Education and Information Technologies

, Volume 24, Issue 6, pp 3577–3589 | Cite as

A machine learning algorithm framework for predicting students performance: A case study of baccalaureate students in Morocco

  • Aimad QazdarEmail author
  • Brahim Er-Raha
  • Chihab Cherkaoui
  • Driss Mammass


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.


Machine learning Educational data mining Decision support tools Predictive model Academic performance 



I would like to express my sincere gratitude to the H.E.K school director (Morocco), for his collaboration, particularly in the collection of data, as well as for his suggestions and encouragements made to the development of this model.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.IMA Laboratory, ENSA / IRF-SIC Laboratory, FSAIbn Zohr UniversityAgadirMorocco
  2. 2.IRF-SIC Laboratory, ENCGIbn Zohr UniversityAgadirMorocco
  3. 3.IRF-SIC Laboratory, FSIbn Zohr UniversityAgadirMorocco

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