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We present the Teacher Talk Tool, which automatically analyzes classroom audio and provides formative feedback on key aspects of teachers’ classroom discourse (e.g., use of open-ended questions). The tool was designed to promote teacher learning by focusing attention and sense-making on their discourse. We conducted a feedback- response study where five English & Language Art teachers used the Teacher Talk Tool in eight classroom sessions. Teachers completed repeated-measure surveys and semi-structured interviews providing quantitative and qualitative evidence of feedback response. Results indicated that the majority of automated feedback was perceived to be accurate and prompted a high degree of reflection, focusing teachers’ attention on the measured talk constructs. This feedback also led teachers to engage in a process of sense-making, linking the measured talk features to classroom processes and contexts. However, evidence of feedback uptake was more limited. Overall, results contribute to the nascent literature on the efficacy of automated feedback on instructional practice.
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Automated feedback on discourse moves: Teachers’ perceived utility of a big data tool.
Using new technology to provide automated feedback on classroom discourse offers a unique opportunity for educators to engage in self-reflection on their teaching, in particular to ensure that the instructional environment is equitable and productive for all students. More information is needed about how teachers experience automated data tools, including what they perceive as relevant and helpful for their everyday teaching. This mixed-methods study explored the perceptions and engagement of 21 math teachers over two years with a big data tool that analyzes classroom recordings and generates information about their discourse practices in near real-time. Findings revealed that teachers perceived the tool as having utility, yet the specific feedback that teachers perceived as most useful changed over time. In addition, teachers who used the tool throughout both years increased their use of talk moves over time, suggesting that they were making changes due to their review of the personalized feedback. These findings speak to promising directions for the development of AI-based, big data tools that help shape teacher learning and instruction, particularly tools that have strong perceived utility.
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- Award ID(s):
- 1837986
- PAR ID:
- 10387282
- Date Published:
- Journal Name:
- Annual meeting of the American Educational Research Association
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.3102/1887987
