智能教育时代认知负荷理论发展、应用与展望——“第十一届国际认知负荷理论大会”综述

The Development, Application and Reflection of Cognitive Loading Theory in the Intelligent Era——A Review for the 11th International Cognitive Load Theory Conference

认知负荷理论已经成为学习与教学领域最有影响力的理论框架之一。认知负荷理论作为一种基于人类认知结构的教学设计理论,其核心是工作记忆的有限性,只有将认知负荷控制在工作记忆所能承载的范围之内,有效学习才能发生。在技术和学习理论的共同推动下,认知负荷理论历经多媒体学习认知理论、多媒体学习认知-情感理论、多媒体学习认知-情感整合理论等发展阶段,主要关注从认知过程和情感过程两个方面进行了理论拓展。随着智能技术的发展成熟,教育领域成为智能技术应用的重要场景,学习环境和学习方式变革也成为重要的时代命题。目前认知负荷理论在智能教育领域的应用主要体现在自主学习、移动学习、游戏化学习、沉浸式学习、基于计算机的模拟学习等方面。未来认知负荷理论应进一步丰富和完善理论模型,并将其运用到以智能技术为依托的复杂学习场景中,同时应充分利用脑科学和大数据技术来完善认知负荷的测量技术和方法。

Nowadays, cognitive load theory has become one of the most influential theoretical frameworks forlearning and teaching in the field of education. It is a teaching design theory based on human cognitive structure,the basic idea of which is that cognitive capacity in working memory is limited, so that if a learning task requires toomuch capacity, learning will be hampered. Due to the development of technology and learning theory, cognitiveloading theory has passed four stages： Cognitive Loading Theory, Cognitive Theory of Multimedia Learning,Cognitive-Affective Theory of Multimedia Learning, Integrated Cognitive-Affective Theory of Learning with Media,mainly expanding from both cognitive and emotional processes. With the development of intelligent technology, theeducation field has become one of the important application scenarios for the application of intelligent technology.In this context, learning environment and learning approach will undergo unprecedented changes. Currently,according to conference participants＇ report, the cognitive load theory is applied into the intelligent education fieldin such aspects as self-regulated learning, mobile learning, digital game-based learning, immersive virtual learning,as well as computer-based simulation learning. In the future, the theoretical model of cognitive load theory shouldbe improved and be applied into the complex learning environment which is dependent on intelligent technology. Atthe same time, the measurement of the cognitive load should be perfected with the help of brain science and big datatechnology.