The core of smart classroom is to promote students' deep learning. In order to ensure that smart classroom can effectively achieve "quality and efficiency improvement" and cultivate students' problem-solving and innovation ability, it is very important to study deep learning in smart classroom. This study discusses the elements of students' deep learning activities, and points out that the transformation of teachers' teaching concepts, the embodiment of digital literacy, the intelligent optimization and iterative upgrading of information technology are the necessary conditions for implementing deep learning in smart classroom. Based on cognitivism learning theory and deep learning strategy, with knowledge acquisition, transformation, application and evaluation as the main line, it has effectively realized the path exploration of deep learning in smart classroom through four stages: sufficient thinking processing of perceptual experience, transition from "active learning" to "deep learning", improvement of knowledge transfer and problem solving ability, and transformation from knowledge to literacy. Along this path, guided by "problem-solving", a content framework of deep learning activities is proposed, which includes pre-learning, research and differentiation as the main links, and aims to cultivate students' independent thinking and dialectical thinking ability as the process, so as to achieve comprehensive learning goals and develop students' higher-order thinking ability.

Zheng Yuxiao, Wang Chengbo. An Activity Architecture of Deep Learning for Students in Smart Classroom. Frontiers in Educational Research (2023) Vol. 6, Issue 19: 11-18. https://doi.org/10.25236/FER.2023.061903.

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