Analyzing the Features of Learning Behaviors of Students using e-Books

Chengjiu Yin, Fumiya Okubo, Atsushi Shimada, Misato Oi, Sachio Hirokawa, Masanori Yamada, Kentaro Kojima, Hiroaki Ogata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The analysis of learning behavior and identification of learning style from learning logs are expected to benefit instructors and learners. This study describes methods for processing learning logs, such as data collection, integration, and cleansing, developed in Kyushu University. The research aims to analyze learning behavior and identify students' learning style using student's learning logs. Students were clustered into four groups using k-means clustering, and features of their learning behavior were analyzed in detail. We found that Digital Backtrack Learning style is better than Digital Sequential Learning style.

Original languageEnglish
Title of host publicationDoctoral Student Consortium (DSC) - Proceedings of the 23rd International Conference on Computers in Education, ICCE 2015
PublisherAsia-Pacific Society for Computers in Education
Pages617-626
Number of pages10
ISBN (Electronic)9784990801496
Publication statusPublished - 2015
Event23rd International Conference on Computers in Education, ICCE 2015 - Hangzhou, China
Duration: Nov 30 2015Dec 4 2015

Other

Other23rd International Conference on Computers in Education, ICCE 2015
CountryChina
CityHangzhou
Period11/30/1512/4/15

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Education

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