An effective decision-making support for student academic path selection using Machine Learning

Abstract

In Benin, after the GCSE (General Certificate of Secondary Education), learners can either enroll in a Technical and Vocational Education and Training (TVET), or further their studies in the general education. Majority of those who take the latter path enroll in Senior High School by choosing the Biology stream or field of study. However, most of them do not have the abilities required to succeed in this field. For instance, for the last edition of the Senior Secondary Education Certificate (French baccalaureate) held in June 2022 in Benin, the Biology field of study had a low success rate of 42%. Therefore, one may consider that there is a problem in the orientation of the students. In recent years, Machine Learning have been used in almost every field to optimize processes or to assist in decision-making. Improving academic performance has always been of general interest. And, good academic performance implies good academic orientation. The goal of this study is to optimally help learners who have just obtained their GCSE to select their field of study. For this purpose, two major elements are predicted : i) Scientific or Literary ability of students, ii) Literature or Mathematics and Physical Sciences (MPS) or Biology stream of learners. More precisely, the average marks in Mathematics, Physics and Chemistry Technology (PCT) and Biology from 6th to 9th grade for 325 students are used. Machine Learning algorithms such as Decision Tree, Random Forest, Linear Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), and Logistic Regression are used to predict learners’ ability and the stream. As a result, for learners’ ability prediction, we obtained the best accuracy of 99% with the random forest algorithm for a split that reserved around 21% of the dataset for testing. As for the learners’ stream prediction, we obtained the best accuracy of 95% with the Linear SVC algorithm for a split that reserved around 20% of the dataset for testing. This study contributes to Educational Data Mining (EDM) by performing academic data exploration using numerous methods. Furthermore, it provides a tool to ease students academic path selection, which may be used by educational institutes to ensure student performance. This paper presents the steps and the outputs of the study, we performed with some recommendations for future research.