随着智能时代的到来,智能技术与现代教育的深度融合正引领学生学习方式发生革命性变化,其核心标志为智慧学习的兴起。智慧学习致力于构建一个互动化、情境化、个性化与自主化的学习环境,以促进学生全面发展与终身成长。然而,在关联认知...随着智能时代的到来,智能技术与现代教育的深度融合正引领学生学习方式发生革命性变化,其核心标志为智慧学习的兴起。智慧学习致力于构建一个互动化、情境化、个性化与自主化的学习环境,以促进学生全面发展与终身成长。然而,在关联认知负荷理论视角下,智慧学习展现巨大潜力的同时,也面临认知资源分配不均、自主学习能力欠缺及创新思维受限等挑战。文章旨在通过该理论框架,优化学习资源设计,打造有益的学习生态环境并创新学习方法,以期在智能时代背景下,有效促进学生深度学习、知识融合与创新思维的显著提升。With the advent of the intelligent age, the deep integration of intelligent technology and modern education is leading to revolutionary changes in students’ learning styles, the core symbol of which is the rise of intelligent learning. Intelligent learning is committed to building a personalized, interactive, contextualized, and autonomous learning environment to promote students’ all-round development and lifelong growth. However, from the perspective of associative cognitive load theory, while intelligent learning shows great potential, it also faces challenges such as uneven distribution of cognitive resources, lack of independent learning ability, and limited innovative thinking. Based on this theoretical framework, the paper aims to optimize the design of learning resources, create a beneficial learning ecological environment, and innovate learning methods so as to effectively promote the significant improvement of students’ deep learning, knowledge integration, and innovative thinking in the context of the intelligent era.展开更多
文摘随着智能时代的到来,智能技术与现代教育的深度融合正引领学生学习方式发生革命性变化,其核心标志为智慧学习的兴起。智慧学习致力于构建一个互动化、情境化、个性化与自主化的学习环境,以促进学生全面发展与终身成长。然而,在关联认知负荷理论视角下,智慧学习展现巨大潜力的同时,也面临认知资源分配不均、自主学习能力欠缺及创新思维受限等挑战。文章旨在通过该理论框架,优化学习资源设计,打造有益的学习生态环境并创新学习方法,以期在智能时代背景下,有效促进学生深度学习、知识融合与创新思维的显著提升。With the advent of the intelligent age, the deep integration of intelligent technology and modern education is leading to revolutionary changes in students’ learning styles, the core symbol of which is the rise of intelligent learning. Intelligent learning is committed to building a personalized, interactive, contextualized, and autonomous learning environment to promote students’ all-round development and lifelong growth. However, from the perspective of associative cognitive load theory, while intelligent learning shows great potential, it also faces challenges such as uneven distribution of cognitive resources, lack of independent learning ability, and limited innovative thinking. Based on this theoretical framework, the paper aims to optimize the design of learning resources, create a beneficial learning ecological environment, and innovate learning methods so as to effectively promote the significant improvement of students’ deep learning, knowledge integration, and innovative thinking in the context of the intelligent era.
