Implementation of the Analytic Hierarchy Process for Student Profile Analysis

Data science is the discipline that allows the exploration and analysis of data in order to extract useful and relevant information for decision making and problem solving. In the educational domain, human experiences need to be synthesized in order to improve the success rate and help the responsible to make the best informed decision. Analytic Hierarchical Process (AHP) is one of the most widely used multi-criteria analysis techniques in decision making. It allows building models for various problems even in the case of insufficient observation data. This paper aims to, benefit from the potentials of AHP technique, to analyze students' profiles. Our objective is to detect and classify the most important factors that increase Moroccan student dropout and failure. We expect that this study is the first one that explores AHP, studying the Moroccan context and describing student profiles depending on variant criteria. It reveals, on the one hand, that Moroccan student failure is strongly related to their family and behavioral characteristics. Indeed, lack of motivation, family instability and lack of responsibility are the top three factors causing failure at the university. On the other hand, student dropout is strongly related to studying context, namely the lack of orientation and repeated failures in modules. These findings will enable the decision makers to develop adequate solutions to overcome these two scourges.