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A Semi-automated Approach to Categorise Learning Outcomes into Digital Literacy or Computer Science

机译:半自动方法,将学习成果分类为数字识字或计算机科学

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Computer science related curricula, standards and frameworks are designed and implemented in many countries to incorporate informatics education in schools, already starting with kindergarten and primary education. A recurring point of discussion addresses the focus of those educational models concerning the different fields of computer science - the topics related to the scientific subject of computer science, or digital literacy (the set of skills and competencies needed in everyday life in the digital age). In this paper, we present a semi-automated approach to categorise learning outcomes of computer science related curricula into one of those two categories. Categorisation is performed with linguistic metrics computed for nouns and verbs of representative curricula of each category. The categorisation is compared against classifications of nine experts of computer science teaching and research. The results show a matching categorisation for 70% of all learning outcomes and 90% of learning outcomes uniformly classified by the experts.
机译:计算机科学相关课程,标准和框架在许多国家设计和实施,在学校纳入学校的信息教育,已经开始与幼儿园和小学教育。一个经常性的讨论点解决了关于计算机科学不同领域的教育模式的焦点 - 与计算机科学科学主题有关的主题,或数字扫盲(数字时代日常生活中所需的一组技能和能力) 。在本文中,我们提出了一种半自动方法,将计算机科学相关课程的学习结果分类为这两类中的一个。用针对每个类别的代表课程的名词和动词计算的语言指标进行分类。将分类与计算机科学教学教学和研究专家的分类进行比较。结果表明,70%的匹配分类,所有学习成果的70%和90%的学习成果由专家均匀分类。

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