Implementing Machine Learning Techniques for Predicting Student Performance in an E-Learning Environment

Adi Suryaputra Paramita, Laura Mahendratta Tjahjono

Abstract


The pandemic of COVID-19 has altered the way people learn. Learning has moved from offline to online throughout this pandemic. Predicting student performance based on relevant data has opened up a new field for educational institutions to improve teaching and learning processes, as well as course curriculum adjustments. Machine learning technology can assist universities in forecasting student performance so that necessary changes in lecture delivery and curriculum can be made. The performance of the pupils was predicted using machine learning techniques in this research. Open University (OU) educational data is examined. Demographic, engagement, and performance metrics are used. The results of the experiment. The k-NN strategy outperformed all other algorithms on the OU dataset in some circumstances, but the ANN approach outperformed them all in others.

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E-Learning, Data Mining, Machine Learning, Student Performance

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