Abstract :

The Rapid Growth Of Online Education Platforms Has Generated Large Volumes Of Learner Data, Creating Opportunities To Analyze Student Behavior And Academic Progress Using Machine Learning (ML) Techniques. Predicting Student Performance In Online Courses Is Essential For Identifying At-risk Learners, Improving Course Design, And Enhancing Personalized Learning Experiences. This Study Proposes A Machine Learning-based Framework To Predict Students’ Academic Performance In Online Courses By Analyzing Various Factors Such As Participation In Online Activities, Assignment Submissions, Quiz Scores, Time Spent On Learning Materials, And Interaction Patterns Within The Learning Management System. The Proposed System Utilizes Data Preprocessing, Feature Selection, And Supervised Machine Learning Algorithms To Build Predictive Models Capable Of Estimating Student Outcomes With High Accuracy. Algorithms Such As Decision Trees, Random Forest, Support Vector Machines, And Logistic Regression Are Applied To Classify Student Performance Levels And Identify Influential Learning Attributes. The Model Helps Educators And Administrators Detect Students Who May Struggle In The Course And Enables Timely Interventions To Support Their Learning Progress. Experimental Results Demonstrate That Machine Learning Models Can Effectively Analyze Online Learning Data And Provide Reliable Predictions Of Student Performance. By Integrating Predictive Analytics Into Online Learning Environments, Educational Institutions Can Enhance Academic Success, Reduce Dropout Rates, And Provide Adaptive Learning Support Tailored To Individual Student Needs. The Proposed Approach Highlights The Potential Of Machine Learning In Transforming Data-driven Decision-making In Modern Digital Education Systems.

Published:

09-10-2022

Issue:

Vol. 22 No. 10 (2022)

Page Nos:

24 - 32

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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