Bahloul Bensassi

Work place: Faculty of Sciences Aïn Chock, University Hassan II of Casablanca, Morocco



Research Interests: Computational Science and Engineering, Computer Architecture and Organization, Data Structures and Algorithms


Bahloul Bensassi is a professor in physics department at faculty of sciences, Hassan II University, Casablanca, Morocco. He is responsible of logistics engineering master degree and the Electronics, Electrical, Automatic and Industrial computing master degree. His main research interests are modeling logistic systems, electronics, automatic and industrial computing.

Author Articles
Machine Learning Algorithms for Quantifying the Role of Prerequisites in University Success

By Najat Messaoudi Ghizlane Moukhliss Jaafar K. Naciri Bahloul Bensassi

DOI:, Pub. Date: 8 Dec. 2022

The use of machine learning algorithms for higher education performance assessment is an emerging area of research and several works have focused on student performance and related problems. The preliminary goal of this work is to determine and quantify the role of prerequisites in academic success by using machine learning algorithms with the Weka environment. The main objective is the development of a tool based on machine learning algorithms for the prediction of future results for a training program based solely on the previous academic profiles of the students. The interest is to link whether success in previous courses is associated with success in subsequent target courses. This will help to improve the planning of course sequences in a training program on the one hand and the overall academic students’ success on the other. The proposed methodology is applied for the analysis of the role of the prerequisites influencing courses success of a training course in Mathematical and Computer Sciences in a Moroccan university. For this purpose, we use several classification algorithms such as Random Forest, J48, and Multilayer Perceptron.
Preliminary results show that the correlation between the prerequisite reliability rates of the courses studied and the accuracy with which the learning algorithms predict the success outcomes of these courses is confirmed.
Also, these results show that the best accuracy and the best Receiver Operator Characteristic ROC area are obtained by using Random Forest algorithm and have reached 86% for the accuracy and 75.6% for the ROC area.

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