赋能高阶思维：数字化教学环境下促进深度学习的教学设计模型构建研究

Empowering Higher-Order Thinking: Research on Constructing Instructional Design Models to Promote Deep Learning in Digital Teaching Environments

摘要: 本文旨在应对数字化教学中普遍存在的“技术应用表层化”与“学习过程浅层化”问题，构建一个系统性的教学设计模型，以赋能学习者高阶思维，有效促进其在数字化环境下的深度学习。构建了包含“目标层(引领)、内容层(基础)、活动层(核心)、交互层(支撑)、评价层(调控)”五个核心要素，并以“技术赋能”与“数据驱动”为贯穿主线的“深度赋能”教学设计模型。案例应用表明，该模型能够系统地将数字化工具、教学活动和评价方法与深度学习目标进行对齐，通过挑战性任务、虚实融合的实践及数据驱动的反馈，有效引导和支持学生的高阶思维发展。模型强调教学设计的系统性与动态调控，对提升数字化教学的质量与效能、培养适应智能时代的创新人才具有重要的理论价值与实践指导意义。

Abstract: This paper aims to address the prevalent issues of “superficial technology application” and “shallow learning processes” in digital teaching by constructing a systematic instructional design model. This model empowers learners with higher-order thinking, effectively promoting deep learning in digital environments. It constructs a “Deep Empowerment” instructional design model comprising five core elements: the “Goal Layer (Guidance)”, “Content Layer (Foundation)”, “Activity Layer (Core)”, “Interaction Layer (Support)”, and “Assessment Layer (Regulation)”. This model is underpinned by the dual themes of “technology empowerment” and “data-driven practice.” Case applications demonstrate that this model systematically aligns digital tools, instructional activities, and assessment methods with deep learning objectives. Through challenging tasks, blended virtual-physical practices, and data-driven feedback, it effectively guides and supports students’ higher-order thinking development. Emphasizing systematic instructional design and dynamic regulation, the model holds significant theoretical value and practical guidance for enhancing the quality and effectiveness of digital teaching while cultivating innovative talents adapted to the intelligent era.