University of Bahrain
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Using Machine Learning to Predict the Low Grade Risk for Students based on Log File in Moodle Learning Management System

Show simple item record Thi-Diem Nguyen, Anh 2021-08-22T15:18:39Z 2021-08-22T15:18:39Z 2021-08-22
dc.identifier.issn 2210-142X
dc.description.abstract Currently, the demand for online teaching and learning is an inevitable development trend, but to deploy an effective online learning model, schools are interested in improving students' sense of active learning. Students, to reduce the rate of students failing and dropping them. Since then, the research has aimed to build a solution to analyze learners' behavior from the data collected on the online learning site - Moodle LMS and use the Linear Regression algorithm to predict the learning average score at the end of the student's course. The expected purpose of the study is to provide lecturers with criteria to classify student learning outcomes right in the teaching process. On that basis, the lecturer can filter out the list of students who are at risk of failing the subject, and promptly warn students to change their learning attitude more actively, so that students can achieve satisfactory results. at the end of the course, thereby reducing the rate of students failing and dropping out of school. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri *
dc.subject Machine Learning en_US
dc.subject Linear Regression en_US
dc.subject Learning Management System en_US
dc.subject Log File en_US
dc.title Using Machine Learning to Predict the Low Grade Risk for Students based on Log File in Moodle Learning Management System en_US
dc.contributor.authorcountry Vietnam en_US
dc.contributor.authoraffiliation Faculty of Information Technology, Van Lang University, Ho Chi Minh City en_US
dc.source.title International Journal Of Computing and Digital System en_US
dc.abbreviatedsourcetitle IJCDS en_US

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