Go to DBLP homepageA Study on Analyzing Classroom Teaching Questions Supported by Generative Artificial Intelligence
Abstract: Classroom questioning can promote students’ learning to happen and is an important means of classroom teaching. In order to analyze the target direction of questions raised by teachers in current classroom teaching, clarify the difference between questions on different topics, and discover the problems of classroom teaching, the article takes the mathematics discipline competence as the direction of classroom questions, constructs a question coding framework based on the analysis of the connotation and behavioral performance of mathematics discipline competence, and analyzes the 20 examples of elementary school mathematics classroom teaching in collaboration with generative artificial intelligence. The results of the study found that the current classroom teaching questions are focused on learning comprehension and application practice, and less on creative transfer. The teaching questions of different teaching topics vary, with the teaching questions of number and algebra, graphing and geometry focusing on generalization and comprehension, and illustration and argumentation, and the questions of probability and statistics focusing on speculation and explanation. Finally, the article suggests that the design of questions should take into account the teaching objectives, teaching content, higher-order cognitive questions, and scientific expression of teaching questions based on the idea of cue engineering.
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