Exploring cross-country prediction model generalizability in MOOCs
Date
2023
Authors
Andres Bray, J.M.
Hutt, S.
Baker, R.S.
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Conference paper
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Proceedings Of The Tenth Acm Conference On Learning @ Scale, L@s 2023, 2023, pp.183-193
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10th ACM Conference on Learning at Scale (L at S) (20 Jul 2023 - 22 Jul 2023 : Copenhagen, Denmark)
Abstract
Massive Open Online Courses (MOOCs) have increased the accessibility of quality educational content to a broader audience across a global network. They provide access for students to material that would be difficult to obtain locally, and an abundance of data for educational researchers. Despite the international reach of MOOCs, however, the majority of MOOC research does not account for demographic differences relating to the learners' country of origin or cultural background, which have been shown to have implications on the robustness of predictive models and interventions. This paper presents an exploration into the role of nation-level metrics of culture, happiness, wealth, and size on the generalizability of completion prediction models across countries. The findings indicate that various dimensions of culture are predictive of cross-country model generalizability. Specifically, learners from indulgent, collectivist, uncertainty-accepting, or short-term oriented, countries produce more generalizable predictive models of learner completion.
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Copyright 2023 ACM