Predicting and Analysis of Students’ Academic Performance using Hybrid Techniques

Authors

  • M. AArul Rozario, R. GunaSundari

Keywords:

Educational Data Mining, Decision Tree, Hybrid Algorithm, Support Vector Machine, Prediction, students’ academic performance.

Abstract

This paper presents a framework for predicting the academic performance of first-year bachelor’s students in computer science courses using data mining techniques. With the exponential growth of data in educational databases, data mining offers a promising avenue for uncovering valuable insights and patterns. The framework employs classification methods including Decision Tree, Naive Bayes, and Multi-Layer Perception, implemented through the python tool, to construct prediction models for students’ academic achievement. Experimental evaluations are conducted to determine the most effective model, with a focus on accuracy. Furthermore, the study emphasizes the significance of utilizing the extracted knowledge to profile students and assess their likelihood of success in the first semester. This research contributes to the field of educational data mining, offering insights that can potentially enhance student outcomes in computer science education.

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References

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Published

05.06.2024

How to Cite

M. AArul Rozario. (2024). Predicting and Analysis of Students’ Academic Performance using Hybrid Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 4189–4195. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6132

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

License

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