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Forecasting Students' Performance Through Self-Regulated Learning Behavioral Analysis

Forecasting Students' Performance Through Self-Regulated Learning Behavioral Analysis

Rodrigo Lins Rodrigues, Jorge Luis Cavalcanti Ramos, João Carlos Sedraz Silva, Raphael A. Dourado, Alex Sandro Gomes
Copyright: © 2019 |Volume: 17 |Issue: 3 |Pages: 23
ISSN: 1539-3100|EISSN: 1539-3119|EISBN13: 9781522563969|DOI: 10.4018/IJDET.2019070104
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MLA

Rodrigues, Rodrigo Lins, et al. "Forecasting Students' Performance Through Self-Regulated Learning Behavioral Analysis." IJDET vol.17, no.3 2019: pp.52-74. http://doi.org/10.4018/IJDET.2019070104

APA

Rodrigues, R. L., Ramos, J. L., Silva, J. C., Dourado, R. A., & Gomes, A. S. (2019). Forecasting Students' Performance Through Self-Regulated Learning Behavioral Analysis. International Journal of Distance Education Technologies (IJDET), 17(3), 52-74. http://doi.org/10.4018/IJDET.2019070104

Chicago

Rodrigues, Rodrigo Lins, et al. "Forecasting Students' Performance Through Self-Regulated Learning Behavioral Analysis," International Journal of Distance Education Technologies (IJDET) 17, no.3: 52-74. http://doi.org/10.4018/IJDET.2019070104

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Abstract

The increasing use of the Learning Management Systems (LMSs) is making available an ever-growing, volume of data from interactions between teachers and students. This study aimed to develop a model capable of predicting students' academic performance based on indicators of their self-regulated behavior in LMSs. To accomplish this goal, the authors analyzed behavioral data from an LMS platform used in a public University for distance learning courses, collected during a period of seven years. With this data, they developed, evaluated, and compared predictive models using four algorithms: Decision Tree (CART), Logistic Regression, SVM, and Naïve Bayes. The Logistic Regression model yielded the best results in predicting students' academic performance, being able to do so with an accuracy rate of 0.893 and an area under the ROC curve of 0.9574. Finally, they conceived and implemented a dashboard-like interface intended to present the predictions in a user-friendly way to tutors and teachers, so they could use it as a tool to help monitor their students' learning process.

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