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Title: Co-Design of AI-enhanced Learning Analytics: Considerations for Ensuring Diversity, Equity, Justice
Authors: Celik, Ismail
Muukkonen, Hanni
Keywords: Learning Sciences
Issue Date: 2022
Publisher: International Society of the Learning Sciences
Citation: Celik, I. & Muukkonen, H. (2022). Co-design of AI-enhanced learning analytics: Considerations for ensuring diversity, equity, justice. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.), Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022 (pp. 1906-1907). International Society of the Learning Sciences.
Abstract: The majority of the participatory/co-design research has somehow ignored the considerations tackling the diversity, equity, and justice challenges in the design process of learning analytics. We suggest four considerations: (1) while determining the needs and expectations of targeted learners, consider all subgroups of populations (2) determine the transparent and inclusive algorithm for meeting the needs (3) consider diversity and equity for model training data (4) consider heterogeneous participants while testing prototypes of learning analytics.
Description: Poster
Appears in Collections:ISLS Annual Meeting 2022

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