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Synchronization Ratio of Time-Series Cross-Section and Teaching Material Clickstream for Visualization of Student Engagement

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Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (AIED 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13356))

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Abstract

This paper describes a study that analyzed the synchronization ratio of learners’ teaching material browsing behavior during lessons using a learning management system and online teaching materials. In lessons attended by many learners, teachers often instruct the learners to open the material, and in such cases, it is important that the browsing behavior of as many learners as possible is synchronized. Therefore, data mining technology and time-series cross-section analysis were applied to the learning log, and a method was developed to calculate the synchronization ratio of teaching material browsing and to visualize it in tables and graphs. It is possible to calculate the average synchronization ratio and the average material synchronization ratio for the section of teaching materials and quizzes used during lessons.

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Numbers 18K11588 and 21K12183.

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Authors and Affiliations

  1. Aichi University, Nagoya, Aichi, 453-8777, Japan

    Konomu Dobashi

  2. University of Hawaiʻi at Mānoa, Honolulu, HI, 96822, USA

    Curtis P. Ho, Catherine P. Fulford, Meng-Fen Grace Lin & Christina Higa

Authors
  1. Konomu Dobashi
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  2. Curtis P. Ho
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  3. Catherine P. Fulford
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  4. Meng-Fen Grace Lin
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  5. Christina Higa
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Corresponding author

Correspondence to Konomu Dobashi .

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Editors and Affiliations

  1. Ateneo De Manila University, Quezon, Philippines

    Maria Mercedes Rodrigo

  2. Department of Computer Science, North Carolina State University, Raleigh, NC, USA

    Noburu Matsuda

  3. Durham University, Durham, UK

    Alexandra I. Cristea

  4. University of Leeds, Leeds, UK

    Vania Dimitrova

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Dobashi, K., Ho, C.P., Fulford, C.P., Lin, MF.G., Higa, C. (2022). Synchronization Ratio of Time-Series Cross-Section and Teaching Material Clickstream for Visualization of Student Engagement. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_22

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  • DOI: https://doi.org/10.1007/978-3-031-11647-6_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11646-9

  • Online ISBN: 978-3-031-11647-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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