AN EXPLAINABLE LEARNING ANALYTICS FRAMEWORK FOR PREDICTING STUDENT ACADEMIC PERFORMANCE AND IDENTIFYING AT-RISK LEARNERS IN ONLINE HIGHER EDUCATION

Authors

  • V Ravikumar,N Srivani, Kasapaka Rubenraju

Keywords:

Learning Analytics, Academic Performance Prediction, At-Risk Students, Online Learning, Educational Data Mining, Explainable Artificial Intelligence, Higher Education

Abstract

The increasing adoption of online and blended learning environments has generated large volumes of educationaldata that can be used to support student success and improve instructional decision-making. Learning managementsystems record various forms of student interaction, including course participation, assignment submissions, assessment performance, and engagement behaviors

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References

G. Siemens and R. S. J. d. Baker, “Learning analytics and educational data mining: Towards communication and collaboration,” Proceedings

of the 2nd International Conference on Learning Analytics and Knowledge, pp. 252–254, 2012.

S. Dawson, L. Macfadyen, S. Risko, J. Foulsham, and K. Kingstone, “Using technology to encourage self-directed learning,” International

Journal of Educational Technology in Higher Education, vol. 11, no. 2, pp. 1–15, 2014.

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Published

2022-12-20

How to Cite

V Ravikumar,N Srivani, Kasapaka Rubenraju. (2022). AN EXPLAINABLE LEARNING ANALYTICS FRAMEWORK FOR PREDICTING STUDENT ACADEMIC PERFORMANCE AND IDENTIFYING AT-RISK LEARNERS IN ONLINE HIGHER EDUCATION. Pegem Journal of Education and Instruction, 12(4), 425–434. Retrieved from https://www.pegegog.net/index.php/pegegog/article/view/5167

Issue

Vol. 12 No. 4 (2022): PEGEGOG

Section

Article

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