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Research Article · Open access · Published 27 January 2026

Association Rule Analysis of Qualitative Contribution Evaluation of Faculty Using Distributed Mining with Apriori Algorithm

Mylene Bello Ragobrio1, Rhoderick D. Malangsa2 & Rujube Hinoguin-Hermano3

Journal of Computer Science, Volume 21, Issue 11, pp. 2718–2725 · doi.org/10.3844/jcssp.2025.2718.2725

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Abstract

Teacher evaluation, where students serve as evaluators, is common in Philippine educational institutions. Student input based on teacher performance is considered crucial for fostering faculty development and professional advancement. The government implemented National Budget Circular (NBC) No. 461 which standardizes promotion procedures, encouraging equity and uniformity in faculty promotions among State Universities and Colleges (SUCs). Financial assistance comes from the Department of Budget and Management (DBM). The circular governs a mass promotion program with guidelines from the Commission on Higher Education (CHED) and the Philippine Association of State Colleges and Universities (PASUC) to oversee higher education standards and administration in the country. In order to find trends in faculty performance based on the Qualitative Contribution Evaluation (QCE), this study investigates the application of association rule mining, more especially the Apriori algorithm. In order to improve the quality of education that academic institutions offer, the goal is to assist them in better analyzing and comprehending the strengths and areas for improvement of their faculty. The four main components of the QCE are: (1) commitment (2) knowledge of subject, (3) teaching for independent learning and (4) management of learning. The dataset used in this investigation came from QCE Evaluation result of Easter Samar State University (ESSU), academic year 2020-2021. The QCE evaluation consisted of 4,654 instance with 7 attributes. The minimum support is 0.65 and confidence is 0.9. Using the Weka software, on the 10 rules generated, there should be given emphasize on the Management of Learning. Moreover, student evaluator chooses the Commitment as the primary parameter with the confidence of 0.88 or 88%, this implies that faculty should show consideration for students' capacity to assimilate material, integrate his learning goals with students' in a collaborative process, and remain accessible outside of class times.

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Ragobrio, M. B., Malangsa, R. D. & Hinoguin-Hermano, R. (2025). Association Rule Analysis of Qualitative Contribution Evaluation of Faculty Using Distributed Mining with Apriori Algorithm. Journal of Computer Science, 21(11), 2718–2725. https://doi.org/10.3844/jcssp.2025.2718.2725

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Association Rule Analysis of Qualitative Contribution Evaluation of Faculty Using Distributed Mining with Apriori AlgorithmJournal of Computer Science · Volume 21, Issue 11 · pp. 2718–2725 · CC BY 3.0Download