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Computer Science > Machine Learning

arXiv:2212.08532 (cs)
[Submitted on 16 Dec 2022]

Title:Do Not Trust a Model Because It is Confident: Uncovering and Characterizing Unknown Unknowns to Student Success Predictors in Online-Based Learning

Authors:Roberta Galici, Tanja Käser, Gianni Fenu, Mirko Marras
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Abstract:Student success models might be prone to develop weak spots, i.e., examples hard to accurately classify due to insufficient representation during model creation. This weakness is one of the main factors undermining users' trust, since model predictions could for instance lead an instructor to not intervene on a student in need. In this paper, we unveil the need of detecting and characterizing unknown unknowns in student success prediction in order to better understand when models may fail. Unknown unknowns include the students for which the model is highly confident in its predictions, but is actually wrong. Therefore, we cannot solely rely on the model's confidence when evaluating the predictions quality. We first introduce a framework for the identification and characterization of unknown unknowns. We then assess its informativeness on log data collected from flipped courses and online courses using quantitative analyses and interviews with instructors. Our results show that unknown unknowns are a critical issue in this domain and that our framework can be applied to support their detection. The source code is available at this https URL.
Comments: Accepted as a full paper at the International Conference on Learning Analytics & Knowledge (LAK23)
Subjects: Machine Learning (cs.LG); Computers and Society (cs.CY)
Cite as: arXiv:2212.08532 [cs.LG]
  (or arXiv:2212.08532v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2212.08532
Related DOI: https://doi.org/10.1145/3576050.3576148

Submission history

From: Mirko Marras [view email]
[v1] Fri, 16 Dec 2022 15:32:49 UTC (2,475 KB)
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