Phenomenological programming: a novel approach to designing domain specific programming environments for science learning

Phenomenological programming: a novel approach to designing domain specific programming environments for science learning
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现象学编程:一种为科学学习设计特定领域编程环境的新方法

DOI:
10.1145/3392063.3394428
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发表时间:
2020
期刊:
ACM Interaction Design and Children (IDC
影响因子:
--
通讯作者:
Wilensky, Uri
Wilensky, Uri
中科院分区:
--
文献类型:
--
作者:
Aslan, Umit;LaGrassa, Nicholas;Horn, Michael;Wilensky, Uri

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人们对使用基于计算机的科学现象模型作为课堂课程的一部分越来越感兴趣,尤其是学习者为自己创建的模型。然而,虽然研究表明,构建现象的计算模型可以作为学习科学的强大基础,但这种方法很难在课堂上得到广泛采用,因为它不仅需要教师学习复杂的技术工具(例如计算机编程),而且还需要宝贵的教学时间向学生介绍这些工具。此外,即使在新手友好的环境中,许多核心科学主题(例如运动分子理论、自然选择和电学)也很难建模。为了解决这些限制,我们提出了一种称为现象学编程的新颖设计方法,该方法建立在学生对现实世界对象、模式和事件的直观理解的基础上,以支持基于代理的计算模型的构建。我们提出初步案例研究,并讨论它们对 STEM 内容学习以及现象学编程的可学习性和表达能力的影响。
There has been a growing interest in the use of computer-based models of scientific phenomena as part of classroom curricula, especially models that learners create for themselves. However, while studies show that constructing computational models of phenomena can serve as a powerful foundation for learning science, this approach has struggled to gain widespread adoption in classrooms because it not only requires teachers to learn sophisticated technological tools (such as computer programming), but it also requires precious instructional time to introduce these tools to students. Moreover, many core scientific topics such as the kinetic molecular theory, natural selection, and electricity are difficult to model even with novice-friendly environments. To address these limitations, we present a novel design approach calledphenomenological programmingthat builds on students' intuitive understanding of real-world objects, patterns, and events to support the construction of agent-based computational models. We present preliminary case studies and discuss their implications for STEM content learning and the learnability and expressive power of phenomenological programming.
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