Teachers' Vocal Expressions and Student Engagement in Asynchronous Video Learning

📅 2026-05-17
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🤖 AI Summary
This study addresses the issue of insufficient emotional engagement among students in asynchronous video-based learning by systematically distinguishing and comparing the differential effects of instructors’ verbal versus nonverbal vocal emotions. Drawing on data from 210 MOOC videos and feedback from 738 learners, the research integrates computational acoustic analysis, textual sentiment analysis, and classification of six basic nonverbal vocal emotion categories. Findings reveal that high-arousal positive nonverbal emotions—such as happiness and surprise—significantly enhance learners’ emotional engagement, whereas high-arousal negative emotions like anger markedly diminish it. In contrast, verbal emotional content shows no significant impact on engagement. These results underscore the critical role of instructors’ nonverbal vocal expressions in fostering emotional engagement in online learning environments.
📝 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, such as happiness and surprise, enhanced engagement, while negative high-arousal emotions, such as anger, reduced it. These findings offer practical insights for instructional video creators, teachers, and influencers to foster emotional engagement in asynchronous video learning.
Problem

Research questions and friction points this paper is trying to address.

asynchronous video learning
student engagement
vocal expressions
emotional engagement
MOOCs
Innovation

Methods, ideas, or system contributions that make the work stand out.

vocal emotion
asynchronous video learning
computational acoustic analysis
affective engagement
nonverbal vocal expressions
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H
Hung-Yue Suen
Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei City, Taiwan
Y
Yu-Sheng Su
Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan; Advanced Institute of Manufacturing with High-tech Innovations, National Chung Cheng University, Taiwan; Department of Computer Science and Engineering, National Taiwan Ocean University, Taiwan