Assessing the Fairness of Course Success Prediction Models in the Face of (Un)equal Demographic Group Distribution-Reference-Cited by-同舟云学术

Assessing the Fairness of Course Success Prediction Models in the Face of (Un)equal Demographic Group Distribution

Published:2023-07-20 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Tenth ACM Conference on Learning @ Scale
language:
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Author:

Deho Oscar Blessed¹^ORCID,Joksimovic Srecko¹^ORCID,Liu Lin¹^ORCID,Li Jiuyong¹^ORCID,Zhan Chen¹^ORCID,Liu Jixue¹^ORCID

Affiliation:

1. University of South Australia, Adelaide, SA, Australia

Funder

Postgraduate Research Scholarship of University of South Australia

Australian Research Council

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3573051.3593381

Reference44 articles.

1. Henry Anderson , Afshan Boodhwani , and Ryan S Baker . 2019 . Assessing the Fairness of Graduation Predictions .. In Proceedings of The 12th International Conference on Educational Data Mining (EDM 2019). 488 -- 491. Henry Anderson, Afshan Boodhwani, and Ryan S Baker. 2019. Assessing the Fairness of Graduation Predictions.. In Proceedings of The 12th International Conference on Educational Data Mining (EDM 2019). 488 -- 491.

2. Lovenoor Aulck , Dev Nambi , Nishant Velagapudi , Joshua Blumenstock , and Jevin West . 2019. Mining University Registrar Records to Predict First-Year Undergraduate Attrition . International Educational Data Mining Society ( 2019 ). Lovenoor Aulck, Dev Nambi, Nishant Velagapudi, Joshua Blumenstock, and Jevin West. 2019. Mining University Registrar Records to Predict First-Year Undergraduate Attrition. International Educational Data Mining Society (2019).

3. Algorithmic Bias in Education

4. Balancing Strategies and Class Overlapping

5. Learning Analytics and Fairness: Do Existing Algorithms Serve Everyone Equally?

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