Fairness of MOOC Completion Predictions Across Demographics and Contextual Variables

Author:

Lallé Sébastien,Bouchet François,Verger Mélina,Luengo Vanda

Publisher

Springer Nature Switzerland

Reference24 articles.

1. Baker, R.S., Hawn, A.: Algorithmic bias in education. Int. J. Artif. Intell. Educ. 1052–1092 (2021)

2. Bridgeman, B., Trapani, C., Attali, Y.: Considering fairness and validity in evaluating automated scoring. In: Proceedings of the Annual meeting of the National Council on Measurement in Education, San Diego, CA (2009)

3. Bridgeman, B., Trapani, C., Attali, Y.: Comparison of human and machine scoring of essays: differences by gender, ethnicity, and country. Appl. Meas. Educ. 25(1), 27–40 (2012)

4. Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Proceedings of the 1st Conference on Fairness, Accountability and Transparency, pp. 77–91 (2018)

5. Castelnovo, A., Crupi, R., Greco, G., Regoli, D., Penco, I.G., et. al.: A clarification of the nuances in the fairness metrics landscape. Sci. Rep. 12(1), 4209 (2022)

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