Profiling high-achieving students for e-book-based learning analytics

Kousuke Mouri, Fumiya Okubo, Atsushi Shimada, Hiroaki Ogata

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

The purpose of this paper is to mine or detect meaningful learning patterns for profiling high-achieving students using e-book-based activity logs and questionnaire. The analysis of this study uses association analysis with Apriori algorithm. Logs for this analysis were collected from 99 first-year students who use a document viewer system called BookLooper, questionnaires and Moodle in an information science course at Kyushu University. From the results of the association analysis, we found that high-achieving students and BookLooer have significant relationships in terms of preparation and review time. This paper believes that the profiling and analysis can be used to predict their final grades and to detect effective learning patterns.

Original languageEnglish
Pages (from-to)5-9
Number of pages5
JournalCEUR Workshop Proceedings
Volume1601
Publication statusPublished - Jan 1 2016
Event1st International Workshop on Learning Analytics Across Physical and Digital Spaces, CrossLAK 2016 - Edinburgh, Scotland, United Kingdom
Duration: Apr 25 2016Apr 29 2016

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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