Abstract—This study focused on the generation of the licensure examination performance models implementing PART and JRip classifiers. Specifically, it identified the attributes that are significant to the response attribute; it generated prediction models using the PART and JRip classifiers of WEKA; and it determined how likely is a reviewee to pass the LET. The respondents were obtained from the Education graduates of Isabela State University Cabagan campus who took a LET review and eventually took the September 2013 LET. The results obtained indicate the significance of the mock board exam, general weighted average of the reviewees in GenEd and MajorCore in predicting LET performance. The reviewee is predicted to fail the LET if he will obtain a mock board rating lower than 34% of the total points. It is further predicted that if the general weighted average in all the general education subjects is fair, or the general weighted average in all the general education subjects is fairly good and has a kinesthetic learning style, then the reviewee will fail the LET.

Index Terms—JRip, LET, PART, performance prediction.

I. M. Tarun is with the Isabela State University, Isabela, Philippines (e-mail: ivy_tarun@yahoo.com).
B. D. Gerardo, is with West Visayas State University, Iloilo, Philippines (e-mail: bgerardo@wvsu.edu.ph).
B. T. Tanguilig III is with the College of Information Technology Education, Technological Institute of the Philippines, Quezon City, Philippines (e-mail: bttanguilig_3@yahoo.com).

Cite:Ivy M. Tarun, Bobby D. Gerardo, and Bartolome T. Tanguilig III, "Generating Licensure Examination Performance Models Using PART and JRip Classifiers: A Data Mining Application in Education," International Journal of Computer and Communication Engineering vol. 3, no. 3, pp. 202-207, 2014.