Affect and Inference in Bayesian Knowledge Tracing with a Robot Tutor
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Breazeal_Affect and Inference.pdf
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161.75 KB
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abaebdff7140900338316451f400cd94
Author(s)
Spaulding, Samuel Lee
Breazeal, Cynthia Lynn
Date Issued
March 2015
Journal
Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts - HRI'15 Extended Abstracts
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Spaulding, Samuel, and Cynthia Breazeal. “Affect and Inference in Bayesian Knowledge Tracing with a Robot Tutor.” Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts - HRI'15 Extended Abstracts. ACM Press, 2015. 219–220.
Version
Author's final manuscript
Abstract
In this paper, we present work to construct a robotic tutoring system that can assess student knowledge in real time during an educational interaction. Like a good human teacher, the robot draws on multimodal data sources to infer whether students have mastered language skills. Specifically, the model extends the standard Bayesian Knowledge Tracing algorithm to incorporate an estimate of the student's affective state (whether he/she is confused, bored, engaged, smiling, etc.) in order to predict future educational performance. We propose research to answer two questions: First, does augmenting the model with affective information improve the computational quality of inference? Second, do humans display more prominent affective signals in an interaction with a robot, compared to a screen-based agent? By answering these questions, this work has the potential to provide both algorithmic and human-centered motivations for further development of robotic systems that tightly integrate affect understanding and complex models of inference with interactive, educational robots.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/2701973.2702720
