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Title: Using Learning Analytics to Detect Off-Task Reading Behaviors in Class
Authors: Akçapınar, Gökhan
Hasnine, Mohammad Nehal
Majumdar, Rwitajit
Flanagan, Brendan
Ogata, Hiroaki
Author's alias: 緒方, 広明
Keywords: learning analytics
educational data mining
in-class decision making
off-task behavior
reading pattern analysis
clustering
Issue Date: Mar-2019
Publisher: Society for Learning Analytics Research (SoLAR)
Journal title: Companion Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK'19)
Start page: 471
End page: 476
Abstract: In this paper, we aimed at detecting off-task behaviors of the students by analyzing logs from a digital textbook reader. We analyzed 47 students’ reading logs from a 60-minutes long in-class reading activity. During the preprocess, we extracted each student’s reading patterns as a single vector. Then we used cluster analysis to find the most common reading patterns. Our results indicated that there are two major reading patterns in data. The first pattern is, the students who are following the instructor from the beginning until the end of the lecture. The second pattern is, students who are following the instructor’s pattern until the first 17th minute but not during the rest of the lecture. Based on these patterns we labeled first group as on-task students while the other group as off-task students. We also investigated academic performance of students in these two groups. Obtained results can be used to design data-driven support for in-class teaching. Instructors can plan interventions when off-task behaviors occur while the lecture is in progress.
Description: [The 9th International Learning Analytics and Knowledge (LAK) Conference] March 4-8, 2019, Tempe, Arizona, USA
Rights: This work is published under the terms of the Creative Commons Attribution- Noncommercial-ShareAlike 3.0 Australia Licence.
URI: http://hdl.handle.net/2433/243256
Appears in Collections:Journal Articles

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