Predictive Modeling for Student Performance: Harnessing Machine Learning to Forecast Academic Marks

Abstract

This research investigates the impact of machine learning on higher education teaching and learning, as well as strategies for enhancing the learning environment. There has been a notable increase in student interest in online and digital courses, and platforms such as Course Era and Udemy have become increasingly popular. This study utilizes machine learning applications in teaching and learning, taking into account students' backgrounds, prior academic performance, and other relevant factors. However, due to the large class sizes, it may be challenging to provide personalized support to each student in open learning courses, which could lead to a higher dropout rate. To address this issue, the study employs linear regression, a machine learning algorithm, to predict outcomes.

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Keywords: Machine Learning, Dropout Rate, Linear Regression, Algorithm, Prediction, Outcomes, Coursera, Udemy

JEL Classification: O3