Advertisement

Interactive Mining for Learning Analytics by Automated Generation of Pivot Table

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 787)

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.

Keywords

Interactive mining Time-series Cross-section Visualization Educational data mining Learning analytics Pivot table 

Notes

Acknowledgments

This work was supported by JSPS KAKENHI Grant Number 15K00498.

References

  1. 1.
    Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33, 135–146 (2007)CrossRefGoogle Scholar
  2. 2.
    Mostow, J., Beck, J., Cen, H., Cuneo, A., Gouvea, E., Heiner, C.: An educational data mining tool to browse tutor-student interactions: time will tell!. Educational data mining. In: 2005 AAAI Workshop. Technical report WS-05-02, pp. 15–22 (2005)Google Scholar
  3. 3.
    Dobashi, K.: Development and trial of excel macros for time series cross section monitoring of student engagement: analyzing students’ page views of course materials. Proc. Comput. Sci. 96, 1086–1095 (2016)CrossRefGoogle Scholar
  4. 4.
    Dobashi, K.: Automatic data integration from Moodle course logs to pivot tables for time series cross section analysis. Proc. Comput. Sci. 112, 1835–1844 (2017)CrossRefGoogle Scholar
  5. 5.
    Kimberley, E.A., Matthew, D.P.: Course signals at purdue: using learning analytics to increase student success. In: LAK 2012 Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 267–270. Vancouver, Canada (2012)Google Scholar
  6. 6.
    Krumm, A.E., Waddington, R.J., Teasley, S.D., Lonn, S.: A learning management system-based early warning system for academic advising in undergraduate engineering. In: Larusson, J.A., White, B. (eds.) Learning Analytics: From Research to Practice, pp. 103–119. Springer, New York (2014)Google Scholar
  7. 7.
    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, pp. 88–91, Vancouver, Canada (2012)Google Scholar
  8. 8.
    Dawson, S.P., McWilliam, E., Tan, J.: Teaching smarter: how mining ICT data can inform and improve learning and teaching practice. In: Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education, pp. 221—230, Melbourne, Australia (2008)Google Scholar
  9. 9.
    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
  10. 10.
    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
  11. 11.
    Konstantinidis, A., Grafton, C.: Using excel macros to analyses Moodle logs, In: Proceedings of the 2nd Moodle Research Conference (MRC2013), pp. 33–39, Sousse, Tunisia (2013)Google Scholar
  12. 12.
    Zhang, H., Almeroth, K.: Moodog: tracking student activity in online course management systems. J. Interact. Learn. Res. 21(3), 407–429 (2010)Google Scholar
  13. 13.
    Mazza, R., Dimitrova, V.: CourseVis: externalising student information to facilitate instructors in distance learning. In: Proceedings of the International Conference in Artificial Intelligence in Education, pp. 279–286, Sydney, Australia (2003)Google Scholar
  14. 14.
    Mazza, R., Milani, C.: GISMO: a graphical interactive student monitoring tool for course management systems. In: Technology Enhanced Learning 04 International Conference (T.E.L. 04), pp. 1–8, Milan, Italia (2004)Google Scholar
  15. 15.
  16. 16.
    Govaerts, S., Verbert, K., Duval, E., Pardo, A.: The student activity meter for awareness and self-reflection. In: CHI EA 2012 CHI 2012 Extended Abstracts on Human Factors in Computing Systems, pp. 869–884, Austin, Texas, USA (2012)Google Scholar
  17. 17.
    Duval, E.: Attention please! Learning analytics for visualization and recommendation. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge (LAK 2011), pp. 9–17, Banff, Alberta, Canada (2011)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

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

Personalised recommendations