Home⇛Isabela State University Linker: Journal of Education, Social Sciences and Allied Health⇛vol. 1 no. 1 (2024)

Generating Licensure Examination Performance Models Using JRip Classifier: A Data Mining Application in Civil Engineering

Rafael Klent R. Llingo | Edrick Simon J. Poniento | Edwin R. Arboleda | Mark Ivan V. Contemprato

Discipline: information systems

 

Abstract:

The purpose of this study was to create performance models for civil engineer licensure examinations using the JRip classifier. It identified the attributes that were significant to the response attribute, generated prediction models using JRip classifiers of WEKA, and determined how likely a CE graduate pass the CE Licensure Examination. The respondents were obtained from the CE graduates of Cavite State University Indang Main Campus who took a CE board examination from November 2016 to May 2019. The results obtained indicated the significance of the subject AENG 65, as well as CENG 65B and CENG 130 in predicting the CE Licensure Examination. The CE graduates were predicted to fail if their grade of AENG 65 is greater than or equal to 3 and CENG 135 is less than or equal to 2.5, and if CENG 120A and MATH 21B are greater than or equal to 2.75 and CENG 106 is less than 1.75. It also further concluded that if DCEE27 is greater than equal to 2.5 and the CENG 22A is greater than equal to 3 and the grade of CENG 110A is less than or equal to 2.75, then the CE graduates would fail the Licensure exam.



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