International Journal of Technology and Applied Science

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Volume 17 Issue 4
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Student Performance Prediction in Online Courses using Machine Learning Algorithms

Author(s) S. Akshitha, T. Abhinaya, U. Sreekanth, S. Siva Shankar
Country India
Abstract The rapid expansion of online education platforms has generated vast amounts of learner data, providing new opportunities to predict and enhance student performance through data-driven methods. Using the Random Forest algorithm, this study suggests a machine learning-based method for forecasting online course success. To determine the main elements affecting academic results, a variety of features are examined, including learning activity logs, assessment results, attendance trends, and engagement indicators. Because of its high accuracy, robustness, and capacity to manage intricate, nonlinear interactions within the dataset, the Random Forest model is used. According to experimental results, the model successfully divides students into performance groups, allowing for the early detection of learners who may be at danger. The results demonstrate how Random Forest-based prediction algorithms can help teachers and educational institutions create individualized learning plans, increase student retention, and boost academic achievement in general in online learning settings.
Keywords Prediction, Machine learning algorithms, courses, academic performance, students.
Field Engineering
Published In Volume 17, Issue 4, April 2026
Published On 2026-04-05
Cite This Student Performance Prediction in Online Courses using Machine Learning Algorithms - S. Akshitha, T. Abhinaya, U. Sreekanth, S. Siva Shankar - IJTAS Volume 17, Issue 4, April 2026.
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