A machine learning algorithm framework for predicting students performance: A case study of baccalaureate students in Morocco
| Autor: | Aimad Qazdar, Chihab Cherkaoui, Driss Mammass, Brahim Er-Raha |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
05 social sciences Educational technology 050301 education Computer-Assisted Instruction Academic achievement Library and Information Sciences Machine learning computer.software_genre Educational data mining Performance results Education Important research 0502 economics and business Management system ComputingMilieux_COMPUTERSANDEDUCATION 050211 marketing Artificial intelligence business 0503 education Algorithm computer At-risk students |
| Zdroj: | Education and Information Technologies. 24:3577-3589 |
| ISSN: | 1573-7608 1360-2357 |
| DOI: | 10.1007/s10639-019-09946-8 |
| Popis: | The use of machine learning with educational data mining (EDM) to predict learner performance has always been an important research area. Predicting academic results is one of the solutions that aims to monitor the progress of students and anticipates students at risk of failing the academic pathways. In this paper, we present a framework for predicting student performance based on Machine Learning algorithm at H.E.K high school in Morocco from 2016 to 2018. The proposed model was analyzed and tested using student’s data collected from The School Management System “MASSAR” (SMS-MASSAR). The dataset used in this study concerns 478 Physics students during the school years: 2015–2016, 2016–2017 and 2017–2018. The predictive performance results showed that our model can make more precise predictions of student’s performance. |
| Databáze: | OpenAIRE |
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