Decision Tree Algorithm Use in Predicting Students’ Academic Performance in Advanced Programming Course - MURAINA, Ismail Olaniyi; Aiyegbusi, Edward; Abam, Solomon (International Journal of Higher Education Pedagogies, 2023)
Student’s academic performance or achievement has from time to time been a subject of discourse to academicians, scholars, researchers and educational institutions all over the globe. To this regard, schools are expected to play major and active roles in ensuring that students actually have good performance at end of their programmes. The academic performance is normally used to classify or predict how students would be ultimately capable to withstand and face future challenges after graduation. Students’ academic performance/achievement in any course of study plays a vital role in contributing and producing outstanding students who will be future viable leaders. The use of algorithms to classify and predict students’ academic performance/achievement is not new in machine learning using different techniques like neural network, logistic regression, decision tree and many more. This study classifies and predicts with the use of graphical technique called Decision Tree. The dataset was built from student’ attendance, practical assessment, assignment, ability to complete a free related course on internet, test score, and examination grade; the dataset was divided into training test and testing set. The training test was used to build and validate the decision tree algorithm (CHAID) while testing set was used to evaluate CHAID on the overall accuracy, sensitivity, and specificity. The results show that decision tree algorithm makes classification and prediction visible and clear with the use of graphics to display the results. Hence, the model built produces 96% accuracy.