Utilizing Data Mining and Machine Learning for Enhancing Bachelor's Degree Outcomes and Predicting Students' Academic Success

Mohamed Sabiri, Yousef Farhaoui and Agoujil Said

Data and Metadata, 2023, vol. 2, 105

Abstract: This paper aims to conceptualize, design, and implement a Data Mining (DM) system integrated with machine learning within the realm of school management. The primary objective is to support the educational community and decision-makers in addressing the issue of school dropout and enhancing success rates at the certificate levels in Morocco, specifically focusing on the bachelor's degree examination in the qualifying cycle. The proposed system categorizes students five months prior to the exam date, facilitating targeted academic interventions for those at risk of course repetition or discontinuation. The DM system, operational throughout the school year, enhances the precision and effectiveness of schools and provincial administrations by identifying areas requiring additional support to improve end-of-year success rates and student performance. Project development is rooted in the collection and analysis of existing data from various departmental information systems, utilizing classification and regression algorithms to predict learner performance, success rates, and overall outcomes at the conclusion of certificate levels

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:2:y:2023:i::p:105:id:1056294dm2023105

DOI: 10.56294/dm2023105

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