AN EXPLAINABLE LEARNING ANALYTICS FRAMEWORK FOR PREDICTING STUDENT ACADEMIC PERFORMANCE IN ONLINE LEARNING ENVIRONMENTS
Keywords:
Learning Analytics, Academic Performance Prediction, Explainable Artificial Intelligence, Online Learning, Educational Data Mining, SHAPAbstract
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 educationalinstitutions to identify learners at risk of poor academic performance and provide timely interventions. However, many machine learning models employed for academic performance prediction operate as black-box systems,limiting transparency and reducing stakeholder
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,
