In Search of Negative Moments: Multi-Modal Analysis of Teacher Negativity in Classroom Observation Videos [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).

Autor/inn/en	Dai, Zilin; McReynolds, Andrew; Whitehill, Jacob
Titel	In Search of Negative Moments: Multi-Modal Analysis of Teacher Negativity in Classroom Observation Videos [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (16th, Bengaluru, India, Jul 11-14, 2023).
Quelle	(2023), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	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 + Suchen Sie Ihr Suchwort? Teacher behaviour; Lehrerverhalten; Negative Fixierung; Non-verbal communication; Nonverbale Kommunikation; Teacher student relationships; Lehrer-Schüler-Beziehung; Interaktion; Emotionales Verhalten; Klassenklima; Unterrichtsklima; Künstliche Intelligenz; Elementary school; Grundschule; Volksschule; Middle school; Mittelschule; Mittelstufenschule
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.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01