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In Search of Negative Moments: Multi-Modal Analysis of Teacher Negativity in Classroom Observation Videos

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  • Additional Information
    • Availability:
      International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
    • Peer Reviewed:
      Y
    • Source:
      8
    • Sponsoring Agency:
      National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL)
    • Contract Number:
      2019805
      2046505
      1822768
    • Education Level:
      Elementary Education
      Junior High Schools
      Middle Schools
      Secondary Education
    • Subject Terms:
      Teacher Behavior; Negative Attitudes; Nonverbal Communication; Teacher Student Relationship; Interaction; Video Technology; Emotional Response; Speech Communication; Classroom Environment; Classroom Observation Techniques; Artificial Intelligence; Elementary Schools; Middle Schools
    • Abstract:
      We explore multi-modal machine learning-based approaches (facial expression recognition, auditory emotion recognition, and text sentiment analysis) to identify "negative moments" of teacher-student interaction during classroom teaching. Our analyses on a large (957 videos, each 20min) dataset of classroom observations suggest that: (1) Negative moments occur sparsely and are laborious to find by manually watching videos from start to finish; (2) Contemporary machine perception tools for emotion, speech, and text sentiment analysis show only limited ability to capture the diverse manifestations of classroom negativity in a fully automatic way; (3) Semi-automatic procedures that combine machine perception with human annotation may hold more promise for finding authentic moments of classroom negativity; Finally, (4) even short 10 sec negative moments contain rich structure in terms of the actions and behaviors that they comprise. [For the complete proceedings, see ED630829.]
    • Abstract:
      As Provided
    • Publication Date:
      2023
    • Accession Number:
      ED630855