Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Amon (in Fongbé) is a prediction of the oracle Fâ, AI stands for Artificial Intelligence.
- 2.
The positive class is then “Non-validated”.
References
Shahiri, A.M., Husain, W., Rashid, N.A.: A review on predicting student’s performance using data mining techniques. School of Computer Sciences, Universiti Sains Malaysia (2015)
Pojon, M.: Using machine learning to predict student performance. University of Tampere (2017)
Daud, A., Aljohani, N.R., Abbasi, R.A., Lytras, M.D., Abbas, F., Alowibdi, J.S.: Predicting student performance using advanced learning analytics. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 415–421 (2017)
Cortez, P., Silva, A.: Using data mining to predict secondary school student performance. University of Minho (2008)
Meier, Y., Xu, J., Atan, O., van der Schaar, M.: Predicting grades. IEEE Trans. Signal Process. 64(4), 959–972 (2016)
Agrawal, H., Mavani, H.: Student performance prediction using machine learning. Int. J. Eng. Res. Technol. 4(03), 111–113 (2015)
Github: Student Performance Prediction. https://github.com/sachanganesh/student-performance-prediction. Accessed 9 Jan 2019
Clusteval: Integrative Clustering Evaluation Framework F2-Score. https://clusteval.sdu.dk/1/clustering_quality_measures/5. Accessed 7 Nov 2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
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. https://doi.org/10.1007/978-3-030-41593-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-41593-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41592-1
Online ISBN: 978-3-030-41593-8
eBook Packages: Computer ScienceComputer Science (R0)