著者

林 勇吾

出版者

ヒューマンインタフェース学会

雑誌

ヒューマンインタフェース学会論文誌 (ISSN:13447262)

巻号頁・発行日

vol.22, no.3, pp.263-270, 2020-08-25 (Released:2020-08-25)

参考文献数

22

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The paper explores what factors underlying the estimation of learner self-confidence during explanations with a pedagogical conversational agent in an explanation task. This study focused on how factors such as the learner's task activities and personal characteristics can be used as useful predictors. To explore this point, this study used an web-based explanation task called WESPA (Web-based Explanation Support by Pedagogical Agent), which was run by a pedagogical conversational agent (PCA) for students in a classroom taking a lecture from psychology. 318 participants were asked to make text-based explanations to the agent in a question-and-answer (Q&A) style, and clarified a particular concept that was taught in a previous lecture in the class. Results show that an increase in the amount of actual task work for explanations and personal characteristics evaluated by AQ scores (such as social skills, attention switching, imagination) helped to predict higher self-confidence. The results show how factors of learner's task activities and personal characteristics especially about interpersonal interaction skills are useful for capturing learner's self-confidence in an online explanation task. It is also discussed how these factors could be used as predictors in future studies to automatically detect learner's confidence.