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Sync Ratio and Cluster Heat Map for Visualizing Student Engagement

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Educational Data Science: Essentials, Approaches, and Tendencies

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Abstract

In the current learning management system, it is difficult for even experienced teachers to grasp the learning situation and to engage in a timely manner for each individual, and the response to this problem remains inadequate. In this study, in order to improve the learner’s engagement and the teacher’s help with the lesson, a cluster heat map of student engagement, teaching material browsing sync ratio, and experimental results of outlier detection were examined. Sync ratios for browsing teaching materials were generated on-site in real time, and teachers could refer to them when teaching lessons. From the analysis of the descriptive statistics in the learning log, the material clickstreams, the quiz scores, and Mahalanobis’ generalized distance were obtained and the engagement cluster heat map was generated based on the weekly learning pattern. As a result, it became possible to clearly discuss the relationship between the appearance frequency of learning patterns and the appearance frequency of abnormal values in teaching material clickstreams and quiz scores. It was clarified that some of the frequency of the appearance of the learning pattern correlated with the frequency of the occurrence of abnormal values of the teaching material clickstream and the quiz score. The results of this study help to find learners who repeat inappropriate learning patterns early and to support appropriate teacher interventions.

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Abbreviations

EDM:

Educational data mining

LA:

Learning analytics

LMS:

Learning management system

MGD:

Mahalanobis’ generalized distance

MOOCs:

Massive open online courses

RTTSCS:

Real-time time-series cross-section

VBA:

Visual Basic for Applications

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Acknowledgments

This work was supported by JSPS KAKENHI (Grant numbers 18K11588 and 21K12183).

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Correspondence to Konomu Dobashi .

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Appendix

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Table 7.2 Correlation coefficient between clickstream and learning pattern
Table 7.3 Correlation coefficient between final quiz score and learning pattern

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Dobashi, K. (2023). Sync Ratio and Cluster Heat Map for Visualizing Student Engagement. In: Peña-Ayala, A. (eds) Educational Data Science: Essentials, Approaches, and Tendencies. Big Data Management. Springer, Singapore. https://doi.org/10.1007/978-981-99-0026-8_7

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