21世纪的学习逐步转向深度学习,高质量教学交互对学生深度学习提升有重要作用。智慧教室环境为教学交互开展和深度学习达成提供了支撑。在基于云端的智慧教室中实施多种技术支持的教学交互策略,通过多模态数据收集、相关分析、方差分析、协方差分析、回归分析等,探讨教学交互与深度学习的关系,结果表明：（1）不同教学交互水平对深度学习有显著性影响。教学交互高水平组在深度学习水平、课程知识水平和思维水平方面均显著高于教学交互低水平组;（2）“应用”“分析”“评价”三种深度认知加工行为对深度学习有显著预测力。“应用”“分析”行为能显著预测学习者深度学习水平,而“应用”“评价”行为能显著预测学习者课程知识水平和思维水平。
Abstract
Under the background of educational transformation in the 21st century, the promotion of deep learning has been paid more and more attention, among which high-quality teaching interaction is considered as the key to enhance students' deep learning. Smart classroom environment provides support for interactive teaching and deep learning. In this paper, a variety of technology-supported teaching interaction strategies are implemented in the smart classroom based on cloud. With the help of multimodal data analysis techniques, including data collection, correlation analysis, variance analysis, covariance analysis, regression analysis, etc., the interaction between teaching interaction and deep learning is discussed, and it is found that: (1) Different levels of teaching interaction have a significant impact on deep learning. The level of deep learning, curriculum knowledge and thinking in the high-level group of teaching interaction is significantly higher than that in the low-level group of teaching interaction; (2) The three deep cognitive processing behaviors of "application", "analysis" and "evaluation" have significant predictive power for deep learning. "Application" and "analysis" behaviors can significantly predict learners' deep learning level, while "application" and "evaluation" behaviors can significantly predict learners' curriculum knowledge level and thinking level.
关键词
多模态数据 /
教学交互 /
认知加工行为 /
深度学习
Key words
multimodal data /
instructional interaction /
cognitive processing behavior /
deep learning
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基金
2023年度湖北省高等学校教学改革研究项目“多模态数据分析视域下设计型学习促进研究生设计思维提升研究”; 湖北省教育厅人文社会科学研究项目“智能学习环境下促进大学生深度学习的教学交互模式及策略研究”,项目编号：20Q012; 湖北省教育科学规划2023年度一般项目“面向深度学习的中小学生STEM学习质量评价模型构建与实践研究”,项目编号：2023GB008
