Göllner Richard, Lazarides Rebecca, Stark Philipp
Department of Education, University of Potsdam, Potsdam, Germany.
Cluster of Excellence Science of Intelligence, Technical University Berlin, Berlin, Germany.
Br J Educ Psychol. 2025 Sep;95 Suppl 1(Suppl 1):S300-S315. doi: 10.1111/bjep.70001. Epub 2025 Jun 10.
Existing conceptions of teaching quality assume that classroom interactions serve as the foundation for effective teaching. The resulting data necessitates analytical approaches capable of extracting the semantics of these interactions.
This study investigates whether and to what extent lesson semantics provide insights into teaching quality (i.e., cognitive engagement, encouragement and warmth, multiple approaches, and the nature of discourse). To achieve this, GPT-4 was applied as a tool for analysing lesson transcripts.
The study is based on data from the TALIS Video study, which included N = 50 teachers delivering two consecutive mathematics lessons in 9th grade. Teaching quality was annotated by trained observers across multiple dimensions.
The analysis involved embedding segmented lesson transcripts to examine their semantic characteristics and associations with human annotations of teaching quality. Additionally, we applied content-informed prompting to evaluate the interpretability of semantic characteristics for the considered dimensions.
GPT-4 identified five distinct semantic representations of transcripts, varying at both the teacher and lesson levels. These representations were related to teaching quality, accounting for up to 20% of variance in teaching quality annotations. Content-informed prompting aligned lesson segments more closely with semantic representations, supporting their interpretability.
The findings suggest that lesson semantics serve as indicators of teaching quality, offering a promising approach to understanding effective classroom learning.
现有的教学质量观念认为课堂互动是有效教学的基础。由此产生的数据需要能够提取这些互动语义的分析方法。
本研究调查课程语义是否以及在多大程度上能为教学质量（即认知参与、鼓励与热情、多种方法以及话语性质）提供见解。为实现这一目标，将GPT-4用作分析课程记录的工具。
该研究基于TALIS视频研究的数据，其中包括N = 50名教师在九年级连续讲授两节数学课。教学质量由经过培训的观察员在多个维度上进行标注。
分析包括对分段的课程记录进行嵌入，以检查其语义特征以及与教学质量的人工标注之间的关联。此外，我们应用内容引导提示来评估语义特征对于所考虑维度的可解释性。
GPT-4识别出了课程记录的五种不同语义表示，在教师和课程层面均有所不同。这些表示与教学质量相关，占教学质量标注方差的20%。内容引导提示使课程片段与语义表示更紧密地对齐，支持了它们的可解释性。
研究结果表明课程语义可作为教学质量的指标，为理解有效的课堂学习提供了一种有前景的方法。
