Analyzing Self-Efficacy and Summary Feedback in Automated Social Skills Training

<italic>Goal:</italic> Although automated social skills training has been proposed to enhance human social skills, the following two aspects have not been adequately explored: what types of feedback are effective from virtual agents and the extent to which such systems enhance users'...

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Main Authors: Hiroki Tanaka, Hidemi Iwasaka, Yasuhiro Matsuda, Kosuke Okazaki, Satoshi Nakamura
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Open Journal of Engineering in Medicine and Biology
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9416779/
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author Hiroki Tanaka
Hidemi Iwasaka
Yasuhiro Matsuda
Kosuke Okazaki
Satoshi Nakamura
author_facet Hiroki Tanaka
Hidemi Iwasaka
Yasuhiro Matsuda
Kosuke Okazaki
Satoshi Nakamura
author_sort Hiroki Tanaka
collection DOAJ
description <italic>Goal:</italic> Although automated social skills training has been proposed to enhance human social skills, the following two aspects have not been adequately explored: what types of feedback are effective from virtual agents and the extent to which such systems enhance users' social self-efficacy. <italic>Methods:</italic> We developed an automated social skills trainer&#x002B; that follows human-based social skills training processes and implemented two types of feedback: 1) a summary of the displayed feedback and 2) feedback based on the results of their previous training. Using our developed system, we measured social self-efficacy, feedback evaluations, and the third-party ratings of participants between pre- and post-training as well as their social responsiveness scales. <italic>Results:</italic> Self-efficacy is significantly correlated to the social responsiveness scale (r &#x003D; &#x2212;0.72) and can be improved with our system (mean improvement of 0.68, p &lt; 0.05). The participants highly rated the feedback that was compared to their past training (14 out of 16, p &lt; 0.05) more than the cases without it and the displayed summary feedback (11 out of 16, p &#x003D; 0.21) more than the verbal comments. <italic>Conclusions:</italic> Our system effectively summarized user feedback in terms of user self-efficacy and third-party ratings.
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spelling doaj-art-62508d8add3e4df7b5b36cb861655ea82025-08-20T03:32:47ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762021-01-012657010.1109/OJEMB.2021.30755679416779Analyzing Self-Efficacy and Summary Feedback in Automated Social Skills TrainingHiroki Tanaka0https://orcid.org/0000-0002-0548-6252Hidemi Iwasaka1Yasuhiro Matsuda2Kosuke Okazaki3Satoshi Nakamura4https://orcid.org/0000-0001-6956-3803Nara Institute of Science and Technology, Ikoma-shi, Nara, JapanNara Medical University, Kashihara-shi, Nara, JapanNara Medical University, Kashihara-shi, Nara, JapanNara Medical University, Kashihara-shi, Nara, JapanNara Institute of Science and Technology, Ikoma-shi, Nara, Japan<italic>Goal:</italic> Although automated social skills training has been proposed to enhance human social skills, the following two aspects have not been adequately explored: what types of feedback are effective from virtual agents and the extent to which such systems enhance users' social self-efficacy. <italic>Methods:</italic> We developed an automated social skills trainer&#x002B; that follows human-based social skills training processes and implemented two types of feedback: 1) a summary of the displayed feedback and 2) feedback based on the results of their previous training. Using our developed system, we measured social self-efficacy, feedback evaluations, and the third-party ratings of participants between pre- and post-training as well as their social responsiveness scales. <italic>Results:</italic> Self-efficacy is significantly correlated to the social responsiveness scale (r &#x003D; &#x2212;0.72) and can be improved with our system (mean improvement of 0.68, p &lt; 0.05). The participants highly rated the feedback that was compared to their past training (14 out of 16, p &lt; 0.05) more than the cases without it and the displayed summary feedback (11 out of 16, p &#x003D; 0.21) more than the verbal comments. <italic>Conclusions:</italic> Our system effectively summarized user feedback in terms of user self-efficacy and third-party ratings.https://ieeexplore.ieee.org/document/9416779/Embodied conversational agentsself-efficacysocial skills trainingsummary feedback
spellingShingle Hiroki Tanaka
Hidemi Iwasaka
Yasuhiro Matsuda
Kosuke Okazaki
Satoshi Nakamura
Analyzing Self-Efficacy and Summary Feedback in Automated Social Skills Training
IEEE Open Journal of Engineering in Medicine and Biology
Embodied conversational agents
self-efficacy
social skills training
summary feedback
title Analyzing Self-Efficacy and Summary Feedback in Automated Social Skills Training
title_full Analyzing Self-Efficacy and Summary Feedback in Automated Social Skills Training
title_fullStr Analyzing Self-Efficacy and Summary Feedback in Automated Social Skills Training
title_full_unstemmed Analyzing Self-Efficacy and Summary Feedback in Automated Social Skills Training
title_short Analyzing Self-Efficacy and Summary Feedback in Automated Social Skills Training
title_sort analyzing self efficacy and summary feedback in automated social skills training
topic Embodied conversational agents
self-efficacy
social skills training
summary feedback
url https://ieeexplore.ieee.org/document/9416779/
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