Abstract
Learning curves are a crucial tool to accurately measure learners skills and give meaningful feedback in intelligent tutoring systems. Here we discuss various ways of building learning curves from empirical data for the Additive Factor model (AFM) and highlight their limitations. We focus on the impact of student attrition, a.k.a. attrition bias. We propose a new way to build learning curves, by combining empirical observations and AFM predictions. We validate this proposition on simulated data, and test it on real datasets.
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Notes
- 1.
Error curves show probability of error, Learning curves probability of success.
- 2.
Both curves are shown in the PSLC Datashop platform diagnostics.
- 3.
Using parameters \(\widehat{\beta }\) and \(\widehat{\gamma }\) estimated on observations outcomes \(o_k^{it}\) only.
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M. McLaughlin, C. Tipper for help with Datashop ds76.
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Goutte, C., Durand, G., Léger, S. (2018). On the Learning Curve Attrition Bias in Additive Factor Modeling. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_21
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DOI: https://doi.org/10.1007/978-3-319-93846-2_21
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