Adaptive Robot Language Tutoring Based on Bayesian Knowledge Tracing and Predictive Decision-Making

• Published in: 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI pp. 128 - 136
• Main Authors: Schodde, Thorsten, Bergmann, Kirsten, Kopp, Stefan
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 06.03.2017
• Series: ACM Conferences
• Subjects: Adaptation models; Applied computing > Education > Interactive learning environments; Assistive robotics; Bayes methods; Bayesian Knowledge Tracing; Computing methodologies > Artificial intelligence > Knowledge representation and reasoning > Cognitive robotics; Computing methodologies > Artificial intelligence > Knowledge representation and reasoning > Probabilistic reasoning; Decision making; Education; Hidden Markov models; Human-centered computing > Human computer interaction (HCI) > Empirical studies in HCI; Language tutoring; Robots; Task analysis; language tutoring; assistive robotics; education; Bayesian knowledge tracing; decision making
• ISBN: 9781450343367; 1450343368
• ISSN: 2167-2148
• DOI: 10.1145/2909824.3020222

In this paper, we present an approach to adaptive language tutoring in child-robot interaction. The approach is based on a dynamic probabilistic model that represents the inter-relations between the learner's skills, her observed behaviour in tutoring interaction, and the tutoring action taken by the system. Being implemented in a robot language tutor, the model enables the robot tutor to trace the learner's knowledge and to decide which skill to teach next and how to address it in a game-like tutoring interaction. Results of an evaluation study are discussed demonstrating how participants in the adaptive tutoring condition successfully learned foreign language words.