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Thinking Fast and Slow in Computer Problem Solving

机译:在计算机问题解决中快速思考和缓慢思考

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Research in spreadsheet management proved that the overuse of slow thinking, rather than fast thinking, is the primary source of erroneous end-user computing. However, we found that the reality is not that simple. To view end-user computing in its full complexity, we launched a project to investigate end-user education, training, support, activities, and computer problem solving. In this project we also set up the base and mathability-extended typology of computer problem solving approaches, where quantitative values are assigned to the different problem solving methods and activities. In this paper we present the results of our analyses of teaching materials collected in different languages from all over the world and our findings considering the different problem solving approaches, set in the frame of different thinking modes, the characteristics of expert teachers, and the meaning system model of teaching approaches. Based on our research, we argue that the proportions of fast and slow thinking and most importantly their manifestation are responsible for erroneous end-user activities. Applying the five-point mathability scale of computer problem solving, we recognized slow thinking activities on both tails and one fast thinking approach between them. The low mathability slow thinking activities, where surface navigation and language details are focused on, are widely accepted in end-user computing. The high mathability slow thinking problem solving activities, where the utilization of concept based approaches and schema construction take place, is hardly detectable in end-user activities. Instead of building up knowledge which requires slow thinking and then using the tools with fast thinking, end-users use up their slow thinking in aimless wandering in huge programs, making wrong decisions based on their untrained, clueless intuition, and distributing erroneous end-user documents. We also found that the dominance of low mathability slow thinking activities has its roots in the education system and through this we point out that we are in great need of expert teachers and institutions and their widely accepted approaches and methods.
机译:电子表格管理方面的研究证明,过度使用慢速思维而非快速思维是最终用户计算错误的主要原因。但是,我们发现现实并非如此简单。为了查看最终用户计算的全部复杂性,我们启动了一个项目来调查最终用户的教育,培训,支持,活动和计算机问题解决。在这个项目中,我们还建立了计算机问题解决方法的基础和数学扩展类型,其中将定量值分配给不同的问题解决方法和活动。在本文中,我们介绍了我们对来自世界各地以不同语言收集的教材的分析结果,并考虑了不同的问题解决方法,并在不同的思维方式,专家教师的特征和含义的框架下进行了研究,得出了我们的发现。教学方法的系统模型。根据我们的研究,我们认为快速思考和缓慢思考的比例以及最重要的是它们的体现是错误的最终用户活动的原因。应用计算机问题解决的五点数学量表,我们识别了两条尾巴上的缓慢思考活动以及它们之间的一种快速思考方法。低数学思考能力的慢活动(重点是表面导航和语言详细信息)已在最终用户计算中被广泛接受。在最终用户活动中几乎无法检测到具有高数学性,思维缓慢的问题解决活动,在这些活动中利用了基于概念的方法和架构。最终用户不必积累需要缓慢思考的知识,然后再使用具有快速思考能力的工具,而是在漫长的程序中漫无目的地走动自己的缓慢思考,根据他们未经训练的无知直觉做出错误的决定,并分配错误的最终用户文件。我们还发现,低数学思考能力的主导地位源于教育体系,通过这一点,我们指出我们非常需要专家教师和机构及其广泛接受的方法和方法。

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