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<p>The research focus on teacher questions is justified by previous research emphasizing the essential role of questions in facilitating meaningful learning in science. Analysis of teacher questions has traditionally been based mainly on manual coding, which is extremely labour intensive. In this study we explore how both machine learning and large language models can be used for this purpose. Whereas machine learning approaches involve supervised training with extensive data, pre-trained large language models operate through prompt engineering. ​​The​ automatic speech recognition text outputs of 23 physics lessons on the same topic from 23 science teachers were analysed with variety of techniques. The results revealed that the large language model approaches improved with few-shot approaches compared to zero-shot ones. Furthermore, few shot approaches outperformed the supervised machine learning approaches, yet human- and hand-crafted approaches continue to demonstrate their relevance. ​​Implications for​ science teaching and learning are discussed.</p>

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Discovering technology-aided possibilities for automatic analysis of science teacher questions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月29日

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  • Sami Lehesvuori,
  • Felipe Urrutia,
  • Ville Heilala,
  • Roberto Araya,
  • Raija Hämäläinen

摘要

The research focus on teacher questions is justified by previous research emphasizing the essential role of questions in facilitating meaningful learning in science. Analysis of teacher questions has traditionally been based mainly on manual coding, which is extremely labour intensive. In this study we explore how both machine learning and large language models can be used for this purpose. Whereas machine learning approaches involve supervised training with extensive data, pre-trained large language models operate through prompt engineering. ​​The​ automatic speech recognition text outputs of 23 physics lessons on the same topic from 23 science teachers were analysed with variety of techniques. The results revealed that the large language model approaches improved with few-shot approaches compared to zero-shot ones. Furthermore, few shot approaches outperformed the supervised machine learning approaches, yet human- and hand-crafted approaches continue to demonstrate their relevance. ​​Implications for​ science teaching and learning are discussed.