Bouchard Kévin, Gonzales Lucas, Maitre Julien et Gaboury Sébastien. (2020). Features exploration for grades prediction using machine learning. Dans Catia Prandi et Johann Marquez-Barja (dir.), GoodTechs '20 : Proceedings of the 6th EAI International Conference on smart objects and technologies for social good. (p. 78-83). New York, NY, United States : Association for computing machinery.
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URL officielle: http://dx.doi.org/doi:10.1145/3411170.3411232
Résumé
The province of Quebec in Canada has begun to implement an important plan to bring a digital shift to the educational system. One of the key aspects of this plan is to implement a global electronic student file system. These electronic files encompass a lot of information that can in turn be used to monitor the progress of the students. In this paper, our team was able to obtain a large dataset from this new technological platform and used it to predict the grade of students. We tested up to 328 features and produced different datasets for classification. Moreover, different features selection methods were used. Finally, we were able to predict the end of the year final grade with up to 75% accuracy.
| Type de document: | Chapitre de livre |
|---|---|
| Date: | 2020 |
| Lieu de publication: | New York, NY, United States |
| Identifiant unique: | 10.1145/3411170.3411232 |
| Sujets: | Sciences naturelles et génie > Sciences mathématiques > Informatique |
| Département, module, service et unité de recherche: | Départements et modules > Département d'informatique et de mathématique |
| Éditeurs: | Prandi, Catia Marquez-Barja, Johann |
| Mots-clés: | classification, data mining, student grade, prediction |
| Déposé le: | 12 févr. 2021 14:15 |
|---|---|
| Dernière modification: | 12 févr. 2021 14:15 |
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