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AmonAI: A Students Academic Performances Prediction System

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Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 311)


This paper presents a system, called AmonAI, that predicts the academic performances of students in the LMD system. The approach used allows to establish, for each of the teaching units of a given semester, some estimates of the students results. To achieve this, various machine learning techniques were used. In order to choose the best model for each teaching unit, we have tested 9 different algorithms offered by the Python Scikit-learn library to make predictions. The experiments were performed on data collected over two years at “Institut de Formation et de Recherche en Informatique (IFRI)” of University of Abomey-Calavi, Benin. The results obtained on the test data reveal that, on five of the nine teaching units for which the work was conducted, we obtain an F2-score of at least 75% for the classification and an RMSE of less than or equal to 2.93 for the regression. The solution therefore provides relatively good results with regard to the dataset used.


  • Students performances prediction
  • Machine learning
  • Classification
  • Regression
  • Teaching unit
  • LMD

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  1. 1.

    Amon (in Fongbé) is a prediction of the oracle , AI stands for Artificial Intelligence.

  2. 2.

    The positive class is then “Non-validated”.


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Correspondence to Vinasetan Ratheil Houndji .

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Houndayi, I.B., Houndji, V.R., Zohou, P.J., Ezin, E.C. (2020). AmonAI: A Students Academic Performances Prediction System. In: Zitouni, R., Agueh, M., Houngue, P., Soude, H. (eds) e-Infrastructure and e-Services for Developing Countries. AFRICOMM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 311. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41592-1

  • Online ISBN: 978-3-030-41593-8

  • eBook Packages: Computer ScienceComputer Science (R0)