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Teaching Computational Thinking to Non-computing Majors Using Spreadsheet Functions

机译:使用电子表格函数的非计算专业教学思考

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Recently, higher education has seen an increasing emphasis on the prominent role of computational thinking in all disciplines. Computational thinking is advocated as not only a fundamental skill or concept in computer science but also a core competency for all disciplines. Teaching students in non-computer science majors computing thinking is challenging because students do not have experts' mental models. This study investigates the knowledge gap that non-computing major college students (n=126) possess about computational thinking in an introductory MS Excel course by measuring their performance using spreadsheet functions in three categories: recall, application, and problem solving. The empirical result, analyzed using ANOVA, shows that students can recall the meaning of those functions but seem to have trouble using them correctly and precisely (cued or uncued). Students' test results suggest the following issues: (1) problems with understanding the data type, (2) failure in translating problems to productive representations using spreadsheet functions, and (3) inadequate stipulation of the computational representations in precise forms. Addressing these problems early and explicitly in future classes could improve the education of computational thinking and alleviate difficulties students may experience in using computational thinking in learning and problem solving.
机译:最近,高等教育已经越来越强调计算思想在所有学科中的突出作用。倡导计算思维,不仅是计算机科学的基本技能或概念,而且是所有学科的核心竞争力。在非计算机科学专业的教学学生计算思维是挑战,因为学生没有专家的心理模型。本研究调查了非计算主要大学生(n = 126)的知识差距通过在三类中使用电子表格函数测量它们的性能来实现介绍性MS Excel课程中的计算思维:召回,应用和解决问题。使用ANOVA分析的经验结果表明,学生可以回顾这些功能的含义,但似乎无法正确且精确地使用它们(提出或未调整)。学生的测试结果表明以下问题:(1)了解数据类型的问题,(2)使用电子表格函数转换为生产性表示的问题,(3)以精确形式的计算表示不足。在未来的课程中提前和明确地解决这些问题可以改善计算思维教育,缓解学生可能在学习和解决问题中使用计算思考。

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