混合学习中认知负荷调控的理论整合与研究进展——基于资源与活动协同视角的文献综述

Theoretical Integration and Research Progress of Cognitive Load Regulation in Blended Learning—A Literature Review from the Perspective of Resource-Activity Synergy

摘要: 人工智能与大数据赋能的混合学习常因资源过载与活动无序引发学习者认知负荷失衡。本文基于认知负荷理论，系统梳理了混合学习中资源与活动的负荷特征及调控路径。研究发现：资源呈现冗余与组织不当易引发外在负荷超载；高阶任务与协作活动缺乏支架则导致内在负荷过高；二者共同抑制相关负荷生成，阻碍深度加工。据此，本文提出阶段性负荷分配视角，主张超越单一维度的局部优化，构建“信息呈现–任务复杂度–支架水平”三维协同调控机制，为混合式教学设计提供系统化理论依据。

Abstract: Blended learning empowered by artificial intelligence and big data often leads to cognitive load imbalance among learners due to information overload and disorganized learning activities. Grounded in cognitive load theory, this paper systematically reviews the load characteristics and regulation mechanisms of resources and activities in blended learning. The findings indicate that redundant presentation and poor organization of resources easily cause excessive extraneous cognitive load, while high-order tasks and collaborative activities without adequate scaffolding result in high intrinsic cognitive load. Together, they inhibit the generation of germane cognitive load and hinder deep learning. Accordingly, this paper proposes a phased load distribution perspective, advocating moving beyond isolated optimization of single dimensions and constructing a three-dimensional synergistic regulation mechanism covering information presentation, task complexity, and scaffolding level, so as to provide a systematic theoretical basis for blended learning design.