Domain-General Tutor Authoring with Apprentice Learner Models
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Domain-General Tutor Authoring with Apprentice Learner Models Christopher J. MacLellan1
· Kenneth R. Koedinger2
Accepted: 30 August 2020 © The Author(s) 2020
Abstract Intelligent tutoring systems are effective for improving students’ learning outcomes (Pane et al. 2013; Koedinger and Anderson, International Journal of Artificial Intelligence in Education, 8, 1–14, 1997; Bowen et al. Journal of Policy Analysis and Management, 1, 94–111 2013). However, constructing tutoring systems that are pedagogically effective has been widely recognized as a challenging problem (Murray 2003; Murray, International Journal of Artificial Intelligence in Education, 10, 98– 129, 1999). In this paper, we explore the use of computational models of apprentice learning, or computer models that learn interactively from examples and feedback, for authoring expert-models via demonstrations and feedback (Matsuda et al. International Journal of Artificial Intelligence in Education, 25(1), 1–34 2014) across a wide range of domains. To support these investigations, we present the Apprentice Learner Architecture, which posits the types of knowledge, performance, and learning components needed for apprentice learning. We use this architecture to create two models: the Decision Tree model, which non-incrementally learns skills, and the Trestle model, which instead learns incrementally. Both models draw on the same small set of prior knowledge (six operators and three types of relational knowledge) to support expert model authoring. Despite their limited prior knowledge, we This article belongs to the Topical Collection: Creating and improving adaptive learning: Smart authoring tools and processes Guest Editors: Stephen B. Gilbert, Andrew M. Olney and Kelly Rivers This work was supported in part by a Graduate Training Grant awarded to Carnegie Mellon University by the Department of Education (#R305B090023 and #R305A090519), by the Pittsburgh Science of Learning Center, which is funded by the NSF (#SBE-0836012), two National Science Foundation Awards (#DRL-0910176 and #DRL-1252440), and by a DARPA award (#HR00111990055). The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. We would also like to thank Carnegie Learning, Inc. for providing the Cognitive Tutor data that supported this work. Christopher J. MacLellan
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Extended author information available on the last page of the article.
International Journal of Artificial Intelligence in Education
demonstrate their use for efficiently authoring a novel experimental design tutor and show that they are capable of learning an expert model for seven additional tutoring systems that teach a wide range of knowledge types (associations, categories, and skills) across multiple domains (language, math, engineering, and science). This work shows that apprentice learner models are efficient for authoring tutors that would be difficu
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