智能算法推荐与高校思政教育耦合机制研究——基于认知负荷理论的分析

Research on the Integration Mechanism between Intelligent Algorithm Recommendations and Ideological and Political Education in Higher Education Institutions—Analysis Based on the Cognitive Load Theory

摘要: 面对教育数字化转型与高校思政教育精准化改革的双重背景，智能算法推荐已成为摆脱思政教育同质化困境、提升教育实效的重要技术支撑，而认知负荷理论为破解二者融合难题、构建科学耦合机制提供了坚实的教育心理学依据。当前，智能算法推荐与高校思政教育的耦合实践面临主体认知缺位驱动价值导向偏移、供需适配脱节导致认知内化深度不足、推送范式失当引发外在认知负荷超标等现实困境。基于认知负荷理论，系统剖析二者之间的耦合逻辑，构建思想引领、认知适配以及推送优化的运行机制，旨在有效调控学生的多维认知负荷，推动思政教育实现“技术赋能”与“认知适配”的有机统一。

Abstract: Against the dual backdrop of educational digital transformation and the precision-driven reform of ideological and political education in higher education institutions, intelligent algorithmic recommendation has emerged as a crucial technological support for overcoming homogenization issues and enhancing educational effectiveness. Cognitive load theory provides a solid psychological foundation for addressing integration challenges between these two domains and establishing a scientific coupling mechanism. Currently, the integration of intelligent algorithmic recommendation with ideological and political education faces practical difficulties, including value orientation deviations due to insufficient subject cognition, inadequate cognitive internalization resulting from supply-demand mismatches, and excessive external cognitive loads caused by inappropriate recommendation paradigms. Based on cognitive load theory, this study systematically analyzes the coupling mechanisms between the two aspects and proposes operational frameworks for value guidance, cognitive adaptation, and recommendation optimization, aiming to effectively regulate students’ multidimensional cognitive loads and achieve an organic integration of “technological empowerment” and “cognitive adaptation” in ideological and political education.