An Educational Data Mining Model for Predicting Student Performance in Programming Course

Abstract

This paper presents an educational data mining model for predicting student performance in programming courses. Identifying variables that predict student programming performance may help educators. These variables are influenced by various factors. The study engages factors like students' mathematical background, programming aptitude, problem solving skills, gender, prior experience, high school mathematics grade, locality, previous computer programming experience, and e learning usage. The proposed model includes three phases; data preprocessing, attribute selection and rule extraction algorithm.