Tokyo, in 1996. Currently, he is a professor at Aichi University, Faculty of Modern Chinese Studies in Nagoya City, Japan, and is mainly in charge of information education. His primary research interests are data mining and text mining in the field of artificial intelligence. He is presently conducting research on educational data mining and learning analytics, with a focus on analyzing and visualizing the course materials browsing behavior of students in classes through a learning management system that accumulates student learning logs.

Speech Title: Analysis and Visualization of Moodle Learning Log by Automatic Generation of a Time-series Cross-section Table
Abstract: The author describes the application of an innovative approach to monitoring and assessing student engagement with course materials in face-to-face blended lessons using course materials uploaded on Moodle. The method collects and analyzes student learning log data using a macro which the author calls time-series cross-section (TSCS) Monitor to generate a TSCS table. The user is then able to analyze data from the Moodle event log from various viewpoints through the operation of Excel pivot table functions capable of producing a variety of useful visualizations. Character string processing is performed on time data recorded in the Moodle event log, and discrete categories representing time such as year, month, day, and time are defined to allow easier pivot table processing. As a result, student page views of course materials, taken either as a whole or considered individually, can be tracked. Similarly, the viewing behavior of the class as a whole or of individual students can be monitored. The Excel macros described here can automatically generate TSCS tables by integrating time data in days, hours, minutes, etc., allowing the user to conduct time-series analyses using multiple timelines.