Enhancing Algorithmic Fairness in Student Performance Prediction Through Unbiased and Equitable Machine Learning Models-Reference-Cited by-同舟云学术

Enhancing Algorithmic Fairness in Student Performance Prediction Through Unbiased and Equitable Machine Learning Models

Published:2024 Issue: Volume: Page:418-426
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:en
Short-container-title:

Author:

de Souza Cabral Luciano^ORCID,Dwan Pereira Filipe^ORCID,Ferreira Mello Rafael^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-64315-6_39

Reference17 articles.

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2. Azcona, D., Hsiao, I.H., Smeaton, A.F.: Personalizing computer science education by leveraging multimodal learning analytics. In: 2018 IEEE Frontiers in Education Conference (FIE), pp. 1–9 (2018). https://doi.org/10.1109/FIE.2018.8658596

3. Barney, N.: Diversity, equity and inclusion (DEI), Technical report, TechTarget (2023), https://www.techtarget.com/searchhrsoftware/definition/diversity-equity-and-inclusion-DEI

4. Bellamy, R.K.E., et al.: AI fairness 360: an extensible toolkit for detecting and mitigating algorithmic bias. IBM J. Res. Dev. 63(4/5), 4:1–4:15 (2019). https://doi.org/10.1147/JRD.2019.2942287

5. Bellamy, R.K.E., et al.: AI fairness 360: an extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943 (2018). https://api.semanticscholar.org/CorpusID:52922804