Should Learning Analytics Models Include Sensitive Attributes? Explaining the Why-Reference-Cited by-同舟云学术

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Should Learning Analytics Models Include Sensitive Attributes? Explaining the Why

Published:2022 Issue: Volume: Page:1-13
ISSN:1939-1382
Container-title:IEEE Transactions on Learning Technologies
language:
Short-container-title:IEEE Trans. Learning Technol.

Author:

Deho Oscar Blessed¹^ORCID,Joksimovic Srecko²^ORCID,Li Jiuyong¹^ORCID,Zhan Chen³,Liu Jixue¹^ORCID,Liu Lin¹^ORCID

Affiliation:

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

2. UniSA Education Futures, University of South Australia, Adelaide, SA, Australia

3. Centre for Change and Complexity in Learning (C3L), University of South Australia, Adelaide, SA, Australia

Funder

Australian Research Council

Postgraduate Research Scholarship of University of South Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,General Engineering,Education

Link

http://xplorestaging.ieee.org/ielx7/4620076/4620077/09969944.pdf?arnumber=9969944

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1. Advancing equity and inclusion in educational practices with AI‐powered educational decision support systems (AI‐EDSS);British Journal of Educational Technology;2024-07-10

2. Implementing equitable and intersectionality‐aware ML in education: A practical guide;British Journal of Educational Technology;2024-05-23

3. What Fairness Metrics Can Really Tell You: A Case Study in the Educational Domain;Proceedings of the 14th Learning Analytics and Knowledge Conference;2024-03-18

4. When the Past != The Future: Assessing the Impact of Dataset Drift on the Fairness of Learning Analytics Models;IEEE Transactions on Learning Technologies;2024

5. Guiding Students Towards Successful Assessments Using Learning Analytics From Behavioral Data to Formative Feedback;Advances in Analytics for Learning and Teaching;2024

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