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.
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References
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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.
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Cabral, B., Figueira, Á. (2019). On the Development of a Model to Prevent Failures, Built from Interactions with Moodle. In: Herzog, M., Kubincová, Z., Han, P., Temperini, M. (eds) Advances in Web-Based Learning – ICWL 2019. ICWL 2019. Lecture Notes in Computer Science(), vol 11841. Springer, Cham. https://doi.org/10.1007/978-3-030-35758-0_37
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DOI: https://doi.org/10.1007/978-3-030-35758-0_37
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