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<p>Questions remain about what professional vision means within mathematics education and the best ways to study it. Epistemic network analysis is a theoretically aligned quantitative ethnography method for discourse modeling that&#xa0;operationalizes cognitive connections, statistically validates patterns, and allows for nuanced discourse analysis, thus offering a socially organized framework for examining PMTs’ growth into teaching. This study explores whether the novel application of epistemic network analysis is a productive method to analyze naturalistic data generated from preservice mathematics teachers’ (PMTs’) weekly field observation reflections for their development of professional vision over time, and if so, what insights are afforded by this strategy. Findings revealed qualitative and quantitative empirical evidence that the PMTs made substantially different connection patterns in their reflections between key instructional aspects over time. The analysis also produced network graph visualizations that highlighted salient changes in PMTs’ discourse data, both at the group and individual levels. Using epistemic netwrok analysis in this study was generative, producing empirical insights about the learning of the individual PMT alongside the learning of the group, validation of qualitative claims, and theoretical saturation to make qualitative studies more reliable and realistic modeling of discourse connections.</p>

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Analyzing preservice mathematics teachers’ professional vision using epistemic network analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月14日

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  • Jennifer Kornell,
  • Akhenaton Wilbourn,
  • Khushbu Singh,
  • Nicole Bannister,
  • Golnaz Arastoopour Irgens

摘要

Questions remain about what professional vision means within mathematics education and the best ways to study it. Epistemic network analysis is a theoretically aligned quantitative ethnography method for discourse modeling that operationalizes cognitive connections, statistically validates patterns, and allows for nuanced discourse analysis, thus offering a socially organized framework for examining PMTs’ growth into teaching. This study explores whether the novel application of epistemic network analysis is a productive method to analyze naturalistic data generated from preservice mathematics teachers’ (PMTs’) weekly field observation reflections for their development of professional vision over time, and if so, what insights are afforded by this strategy. Findings revealed qualitative and quantitative empirical evidence that the PMTs made substantially different connection patterns in their reflections between key instructional aspects over time. The analysis also produced network graph visualizations that highlighted salient changes in PMTs’ discourse data, both at the group and individual levels. Using epistemic netwrok analysis in this study was generative, producing empirical insights about the learning of the individual PMT alongside the learning of the group, validation of qualitative claims, and theoretical saturation to make qualitative studies more reliable and realistic modeling of discourse connections.