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

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

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

The way people learn has been affected by the COVID-19 pandemic, resulting in a shift from traditional classroom-based
learning to online learning. As a result, educational institutions have a new opportunity to use relevant data to predict student
performance, which can help improve teaching and learning processes and adjust course curriculum. Universities can leverage
machine learning technology to forecast student performance, enabling them to make necessary changes to lecture delivery and
curriculum. A study examined open university educational data, using demographic, engagement, and performance metrics to
predict student performance with machine learning techniques. The study found that the k-NN strategy outperformed all other
algorithms in some cases, while the ANN approach performed better in others.

Adi Suryaputra Paramita 1,*, Laura Mahendratta Tjahjono

2021

PERI IRAWAN

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