Decision Tree Algorithm Use in Predicting Students’ Academic Performance in Advanced Programming Course

Student’s academic performance or achievement has from time to been a subject of discourse academicians, scholars, researchers and educational institutions all over the globe. To this regard, schools are expected play major active roles in ensuring that students actually have good at end their programmes. The is normally used classify predict how would be ultimately capable withstand face future challenges after graduation. Students’ performance/achievement any course study plays vital role contributing producing outstanding who will viable leaders. use algorithms students’ not new machine learning using different techniques like neural network, logistic regression, decision tree many more. This classifies predicts with graphical technique called Decision Tree. dataset was built student’ attendance, practical assessment, assignment, ability complete free related on internet, test score, examination grade; divided into training testing set. build validate algorithm (CHAID) while set evaluate CHAID overall accuracy, sensitivity, specificity. results show makes classification prediction visible clear graphics display results. Hence, model produces 96% accuracy.