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Simulated learners represent computational theories of human learning that can be used to evaluate educational technologies, provide practice opportunities for teachers, and advance our theoretical understanding of human learning. A key challenge in working with simulated learners is evaluating the accuracy of the simulation compared to the behavior of real human students. One way this evaluation is done is by comparing the error-rate learning curves from a population of human learners and a corresponding set of simulated learners. In this paper, we argue that this approach misses an opportunity to more accurately capture nuances in learning by treating all errors as the same. We present a simulated learner system, the Apprentice Learner (AL) Architecture, and use this more nuanced evaluation to demonstrate ways in which it does and does not explain and accurately predict student learning in terms of the reduction of different kinds of errors over time as it learns, as human students do, from an Intelligent Tutoring System (ITS).
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Toward Stable Asymptotic Learning with Simulated Learners
Simulations of human learning have shown potential for supporting ITS authoring and testing, in addition to other use cases. To date, simulated learner technologies have often failed to robustly achieve perfect performance with considerable training. In this work we identify an impediment to producing perfect asymptotic learning performance in simulated learners and introduce one significant improvement to the Apprentice Learner Framework to this end.
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- Award ID(s):
- 1824257
- PAR ID:
- 10277329
- Editor(s):
- Roll I., McNamara D.
- Date Published:
- Journal Name:
- Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science
- Volume:
- 12749
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/https://doi.org/10.1007/978-3-030-78270-2_69
