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

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.