AN EXPLAINABLE LEARNING ANALYTICS FRAMEWORK FOR PREDICTING STUDENT ACADEMIC PERFORMANCE IN ONLINE LEARNING ENVIRONMENTS

Authors

  • Kumbala Pradeep Reddy,B. Narendra Kumar ,S Jagadeesh

Keywords:

Learning Analytics, Academic Performance Prediction, Explainable Artificial Intelligence, Online Learning, Educational Data Mining, SHAP.

Abstract

The rapid adoption of online learning platforms has generated large volumes of educational data that can be utilizedto improve student outcomes through predictive analytics. Learning analytics techniques enable educational institutions to identify learners at risk of poor academic performance and provide timely interventions. However,many machine learning models

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References

C. Romero and S. Ventura, “Educational data mining: A review of the state of the art,” IEEE Transactions on Systems, Man, and Cybernetics

Part C, vol. 40, no. 6, pp. 601–618, 2010.

R. S. J. d. Baker and K. Yacef, “The state of educational data mining in 2009,” Journal of Educational Data Mining, vol. 1, no. 1, pp. 3–17,

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Published

2022-11-15

How to Cite

Kumbala Pradeep Reddy,B. Narendra Kumar ,S Jagadeesh. (2022). AN EXPLAINABLE LEARNING ANALYTICS FRAMEWORK FOR PREDICTING STUDENT ACADEMIC PERFORMANCE IN ONLINE LEARNING ENVIRONMENTS. Pegem Journal of Education and Instruction, 12(4), 418–424. Retrieved from https://www.pegegog.net/index.php/pegegog/article/view/5164

Issue

Vol. 12 No. 4 (2022): PEGEGOG

Section

Article

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