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Computational thinking pattern analysis: a phenomenological approach to compute computational thinking.

机译：计算思维模式分析：一种用于计算计算思维的现象学方法。

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摘要

Since the 1990s there have been multiple efforts to fix the broken pipeline at the K-12 level in computer science education, and most of those efforts have focused on the student motivational factor. The results of many studies in computer science education indicate that student motivation in computer science has been successfully increased by those efforts [ref], but most of them have failed to address educational benefits of these efforts. I believe that this biased tendency of CS education research has been caused by the lack of an adequate instrument to measure students' achieved skills with learning objectives at the semantic level. In other words, the right assessment instrument should be able to assess not only student learning skills but also achieved learning objectives: what kinds of knowledge students have learned through their activities in the class. Student learning skills may be measured with existing tools such as grading rubrics, but they are extremely time consuming and have a limited functionality to provide necessary educational feedback such as student learning progression.;I developed a learning data analysis tool to measure student-learning skills and represent students' learning achievements at the semantic level through phenomenological analysis in real-time. This concept uses a LSA [6] inspired technique, multiple high dimensional cosine calculations to analyze semantic meanings of the pre-defined subject/phenomena in a given. Theoretically, this idea can be applied to several different domains such as natural language processing and visual end user programming. Therefore, this idea can be employed to build a learning assessment tool for computer science (CS) and/or computational thinking (CT) education where visual programming is widely adopted.;As a semantic assessment tool for CS/CT learning, I propose a method, Computational Thinking Pattern Analysis (CTPA) in which nine canonical computational thinking patterns [18] work as pre-defined phenomena within a programmed artifact's context. The CTPA measures students' learning of skills (how well they have learned a skill) and students' learning of objectives (how well they have learned certain objectives) at the semantic level through phenomenological analysis from student-programmed artifacts in real-time. The outcomes of CTPA can be used to provide valid and useful educational feedback to educators and learners in CS/CT education such as measuring and tracking student learning outcomes.;Semantic assessment in CS/CT education would be able to provide better individual feedback and faster learning assessment to students and teachers by measuring student skills and challenges and analyzing learning objectives at the semantic level. This kind of feedback can be used to determine when and how teachers can expand students' learning capability in accordance with the theories of the Zone of Proximal Development and Flow [14]. A validated CTPA will contribute to the study of learning theory, professional development, and educational data mining by providing empirical data in order to refine the current conceptual framework of educational systems.;This research suggests a method that can assess students' learning skills, provide effective learning guidelines, and compute students' learning outcomes. This type of method, which cannot be found widely, can be used to create real cyberlearning systems that help large numbers of teachers and students to learn computational thinking.
机译：自1990年代以来，在计算机科学教育中曾进行过多次努力以解决K-12层次上的管道中断问题，其中大多数努力都集中在学生的动机因素上。许多计算机科学教育研究的结果表明，通过这些努力，学生对计算机科学的积极性得到了成功的提高[参考]，但是其中大多数未能解决这些努力的教育益处。我认为，CS教育研究的这种偏见趋势是由于缺乏一种足够的工具来衡量学生在语义层面上具有学习目标的已达到技能而造成的。换句话说，正确的评估工具不仅应该能够评估学生的学习技能，而且还能够评估学习目标：学生通过课堂活动中学到了哪些知识。可以使用现有的工具（例如，评分标准）来衡量学生的学习技能，但是它们非常耗时，并且功能有限，无法提供必要的教育反馈（例如学生学习进度）。;我开发了一种学习数据分析工具来衡量学生的学习技能并通过现象学分析实时地在语义层面上展示学生的学习成果。这个概念使用了LSA [6]启发的技术，多个高维余弦计算来分析给定条件下预定义主题/现象的语义。从理论上讲，该思想可以应用于几个不同的领域，例如自然语言处理和可视最终用户编程。因此，该思想可用于构建广泛应用视觉程序设计的计算机科学（CS）和/或计算思维（CT）教育的学习评估工具。;作为CS / CT学习的语义评估工具，我提出了一个方法，计算思维模式分析（CTPA），其中九种规范的计算思维模式[18]作为程序化工件上下文中的预定义现象起作用。 CTPA通过从学生编程的工件中实时进行现象学分析，在语义级别上衡量学生的技能学习（他们学习技能的程度）和目标学习情况（他们学习某些目标的程度）。 CTPA的结果可用于向CS / CT教育中的教育者和学习者提供有效和有用的教育反馈，例如测量和跟踪学生的学习结果。CS / CT教育中的语义评估将能够提供更好的个人反馈，并且更快通过测量学生的技能和挑战并在语义层次上分析学习目标，对学生和老师进行学习评估。这种反馈可以用来确定教师何时以及如何根据近端发展与流动区[14]的理论来扩大学生的学习能力。经过验证的CTPA将通过提供经验数据来完善学习系统的当前概念框架，从而有助于学习理论，专业发展和教育数据挖掘的研究;该研究提出了一种可评估学生学习技能，有效的学习指南，并计算学生的学习成果。这种无法广泛发现的方法可用于创建真正的网络学习系统，以帮助大量的师生学习计算思维。

著录项

  • 作者

    Koh, Kyu Han.;

  • 作者单位

    University of Colorado at Boulder.;

  • 授予单位 University of Colorado at Boulder.;
  • 学科 Computer Science.;Education Sciences.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 156 p.
  • 总页数 156
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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