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Title: Investigating Students’ e-Book Reading Patterns with Markov Chains
Authors: AKÇAPINAR, Gökhan
MAJUMDAR, Rwitajit
FLANAGAN, Brendan
OGATA, Hiroaki  kyouindb  KAKEN_id
Author's alias: 緒方, 広明
Keywords: e-book
sequential behavior analysis
markov chains
clustering
learning analytics
educational data mining
Issue Date: 24-Nov-2018
Publisher: Asia-Pacific Society for Computers in Education (APSCE)
Journal title: 26th International Conference on Computers in Education Main Conference Proceedings
Start page: 310
End page: 315
Abstract: In this paper, we analyze students’ e-book reading patterns by using MarkovChains (MCs). We used click-stream data of 236 students while they read 7 differentcontents shared by the instructor across different weeks of the course. To analyze readingpatterns, we first clustered students independently based on their interaction with eachcontent. We grouped students in None, Low, Medium, and High clusters. Then by usingMCs, we calculated cluster transition probabilities between different contents. We alsovisualized these patterns and applied a prediction algorithm to predict students’ readingpatterns. Results revealed that students are likely to follow the same reading patterns acrossthe semester. In other words, if a student reads less in the first content, s/he is likely to readless during the rest of the semester. We also found that transition data could be used topredict students’ further reading behaviors. The developed model can be used to plan anintervention system for at-risk students. Visualization of these transitions may help a teacherto understand how well students use contents.
Description: 26th International Conference on Computers in Education, Metro Manila, Philippines, November 26-30, 2018.
Rights: Copyright 2018 Asia-Pacific Society for Computers in Education. All rights reserved. No part of this book may be reproduced, stored in a retrieval system, transmitted, in any forms or any means, without the prior permission of the Asia-Pacific Society for Computers in Education. Individual papers may be uploaded on to institutional repositories or other academic sites for self-archival purposes.
URI: http://hdl.handle.net/2433/237324
Appears in Collections:Journal Articles

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