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Embodied cognition posits that human-environment interac- tion positively impacts thinking and learning, making it a valuable ped- agogical tool. Technology in teaching and learning has seen tremendous maturation, such as the development of Intelligent Tutoring Systems (ITS). However, most ITS provide static learning experiences that do not incorporate embodiment, movement, and interaction with the space around the learner. This paper examines the results of using an embod- ied tutoring system across three case studies with different dimensions of embodiment. In all cases, we found trends highlighting how embodied tutoring systems can support learning. We also discuss different ways to incorporate embodiment into future research on ITS. 

Domain modeling is an important task in designing, developing, and deploying intelligent tutoring systems and other adaptive instructional systems. We focus here on the more specific task of automatically extracting a domain model from textbooks. In particular, this paper explores using multiple textbook indexes to extract a domain model for computer programming. Our approach is based on the observation that different experts, i.e., authors of intro-to-programming textbooks in our case, break down a domain in slightly different ways, and identifying the commonalities and differences can be very revealing. To this end, we present automated approaches to extracting domain models from multiple textbooks and compare the resulting common domain model with a domain model created by experts. Specifically, we use approximate string-matching approaches to increase coverage of the resulting domain model and majority voting across different textbooks to discover common domain terms related to computer programming. Our results indicate that using approximate string matching gives more accurate domain models for computer programming with increased precision and recall. By automating our approach, we can significantly reduce the time and effort required to construct high-quality domain models, making it easy to develop and deploy tutoring systems. Furthermore, we obtain a common domain model that can serve as a benchmark or skeleton that can be used broadly and adapted to specific needs by others.