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Teachers' Vocal Expressions and Student Engagement in Asynchronous Video Learning.

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Title: Teachers' Vocal Expressions and Student Engagement in Asynchronous Video Learning.
Authors: Suen, Hung-Yue1 (AUTHOR), Su, Yu-Sheng2,3,4 (AUTHOR) ccucssu@gmail.com
Source: International Journal of Human-Computer Interaction. Nov2025, Vol. 41 Issue 21, p13483-13494. 12p.
Subjects: Student engagement, Asynchronous learning, Communication styles, Distance education, Massive open online courses, Teachers, Facial expression, Sentiment analysis
Abstract: Asynchronous video learning, including massive open online courses (MOOCs), offers flexibility but often lacks students' affective engagement. This study examines how teachers' verbal and nonverbal vocal emotive expressions influence students' self-reported affective engagement. Using computational acoustic and sentiment analysis, valence and arousal scores were extracted from teachers' verbal vocal expressions, and nonverbal vocal emotions were classified into six categories: anger, fear, happiness, neutral, sadness, and surprise. Data from 210 video lectures across four MOOC platforms and feedback from 738 students collected after class were analyzed. Results revealed that teachers' verbal emotive expressions, even with positive valence and high arousal, did not significantly impact engagement. Conversely, vocal expressions with positive valence and high arousal (e.g., happiness, surprise) enhanced engagement, while negative high-arousal emotions (e.g., anger) reduced it. These findings offer practical insights for instructional video creators, teachers, and influencers to foster emotional engagement in asynchronous video learning. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Teachers' Vocal Expressions and Student Engagement in Asynchronous Video Learning.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Nov2025, Vol. 41 Issue 21, p13483-13494. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Student+engagement%22">Student engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Asynchronous+learning%22">Asynchronous learning</searchLink><br /><searchLink fieldCode="DE" term="%22Communication+styles%22">Communication styles</searchLink><br /><searchLink fieldCode="DE" term="%22Distance+education%22">Distance education</searchLink><br /><searchLink fieldCode="DE" term="%22Massive+open+online+courses%22">Massive open online courses</searchLink><br /><searchLink fieldCode="DE" term="%22Teachers%22">Teachers</searchLink><br /><searchLink fieldCode="DE" term="%22Facial+expression%22">Facial expression</searchLink><br /><searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink>
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  Data: Asynchronous video learning, including massive open online courses (MOOCs), offers flexibility but often lacks students' affective engagement. This study examines how teachers' verbal and nonverbal vocal emotive expressions influence students' self-reported affective engagement. Using computational acoustic and sentiment analysis, valence and arousal scores were extracted from teachers' verbal vocal expressions, and nonverbal vocal emotions were classified into six categories: anger, fear, happiness, neutral, sadness, and surprise. Data from 210 video lectures across four MOOC platforms and feedback from 738 students collected after class were analyzed. Results revealed that teachers' verbal emotive expressions, even with positive valence and high arousal, did not significantly impact engagement. Conversely, vocal expressions with positive valence and high arousal (e.g., happiness, surprise) enhanced engagement, while negative high-arousal emotions (e.g., anger) reduced it. These findings offer practical insights for instructional video creators, teachers, and influencers to foster emotional engagement in asynchronous video learning. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/10447318.2025.2474469
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      – Code: eng
        Text: English
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        PageCount: 12
        StartPage: 13483
    Subjects:
      – SubjectFull: Student engagement
        Type: general
      – SubjectFull: Asynchronous learning
        Type: general
      – SubjectFull: Communication styles
        Type: general
      – SubjectFull: Distance education
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      – SubjectFull: Massive open online courses
        Type: general
      – SubjectFull: Teachers
        Type: general
      – SubjectFull: Facial expression
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      – SubjectFull: Sentiment analysis
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              M: 11
              Text: Nov2025
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