Promoting Fairness in Academic Risk Prediction: An Evaluation of the Reweight Technique in Higher Education
Resumo
Este estudo avalia a justiça algorítmica na previsão de risco acadêmico utilizando dados de 1.796 estudantes universitários latino-americanos. Ao longo de 80 combinações de modelos e cenários de desequilíbrio, a AUC-ROC aumenta de 0,87 para 0,96 à medida que a quantidade de informações cresce. A técnica de reweight reduz as disparidades de DI e AOD entre grupos sensíveis, enquanto análises de bootstrap IC95% indicam ganhos significativos de equidade sem perda de desempenho preditivo.
Palavras-chave:
Análise de Aprendizagem, Predição de Risco Acadêmico, Justiça Algorítmica
Referências
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Bellamy, R.K.E., Dey, K., Hind, M., Hoffman, S.C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., et al.: AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM Journal of Research and Development 63(4/5), 4:1–4:15 (2019). DOI: 10.1147/JRD.2019.2942287.
Blow, C.H., Qian, L., Gibson, C., Obiomon, P., Dong, X.: Comprehensive validation on reweighting samples for bias mitigation via AIF360. Applied Sciences 14(9), 3826 (2024). DOI: 10.3390/app14093826.
Cechinel, C., Ochoa, X., Lemos dos Santos, H., Carvalho Nunes, J.B., Rodés, V., Marques Queiroga, E.: Mapping Learning Analytics initiatives in Latin America. British Journal of Educational Technology 51(4), 892–914 (2020). DOI: 10.1111/bjet.12941.
Feldman, M., Friedler, S.A., Moeller, J., Scheidegger, C., Venkatasubramanian, S.: Certifying and removing disparate impact. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 259–268. Association for Computing Machinery, Sydney, Australia (2015). DOI: 10.1145/2783258.2783311.
Friedler, S.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P., Roth, D.: A comparative study of fairness-enhancing interventions in machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency. pp. 329–338. Association for Computing Machinery, New York, NY, USA (2019). DOI: 10.1145/3287560.3287589.
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edn. Springer, New York (2009). DOI: 10.1007/978-0-387-84858-7.
Hlosta, M., Zdrahal, Z., Zendulka, J.: Ouroboros: Early identification of at-risk students without models based on legacy data. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference (LAK ’17). pp. 6–15. Association for Computing Machinery, Vancouver, Canada (2017). DOI: 10.1145/3027385.3027449.
Institute for the Future of Education (IFE): Educational Innovation Dataset Collection. Tecnológico de Monterrey Research Data Hub, Monterrey, Mexico (2024). DOI: 10.57687/FK2/UFM2QH. Last accessed 2026/06/24.
Jayaprakash, S.M., Moody, E.W., Lauría, E.J.M., Regan, J.R., Baron, J.D.: Early alert of academically at-risk students: an open source analytics initiative. Journal of Learning Analytics 1(1), 6–47 (2014). DOI: 10.18608/jla.2014.11.3.
Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowledge and Information Systems 33(1), 1–33 (2012). DOI: 10.1007/s10115-011-0463-8.
Kizilcec, R.F., Lee, H.: Algorithmic fairness in education. In: Holmes, W., Porayska-Pomsta, K. (eds.) The Ethics of Artificial Intelligence in Education: Practices, Challenges, and Debates, pp. 174–202. Routledge, New York (2022). DOI: 10.4324/9780429329067-10.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., Gulin, A.: CatBoost: unbiased boosting with categorical features. In: Advances in Neural Information Processing Systems 31 (NeurIPS 2018). pp. 6639–6649 (2018). DOI: 10.5555/3327757.3327770.
Raftopoulos, G., Davrazos, G., Kotsiantis, S.: Evaluating fairness strategies in educational data mining: a comparative study of bias mitigation techniques. Electronics 14(9), 1856 (2025). DOI: 10.3390/electronics14091856.
Romero, C., Cerezo, R., Bogarín, A., Sánchez-Santillán, M.: Educational process mining. In: Data Mining and Learning Analytics: Applications in Educational Research, chap. 1, pp. 1–28. Wiley (2016). DOI: 10.1002/9781118998205.ch1.
Saxena, N.A., Huang, K., DeFilippis, E., Radanovic, G., Parkes, D.C., Liu, Y.: How do fairness definitions fare? Examining public attitudes towards algorithmic definitions of fairness. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). pp. 99–106. Association for Computing Machinery, Honolulu, USA (2019). DOI: 10.1145/3306618.3314248.
Tecnológico de Monterrey: Monterrey Institute of Technology and Higher Education. Monterrey, Mexico (2024). Last accessed 2026/06/22.
Publicado
13/10/2026
Como Citar
SCHMIDT, Augusto et al.
Promoting Fairness in Academic Risk Prediction: An Evaluation of the Reweight Technique in Higher Education. In: ARTIGOS DE PESQUISA - CONFERÊNCIA LATINO-AMERICANA DA TECNOLOGIAS DE APRENDIZAGEM (LACLO), 21. , 2026, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 320-334.
