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Analyzing the quality of classroom talk is central to educational research and improvement efforts. In particular, the presence of authentic teacher questions, where answers are not predetermined by the teacher, helps constitute and serves as a marker of productive classroom discourse. Further, authentic questions can be cultivated to improve teaching effectiveness and consequently student achievement. Unfortunately, current methods to measure question authenticity do not scale because they rely on human observations or coding of teacher discourse. To address this challenge, we set out to use automatic speech recognition, natural language processing, and machine learning to train computers to detect authentic questions in real-world classrooms automatically. Our methods were iteratively refined using classroom audio and human coded observational data from two sources: (a) a large archival database of text transcripts of 451 observations from 112 classrooms; and (b) a newly collected sample of 132 high-quality audio recordings from 27 classrooms, obtained under technical constraints that anticipate large-scale automated data collection and analysis. Correlations between human coded and computer-coded authenticity at the classroom level were sufficiently high (r = .602 for archival transcripts and .687 for audio recordings) to provide a valuable complement to human coding in research efforts.
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Fine-Grained, Nomination Coding in the Support Domain: Promising Teacher Discourse Measures
In this study, we report results from a novel coding of the Measures of Effective Teaching (MET) Study data that offers evidence on a set of teacher discourse measures in the domain of teacher support including: public praise vs. admonishment, autonomy support vs. controlling language, strategy suggestion vs. lack thereof, and discourse supporting (vs.undermining) learning mindsets. Novel coding of these constructs is paired with extant measures of instruction and achievement in the MET data. Several of the newly coded discourse measures have promising features, including high lesson- and teacher-level variability, and convergent and discriminant validity with existing protocols. We also report possible associations with change in achievement over two years.
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- Award ID(s):
- 2326169
- PAR ID:
- 10539290
- Publisher / Repository:
- https://doi.org/10.1080/00220973.2024.2312521
- Date Published:
- Journal Name:
- The Journal of experiential education
- ISSN:
- 1053-8259
- Subject(s) / Keyword(s):
- Classroom observation discourse support engagement motivation
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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