Version 1
: Received: 18 August 2023 / Approved: 18 August 2023 / Online: 18 August 2023 (09:52:57 CEST)
How to cite:
Hennebelle, A.; Ismail, L.; Linden, T. Schools Students Performance with Artificial Intelligence Machine Learning: Features Taxonomy, Methods and Evaluation. Preprints2023, 2023081358. https://doi.org/10.20944/preprints202308.1358.v1
Hennebelle, A.; Ismail, L.; Linden, T. Schools Students Performance with Artificial Intelligence Machine Learning: Features Taxonomy, Methods and Evaluation. Preprints 2023, 2023081358. https://doi.org/10.20944/preprints202308.1358.v1
Hennebelle, A.; Ismail, L.; Linden, T. Schools Students Performance with Artificial Intelligence Machine Learning: Features Taxonomy, Methods and Evaluation. Preprints2023, 2023081358. https://doi.org/10.20944/preprints202308.1358.v1
APA Style
Hennebelle, A., Ismail, L., & Linden, T. (2023). Schools Students Performance with Artificial Intelligence Machine Learning: Features Taxonomy, Methods and Evaluation. Preprints. https://doi.org/10.20944/preprints202308.1358.v1
Chicago/Turabian Style
Hennebelle, A., Leila Ismail and Tanya Linden. 2023 "Schools Students Performance with Artificial Intelligence Machine Learning: Features Taxonomy, Methods and Evaluation" Preprints. https://doi.org/10.20944/preprints202308.1358.v1
Abstract
. Identifying students who might have difficulty in their course of studies ahead of time is crucial. There can be many reasons for performance issues, such as personality, family, social, and/or economic. We advocate that educational systems should use machine learning to predict students’ performance based on performance factors. This would allow educational professionals and institutions to put in place a preventive plan to help students towards achievements of their educational goals and success. In this chapter, we propose a student performance prediction method and evaluate its performance. We provide a taxonomy of performance factors that help to gauge students performance from different perspectives and give insights on the categories and features that have more significant impact on students’ performance. The results of this work can be used by education institutions to put in place a student-centric approach to tackle performance issues before they create long-term effects on student’s life. In addition, it will help education policymakers to introduce a tailored approach for the population in specific areas.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
