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Authors: Masato Fukuda ; Hung-Hsuan Huang and Toyoaki Nishida

Affiliation: Center for Advanced Intelligence Project, RIKEN, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto and Japan

ISBN: 978-989-758-350-6

Keyword(s): Intention Detection, Multimodal Interaction, Educational Application, User Assessment.

Related Ontology Subjects/Areas/Topics: Agents ; AI and Creativity ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bioinformatics ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Conversational Agents ; Data Manipulation ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Information Systems Analysis and Specification ; Knowledge Discovery and Information Retrieval ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Methodologies and Technologies ; Multi-Agent Systems ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Operational Research ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Simulation ; Soft Computing ; Software Engineering ; Symbolic Systems

Abstract: The training program for high school teachers in Japan has less opportunity to practice teaching skills. As a new practice platform, we are running a project to develop a simulation platform of school environment with computer graphics animated virtual students for students’ teachers. In order to interact with virtual students and teachers, it is necessary to estimate the intention of the teacher’s behavior and utterance. However, it is difficult to detection the teacher’s intention at the classroom only by verbal information, such as whether to ask for a response or seek a response. In this paper, we propose an automatic detection model of teacher’s intention using multimodal features including linguistic, prosodic, and gestural features. For the linguistic features, we consider the models with and without lecture contents specific information. As a result, it became clear that estimating the intention of the teacher is better when using prosodic / non-verbal information together tha n using only verbal information. Also, the models with contents specific information perform better. (More)

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Paper citation in several formats:
Fukuda, M.; Huang, H. and Nishida, T. (2019). Detection of Student Teacher's Intention using Multimodal Features in a Virtual Classroom.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-350-6, pages 170-177. DOI: 10.5220/0007379901700177

@conference{icaart19,
author={Masato Fukuda. and Hung{-}Hsuan Huang. and Toyoaki Nishida.},
title={Detection of Student Teacher's Intention using Multimodal Features in a Virtual Classroom},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2019},
pages={170-177},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007379901700177},
isbn={978-989-758-350-6},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Detection of Student Teacher's Intention using Multimodal Features in a Virtual Classroom
SN - 978-989-758-350-6
AU - Fukuda, M.
AU - Huang, H.
AU - Nishida, T.
PY - 2019
SP - 170
EP - 177
DO - 10.5220/0007379901700177

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