Advertisement

On the Development of a Model to Prevent Failures, Built from Interactions with Moodle

  • Bruno CabralEmail author
  • Álvaro FigueiraEmail author
Conference paper
  • 331 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11841)

Abstract

In this article we propose an automatic system that informs students of abnormal deviations of a virtual learning path that leads to the best grades in the course. Our motivation is based on the fact that by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student’s current online actions towards the course. Our goal is therefore to prevent situations that have a significant probability to lead to a pour grade and, eventually, to failing. Our methodology can be applied to online courses that integrate the use of an online platform that stores user actions in a log file, and that has access to other student’s evaluations. The system is based on a data mining process on the log files and on a self-feedback machine learning algorithm that works paired with the Moodle LMS. Our results shown that it is possible to predict grade levels by only taking interaction patterns in consideration.

Keywords

Data mining e-Learning Grade prediction Student learning path 

Notes

Acknowledgements

This work is financed by National Funds through the Portuguese funding agency, FCT – “Fundação para a Ciência e a Tecnologia”, within the project: UID/EEA/50014/2019.

References

  1. 1.
    Conijn, R., Snijders, C., Kleingeld, A., Matzat, U.: Predicting student performance from LMS data: a comparison of 17 blended courses using moodle LMS. IEEE Trans. Learn. Technol. 10(1), 17–29 (2017)CrossRefGoogle Scholar
  2. 2.
    Figueira, Á.: Predicting grades by principal component analysis a data mining approach to learning analytics. In: IEEE 16th International Conference on Advanced Learning Technologies (ICALT), pp. 465–467. IEEE, Austin (2016)Google Scholar
  3. 3.
    Nam Liao, S., Zingaro, D., Thai, K., Alvarado, C., Griswold, W.G., Porter, L.: A robust machine learning technique to predict low-performing students. ACM Trans. Comput. Educ. (TOCE) 19(3), 18:1–18:19 (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.CRACS/INESCTECUniversity of PortoPortoPortugal

Personalised recommendations