Abstract—The increase of students attending tertiary levels creates challenges for educational institutions to keep track on university status through student’s academic performance. This study is a prospective investigation of the academic predictors of academic performance of Bachelor of Science in Information Technology students of selected university in the Philippines. It aims to analyze the student’s performance using predictive data mining techniques, specifically, on classification. The results of the study shows that students’ failure or success in passing their enrolled professional course has nothing to do with their gender. Moreover, consideration in the number of units in designing the curriculum should also be considered since data shows that a very high passing rate for summer class was evident, the period when the students have less course load.
Index Terms—Data mining, classification techniques, non-technical skills.
The authors are with the Pangasinan State University-San Carlos City Campus, Philippines (e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com).
Cite: Leah G. Rodriguez, Christopher A. Rodriguez, Jasmin D. Niguidula, and Dawn Iris Calibo, "Analysis of Students Performance: Input to Program Enhancement of Students in Computing," International Journal of Information and Education Technology vol. 9, no. 6, pp. 429-432, 2019.