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• W4214949416 abstract "From carefully crafted messages to flippant remarks, warm expressions to unfriendly grunts, teachers’ behaviors set the tone, expectations, and attitudes of the classroom. Thus, it is prudent to identify the ways in which teachers foster motivation, positive identity, and a strong sense of belonging through inclusive messaging and other interactions. We leveraged a new coding of teacher supportive discourse in 156 video clips from 73 6th to 8th grade math teachers from the archival Measures of Effective Teaching (MET) project. We trained Random Forest classifiers using verbal (words used) and paraverbal (acoustic-prosodic cues, e.g., speech rate) features to detect seven features of teacher discourse (e.g., public admonishment, autonomy supportive messages) from transcripts and audio, respectively. While both modalities performed over chance guessing, the specific language content was more predictive than paraverbal cues (mean correlation = .546 vs. .276); combining the two yielded no improvement. We examined the most predictive cues in order to gain a deeper understanding of the underlying messages in teacher talk. We discuss implications of our work for teacher analytics tools that aim to provide educators and researchers with insight into supportive discourse." @default.
• W4214949416 created "2022-03-05" @default.
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• W4214949416 date "2022-03-21" @default.
• W4214949416 modified "2023-10-17" @default.
• W4214949416 title "“Beautiful work, you're rock stars!”: Teacher Analytics to Uncover Discourse that Supports or Undermines Student Motivation, Identity, and Belonging in Classrooms" @default.
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• W4214949416 doi "https://doi.org/10.1145/3506860.3506896" @default.
• W4214949416 hasPublicationYear "2022" @default.
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