Work

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

Year: 2017

Type: conference-paper

Abstract: 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.

Source: ACM/IEEE International Conference on Human-Robot Interaction (HRI)

DOI: 10.1145/2909824.3020222

Authors: Thorsten Schodde, Kirsten Bergmann, Stefan Kopp

Institution: Bielefeld University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: green

Funder: Horizon 2020