A Predictive Approach to Student Performance in Online Learning Using Data Analytics

Authors

  • Mr G. Bharath Kumar|Dr Sk. Mulla Shabbeer|Dr Y. Rokesh Kumar|S.Venkata Durga Prasad

Keywords:

student engagement, binary classification, Random Forest, machine learning, educational technology, digital education, adaptive learning

Abstract

Student engagement plays a vital role in ensuring effective learning outcomes,especially in digital education settings. This study presents a machine learning approach toclassify engagement levels as "engaged

References

Alhothali, A.; Albsisi, M.; Assalahi, H.; Aldosemani, T. Predicting student outcomes in online courses using machine learning techniques: A review. Sustainability 2022, 14, 6199. [CrossRef]

Tao, T.; Sun, C.; Wu, Z.; Yang, J.; Wang, J. Deep Neural Network-Based Prediction and Early Warning of Student Grades and Recommendations for Similar Learning Approaches. Appl. Sci. 2022, 12, 7733. [CrossRef]

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Published

2025-06-27

How to Cite

Mr G. Bharath Kumar|Dr Sk. Mulla Shabbeer|Dr Y. Rokesh Kumar|S.Venkata Durga Prasad. (2025). A Predictive Approach to Student Performance in Online Learning Using Data Analytics . Journal of Computational Analysis and Applications (JoCAAA), 34(6), 175–186. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3070

Issue

Vol. 34 No. 6 (2025): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.