This research draws on Legitimation Code Theory (LCT) to understand the epistemic dimension of the higher education classroom discourse of a professor who is well evaluated by his students. In expository science classroom discourse, concepts are loaded with different meanings, a process that is called “condensation of meanings” in LCT, and are expressed using the concept of epistemic semantic density (ESD). The ESD can vary in classroom discourse, generating what is called a semantic wave in the LCT context. In this work, we initially compare two different translation devices, which are ways of systematically exploring the characteristics of the knowledge expressed in the classroom and how they vary, generating semantic waves. We were able to observe the strengths and weaknesses of both devices. They proved to be powerful tools in revealing how the complexity of science knowledge varies in a lesson. However, neither one demonstrates how meanings are connected in the lesson or how the semantic density of a particular concept changes over time. In this sense, we explore how everyday words at times become loaded with technical meaning when other concepts are connected to them. To achieve that, we use third device based on network science, an approach that analyzes and represents the systems of interactions of a wide range of physical and social phenomena. This allows us to evaluate how a term commonly used in everyday language, such as “lamp,” can play a central role in the construction of meanings related to the episteme of chemistry. In addition, while LCT assumes that semantic waves necessarily optimize learning, the results obtained herein revealed that constellation of meanings does not directly relate to semantic waves.

中文翻译：

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使用不同的翻译设备分析大学化学课堂中的知识构建

本研究利用合法性代码理论 (LCT) 来理解一位教授的高等教育课堂话语的认知维度，该教授受到学生的好评。在说明性科学课堂话语中，概念加载了不同的含义，这个过程在 LCT 中称为“含义的浓缩”，并使用认知语义密度 (ESD) 的概念来表达。ESD 可以在课堂话语中变化，在 LCT 上下文中产生所谓的语义波。在这项工作中，我们初步比较了两种不同的翻译设备，它们是系统地探索课堂中表达的知识的特征及其变化​​方式，从而产生语义波的方式。我们能够观察到这两种设备的优缺点。事实证明，它们是揭示科学知识复杂性如何在课程中变化的有力工具。然而，他们都没有展示课程中的含义是如何连接的，或者特定概念的语义密度如何随时间变化。从这个意义上说，我们探索了当其他概念与日常词汇相关联时，日常词汇如何变得充满技术意义。为了实现这一目标，我们使用基于网络科学的第三种设备，这是一种分析和表示广泛物理和社会现象相互作用系统的方法。这使我们能够评估日常语言中常用的术语（例如“灯”）如何在与化学知识相关的含义构建中发挥核心作用。此外，虽然 LCT 假设语义波必然优化学习，