【作 者】：
郝晓鑫，李 艳，耿凤基
【关 键 词 】：
计算思维； 问题解决； 层级加工； 认知控制； 认知神经机制
【栏 目】：
课程与教学
【中文摘要】：
计算思维是21世纪人才必须具备的一项关键问题解决技能。当前关于计算思维的认知本质尚不清楚，给教育实践带来一系列挑战，如缺乏统一的教学框架和明确的评估准则等。为此，研究从已有理论共识和观点出发，基于算法与问题解决的相关理论，深入分析了计算思维问题解决的层级加工特性。在此基础上，借鉴层级加工的相关认知理论，进一步提出计算思维层级加工的理论假说，其核心观点是，计算思维的认知本质在于对不同抽象层级的信息或子问题及其之间的关系进行加工。从认知与神经机制来看，计算思维层级加工需要卷入不同的认知控制模式来调控工作记忆对层级信息进行持续性维持和选择性更新，在神经机制上导致大脑认知控制脑区的持续性和瞬时性高激活。伴随这些信息加工活动对认知资源的消耗，将导致认知负荷，引起大脑默认网络脑区的抑制程度增强。这些理论观点将为计算思维的认知、教学与测评提供启示，并可在后续实证研究中采用交叉学科的研究范式进行检验。
【英文摘要】：
Computational thinking is a key problem-solving skill that talents in the 21st century must possess. At present, the cognitive nature of computational thinking is still unclear, which brings a series of challenges to educational practice, such as the lack of a unified teaching framework and clear assessment criteria. To this end, this study starts from existing theoretical consensus and viewpoints, based on the relevant theories of algorithms and problem-solving, and deeply analyzes the hierarchical processing characteristics of problem-solving in computational thinking. On this basis, drawing on the relevant cognitive theories of hierarchical processing, this study further proposes a theoretical hypothesis of hierarchical processing in computational thinking, the core point of which is that the cognitive essence of computational thinking lies in processing information or sub-problems at different levels of abstraction and their interrelationships. From the perspective of cognitive and neural mechanisms, the hierarchical processing of computational thinking requires the involvement of different cognitive control modes to regulate the working memory for the sustained maintenance and selective updating of the hierarchical information, which leads to continuous and instantaneous high activation of the cognitive control area of the brain in the neural mechanism. The consumption of cognitive resources accompanied by these information processing activities will lead to cognitive load and increase the degree of inhibition in the default network of the brain. These theoretical viewpoints will provide insights for the cognition, teaching and assessment of computational thinking, and can be tested in subsequent empirical studies using an interdisciplinary research paradigm.