Deprecating Misconceptions through Context-Dependent Accounts of Productive Knowledge
Deprecating Misconceptions through Context-Dependent Accounts of Productive Knowledge
复制标题
通过对生产性知识的情境相关描述来反对误解
DOI：
10.1145/3291279.3339424
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Brian A. Danielak
中科院分区：
文献类型：
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作者：
Brian A. Danielak
本文的目的是扩大我们的意义上什么是可能的建模认知计算教育研究。我们认为，研究方法，特权规范知识这样做的代价是其他生产性知识和了解学生的方式。我们通过展示多种认知模型如何成为计算教育（CEd）分析的强大框架来探索适用的认知理论。最后，我们的结论与探索认识论的关注，认为我们的研究界的一个基本问题应该注意什么算知识，知道在计算学习环境。
This paper aims to expand our sense of what's possible in modeling cognition within computing education research. We argue that research approaches that privilege canonical knowledge do so at the expense of other productive knowledge and ways of knowing that students have. We explore applicable cognitive theory by showing how manifold models of cognition can be powerful frameworks for analysis in Computing Education (CEd). Finally, we conclude with an exploration of epistemological concerns, arguing that a fundamental concern for our research community should be paying attention to what counts as knowledge and knowing in computing learning environments.