Due to benefits for teaching and learning, an increasing number of studies have focused on classroom dialogue and how to make it productive. Coding, in which the transcribed conversation is allocated to a set of features, is commonly employed to deal with the textual data arising from this dialogue. This is generally done manually and cannot provide timely feedback to the participants. To address this issue, we explored the possibility of automatically classifying the semantic content of classroom dialogue. Seven categories (prior-known knowledge, analysis, coordination, speculation, uptake, agreement and querying) were distinguished automatically using an artificial neural network-based model. The model achieved acceptable performance and was comparable to human coding. Information about quality of dialogue can be identified in a timely manner. With this knowledge, classroom dialogue can be managed more skilfully, and a more productive form of dialogue is likely to be achieved by teachers and students.

由于教与学的好处，越来越多的研究集中在课堂对话以及如何使对话富有成效。通常使用编码方式，其中将转录的对话分配给一组功能，以处理由该对话产生的文本数据。这通常是手动完成的，无法及时向参与者提供反馈。为了解决这个问题，我们探索了自动分类课堂对话语义内容的可能性。使用基于人工神经网络的模型自动区分七个类别（先验知识，分析，协调，推测，摄取，同意和查询）。该模型实现了可接受的性能，与人类编码相当。有关对话质量的信息可以及时识别。