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Forecasting Students' Performance through Self-Regulated Learning Behavioral Analysis

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Title: Forecasting Students' Performance through Self-Regulated Learning Behavioral Analysis
Language: English
Authors: Rodrigues, Rodrigo Lins, Ramos, Jorge Luis Cavalcanti, Silva, João Carlos Sedraz, Dourado, Raphael A., Gomes, Alex Sandro
Source: International Journal of Distance Education Technologies. 2019 17(3):52-74.
Availability: 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/
Peer Reviewed: Y
Page Count: 23
Publication Date: 2019
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Management Systems, Teacher Student Relationship, Distance Education, College Students, College Faculty, Academic Achievement, Learning Processes, Student Behavior, Decision Making, Accuracy, Prediction, Longitudinal Studies, Regression (Statistics), Bayesian Statistics, Self Management, Classification, Metacognition
DOI: 10.4018/IJDET.2019070104
ISSN: 1539-3100
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.
Abstractor: As Provided
Entry Date: 2019
Accession Number: EJ1214700
Database: ERIC
FullText Text:
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  Data: Forecasting Students' Performance through Self-Regulated Learning Behavioral Analysis
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  Data: <searchLink fieldCode="AR" term="%22Rodrigues%2C+Rodrigo+Lins%22">Rodrigues, Rodrigo Lins</searchLink><br /><searchLink fieldCode="AR" term="%22Ramos%2C+Jorge+Luis+Cavalcanti%22">Ramos, Jorge Luis Cavalcanti</searchLink><br /><searchLink fieldCode="AR" term="%22Silva%2C+João+Carlos+Sedraz%22">Silva, João Carlos Sedraz</searchLink><br /><searchLink fieldCode="AR" term="%22Dourado%2C+Raphael+A%2E%22">Dourado, Raphael A.</searchLink><br /><searchLink fieldCode="AR" term="%22Gomes%2C+Alex+Sandro%22">Gomes, Alex Sandro</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Distance+Education+Technologies%22"><i>International Journal of Distance Education Technologies</i></searchLink>. 2019 17(3):52-74.
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  Data: 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/
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  Data: Y
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  Data: 23
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  Data: 10.4018/IJDET.2019070104
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  Data: 1539-3100
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  Label: Abstract
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  Data: 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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  Data: 2019
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  Label: Accession Number
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  Data: EJ1214700
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        Value: 10.4018/IJDET.2019070104
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        PageCount: 23
        StartPage: 52
    Subjects:
      – SubjectFull: Management Systems
        Type: general
      – SubjectFull: Teacher Student Relationship
        Type: general
      – SubjectFull: Distance Education
        Type: general
      – SubjectFull: College Students
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      – SubjectFull: College Faculty
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      – SubjectFull: Academic Achievement
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      – SubjectFull: Learning Processes
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      – SubjectFull: Decision Making
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      – SubjectFull: Accuracy
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      – SubjectFull: Prediction
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      – SubjectFull: Classification
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      – SubjectFull: Metacognition
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      – TitleFull: Forecasting Students' Performance through Self-Regulated Learning Behavioral Analysis
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