Paper Info
Title
Forecasting Students' Performance Through Self-Regulated Learning Behavioral Analysis
Abstract
AbstractThe 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.
Year
DOI
Venue
2019
10.4018/IJDET.2019070104
Periodicals
Keywords
Field
DocType
Educational Data Mining, Learning Analytics, Learning Management Systems, Learning Systems, Self-regulated Learning
Self-regulated learning,Self-management,Regression analysis,Computer science,Knowledge management,Distance education,Metacognition,Mathematics education,Bayesian statistics,Academic achievement,Management system
Journal
Volume
Issue
ISSN
17
3
1539-3100
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Rodrigo L. Rodrigues103.38
Jorge Luis Cavalcanti Ramos200.34
João Carlos Sedraz Silva300.68
Raphael A. Dourado411.07
Alex S. Gomes564.89