A Machine Learning Approach to Predicting Academic Performance

AUTHOR(S)

S Sukanya, Dr D William Albert

DOI: https://doi.org/10.46647/ijetms.2023.v07i06.003

ABSTRACT
Academic performance prediction is an indispensable task for policymakers. Academic performance is frequently examined using classical statistical software, which can be used to detect logical connections between socioeconomic status and academic performance. These connections, whose accuracy depends on determine prediction accuracy. To eliminate the effects of logical relationships on such accuracy, machine learning models extended with education and socioeconomic data to predict academic performance. The decision tree, random forest, logistic regression, support vector machine, and neural network are used for testing. The neural network model can be used by policymakers to forecast academic performance, which in turn can aid in the formulation of various policies, such as those regarding funding and teacher selection. Finally, this study demonstrated the feasibility of machine learning as an auxiliary educational decision-making tool for use in the future.

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How to Cite This Article:
S Sukanya, Dr D William Albert . ijetms;7(6):12-16. DOI: 10.46647/ijetms.2023.v07i06.003