| With the deepening of basic education curriculum reform,deep learning,which focuses on cultivating students’ higher-order thinking skills,has provided a new way of thinking for curriculum reform.As a common way of teacher-student interaction,classroom questioning plays an important role in promoting students’ knowledge construction and thinking development,and has a practical basis for implementing deep learning.Thus,the study takes classroom questioning as an entry point to explore how to optimize elementary school mathematics teachers’ classroom questioning in order to promote students’ deep learning.It is important for improving teachers’ classroom questioning and guiding students’ deep learning.The study first adopts the literature research method,draws on existing research results,sorts out the principles of classroom questioning in elementary school mathematics under the perspective of deep learning,combines IRF classroom discourse analysis theory,and constructs an observation scale of classroom questioning in elementary school mathematics under the perspective of deep learning.Then the Delphi method was used to revise the observation scale according to the experts’ opinions and finalize the framework.It includes three primary observation dimensions: teacher’s questioning,student’s response,and teacher’s feedback;six secondary observation dimensions: type of questioning,level of thinking of the question,waiting time,target of the question,level of response,and way of reasoning;and 25 observation points.Finally,using the video analysis method,lessons of the "Area of Polygons" unit were selected as the research samples,and the three dimensions of classroom questioning were recorded,coded,and data collected using Nvivo12 Pro qualitative analysis software,and the conclusions were drawn by combining qualitative and quantitative analysis.Based on the above analysis,the characteristics of classroom questioning in elementary school mathematics quality lessons under the deep learning perspective were obtained:(1)In terms of the types of questions,teachers paid more attention to the design and use of high cognitive questions such as analytical questions,and high cognitive questions such as evaluative and creative questions were usually paved with low cognitive questions such as literacy and comprehension questions,which were in line with the high cognitive of deep learning.(2)In terms of the level of thinking of the questions,teachers’ use of convergent questions was significantly higher than their use of divergent questions.(3)In terms of waiting time,teachers were able to recognize the importance of waiting time and were able to make full use of it to observe students’ behavioral performance.(4)In terms of the target audience of the questions,the proportion of collective responses was slightly larger than individual responses,and students’ participation was high,which was in line with the high emotion of deep learning.(5)In terms of response level,students mostly responded with comprehension,and the total number of high cognitive responses was more than half,which echoed the teacher’s focus on high-level questioning.(6)In terms of response style,teachers mostly used positive feedback such as "follow-up questions" and "encouragement and appreciation",showing the characteristics of timely feedback and timely reinforcement,in line with the high behavior of deep learning.(7)In terms of correlation,there is a strong correlation between students’ response style and teachers’ questioning type;there is a correlation between teachers’ waiting time and students’ response level.Based on the results of the study,the following three suggestions were made to optimize the classroom questioning of elementary school mathematics teachers:(1)design deep questions to stimulate students’ high cognition.(2)Balancing collective and individual questions to promote students’ high affect.(3)Give timely feedback to develop students’ high behavior. |
