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Development of Real-Time Learning Analytics Using Scraping and Pivot Tables

  • Konomu DobashiEmail author
Conference paper
  • 19 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1207)

Abstract

In a PC classroom attended by a large number of students, the author conducted a face-to-face, blended lesson using Moodle and proposes a method to efficiently analyze student learning logs. The system (TSCS Monitor) allows the user to visualize the analyzed results in a time-series presented in both table and graph form. In this paper, real-time processing using scraping was integrated to the above functions in order to reduce the burden of system operation on the teacher and obtain analysis results faster while conducting classes. With the integrated scraping function, it is now possible to automatically download Moodle course logs. Teachers can check the clickstream of course materials in real-time in a time-series cross-section table simply by starting TSCS Monitor during class. The author tested the system, released the analysis results to the students, and assessed the effects on the students via a class evaluation questionnaire.

Keywords

Real-time Learning analytics Scraping Visualization Time-series Cross-section Pivot table Moodle 

Notes

Acknowledgments

This work was supported by JSPS KAKENHI Grant Number 18K11588.

References

  1. 1.
    Shimada, A., Konomi, S.I., Ogata, H.: Real-time learning analytics system for improvement of on-site lectures. Interact. Technol. Smart Educ. 15, 314–331 (2018)CrossRefGoogle Scholar
  2. 2.
    Dougiamas, M., Taylor, P.: Moodle: using learning communities to create an open source course management system. In: Proceedings of EdMedia, World Conference on Educational Media and Technology 2003, pp. 171–178 (2003)Google Scholar
  3. 3.
    Dobashi, K.: Interactive mining for learning analytics by automated generation of pivot table. In: Advances in Artificial Intelligence, Software and Systems Engineering, AHFE 2018. Advances in Intelligent Systems and Computing, vol. 787, pp. 66–77. Springer, Cham (2018)Google Scholar
  4. 4.
    McKay, T., Miller, K., Tritz, J.: What to do with actionable intelligence: E2Coach as an intervention engine. In: LAK 2012 Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, Canada, pp. 88–91 (2012)Google Scholar
  5. 5.
    Arnold, K.E., Pistilli, M.D.: Course signals at Purdue: using learning analytics to increase student success. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 267–270. ACM (2012)Google Scholar
  6. 6.
    Hardy, J., Bates, S., Hill, J., Antonioletti, M.: Tracking and visualization of student use of online learning materials in a large undergraduate course. In: ICWL. LNCS, vol. 4823, pp. 464–474. Springer (2008)Google Scholar
  7. 7.
    May, M., Sebastien, G., Patrick, P.: TrAVis to enhance online tutoring and learning activities: real-time visualization of students tracking data. Technol. Smart Educ. 8(1), 52–69 (2011)Google Scholar
  8. 8.
    Mazza, R., Milani, C.: GISMO: a graphical interactive student monitoring tool for course management systems. In: 2004 International Conference on Technology Enhanced Learning, TEL 2004, Milan, Italia, pp. 1–8 (2004)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Modern Chinese StudiesAichi UniversityNagoya-shiJapan

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