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).
Cite: Frederick F. Patacsil, Paulo V. Cenas, Bobby F. Roaring, Jennifer M. Parrone, and Daniel Bezalel A. Garcia, "Evaluating Pangasinan State University Faculty Performance Using Associative Rule Analysis," International Journal of Information and Education Technology vol. 12, no. 1, pp. 21-29, 2022.
Copyright © 2022 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).