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Evaluating Pangasinan State University Faculty Performance Using Associative Rule Analysis

Frederick F. Patacsil, Paulo V. Cenas, Bobby F. Roaring, Jennifer M. Parrone, and Daniel Bezalel A. Garcia

Abstract—Evaluating faculty members' performance is a very complex area to study. In addition, predicting the performance of these faculty members is a very difficult and challenging task. However, the core of education is teaching and learning, and teaching-learning works to its fullest when there are effective teachers. Measuring the effectiveness of faculty members is done based on the student evaluation of faculty. This research aims to develop a model to predict the performance of the faculty members using associative rule based on the existing evaluation form used by PSU to evaluate faculty members. The model is designed to utilize the knowledge of text analytics rule capabilities that will provide great support for the decision-making of Pangasinan State University in the Philippines. The result reveals that the term good is still the top one terms occurred for all campuses followed by teaching. The results indicated that teacher/faculty members on all campuses are good teachers. Associating words reveal that "teaching good subject/topic," "explains simply" and other meaningful associated words can be utilized to evaluate the performance of the teacher. The results exposed not only the quantitative values of faculty evaluation it also exposed the qualitative opinion of the students in the performance of their faculty members. This study reveals important aspects of the faculty member's teaching performance in terms of words/association of words that will describe their teaching performance. The results can be utilized in coaching and mentoring faculty members to cope with their weaknesses. The proposed model can be utilized by Pangasinan State University to evaluate the faculty members in terms of their teaching performance by utilizing the comments/opinions of the students.

Index Terms—Association rule, data analytics, faculty performance.

The authors are with Pangasinan State University, Philippines (e-mail: frederick_patacsil@yahoo.co.uk, arctangent2008@yahoo.com, bob_roaring@yahoo.com, plukjenniferparrone@gmail.com, dagarcia@up.edu.ph).


Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (Since 2019), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Electronic Journals Library, Google Scholar, Crossref, etc.
  • E-mail: ijiet@ejournal.net

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