"Beautiful work, you're rock stars!": Teacher Analytics to Uncover Discourse that Supports or Undermines Student Motivation, Identity, and Belonging in Classrooms

被引:18
作者
Hunkins, Nicholas C. [1 ]
Kelly, Sean [2 ]
D'Mello, Sidney K. [1 ]
机构
[1] Univ Colorado, Boulder, CO 80309 USA
[2] Univ Pittsburgh, Pittsburgh, PA 15260 USA
来源
LAK22 CONFERENCE PROCEEDINGS: THE TWELFTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE | 2022年
关键词
Teacher Analytics; Discourse Analysis; Natural Language Processing; Automated Feedback; NONVERBAL-COMMUNICATION; PERCEPTIONS; ADOLESCENTS; SCHOOL; ENGAGEMENT; RACE;
D O I
10.1145/3506860.3506896
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:230 / 238
页数:9
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