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.
|Number of pages||5|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - Jan 1 2016|
|Event||1st International Workshop on Learning Analytics Across Physical and Digital Spaces, CrossLAK 2016 - Edinburgh, Scotland, United Kingdom|
Duration: Apr 25 2016 → Apr 29 2016
All Science Journal Classification (ASJC) codes
- Computer Science(all)