Abstract
This paper describes a method to reproduce and visualize student course material page views chronologically as a basis for improving lessons and supporting learning analysis. Interactive mining was conducted on Moodle course logs downloaded in an Excel format. The method uses a time-series cross-section (TSCS) analysis framework; in the resulting TSCS table, the page view status of students can be represented numerically across multiple time intervals. The TSCS table, generated by an Excel macro that the author calls TSCS Monitor, makes it possible to switch from an overall, class-wide viewpoint to more narrowly-focused partial viewpoints. Using numerical values and graph, the approach enables a teacher to capture the course material page view status of students and observe student responses to the teacher’s instructions to open various teaching materials. It allows the teacher to identify students who fail to open particular materials during the lesson or who are late opening them.
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This work was supported by JSPS KAKENHI Grant Number 15K00498.
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Dobashi, K. (2019). Interactive Mining for Learning Analytics by Automated Generation of Pivot Table. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2018. Advances in Intelligent Systems and Computing, vol 787. Springer, Cham. https://doi.org/10.1007/978-3-319-94229-2_7
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DOI: https://doi.org/10.1007/978-3-319-94229-2_7
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