Course Elective Requesting Platform and Recommender System Using Apriori and Decision Tree Analysis

Cherry Rose V. Concha, Elmerito D. Pineda, Isagani M. Tano, Ace C. Lagman, Jayson M. Victoriano, Jonilo C. Mababa and Jovy Jay D.S. Cabrera
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Cherry Rose V. Concha: Graduate School Department, La Consolacion University Philippine, Bulihan, City of Malolos, Bulacan, Philippines
Elmerito D. Pineda: Graduate School Department, La Consolacion University Philippine, Bulihan, City of Malolos, Bulacan, Philippines
Isagani M. Tano: Graduate School Department, La Consolacion University Philippine, Bulihan, City of Malolos, Bulacan, Philippines
Ace C. Lagman: Graduate School Department, La Consolacion University Philippine, Bulihan, City of Malolos, Bulacan, Philippines
Jayson M. Victoriano: Graduate School Department, La Consolacion University Philippine, Bulihan, City of Malolos, Bulacan, Philippines
Jonilo C. Mababa: Graduate School Department, La Consolacion University Philippine, Bulihan, City of Malolos, Bulacan, Philippines
Jovy Jay D.S. Cabrera: Graduate School Department, La Consolacion University Philippine, Bulihan, City of Malolos, Bulacan, Philippines

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 6, 834-839

Abstract: This paper presents an Elective Recommender System based on the Apriori Algorithm and Decision Tree Analysis for enhancing elective course selection in higher education. The system uses historical student performance data to recommend electives aligned with students' academic strengths and career goals. Association rule mining is used to recommend elective combinations based on past trends, while the Decision Tree algorithm is used in personalized recommendations with success likelihood. The system's performance was evaluated using the ISO/IEC 25010 Software Quality Model, yielding high scores in functional suitability, usability, and performance efficiency. The results show the system's potential in assisting both students and academic advisors in making data-driven elective course decisions.

Date: 2025
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