NotesFAQContact Us
Collection
Advanced
Search Tips
Back to results
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1214700
Record Type: Journal
Publication Date: 2019
Pages: 23
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1539-3100
Forecasting Students' Performance through Self-Regulated Learning Behavioral Analysis
Rodrigues, Rodrigo Lins; Ramos, Jorge Luis Cavalcanti; Silva, João Carlos Sedraz; Dourado, Raphael A.; Gomes, Alex Sandro
International Journal of Distance Education Technologies, v17 n3 Article 4 p52-74 2019
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.
IGI Global. 701 East Chocolate Avenue, Hershey, PA 17033. Tel: 866-342-6657; Tel: 717-533-8845; Fax: 717-533-8661; Fax: 717-533-7115; e-mail: journals@igi-global.com; Web site: https://www.igi-global.com/journals/
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A