Teaching Machine Learning in K-12 Computing Education: Potential and Pitfalls
Teaching Machine Learning in K-12 Computing Education: Potential and Pitfalls
复制标题
K-12 计算机教育中的机器学习教学：潜力和陷阱
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
A. Pears
中科院分区：
文献类型：
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作者：
M. Tedre;Tapani Toivonen;J. Kahila;Henriikka Vartiainen;Teemu Valtonen;I. Jormanainen;A. Pears
在过去的几十年中，机器学习技术的众多实际应用已经显示了大量计算字段中数据驱动方法的潜力。机器学习越来越多地包括在高等教育的计算课程中，并且在K-12计算机教育中也将迅速增长的计划扩大。随着机器学习进入K-12计算教育，了解此类系统中的直觉和代理如何成为关键研究领域。但是，随着学校和教师已经在努力将传统的计算思维和传统人工智能纳入学校课程中，了解K-12中的教学机器学习背后的挑战是计算教育研究的挑战。尽管机器学习在现代计算领域的中心位置，但计算教育研究机构的文献研究机构很少有关于人们如何学习训练，测试，改进和部署机器学习系统的研究。 K-12课程空间尤其如此。本文列出了与K-12教育中教学机器学习有关的教育实践，理论和技术中的新兴轨迹。这篇文章将现有的工作一般地定位在计算教育的背景下，并描述了K-12计算机教育者在面对这一挑战时应考虑的一些差异。本文着重于将机器学习成功地集成到更广泛的K-12计算课程中，范式转移的关键方面。一个关键的步骤是放弃这样一种信念，即基于规则的“传统”编程是发展下一代计算思维的核心方面和基础。
Over the past decades, numerous practical applications of machine learning techniques have shown the potential of data-driven approaches in a large number of computing fields. Machine learning is increasingly included in computing curricula in higher education, and a quickly growing number of initiatives are expanding it in K-12 computing education, too. As machine learning enters K-12 computing education, understanding how intuition and agency in the context of such systems is developed becomes a key research area. But as schools and teachers are already struggling with integrating traditional computational thinking and traditional artificial intelligence into school curricula, understanding the challenges behind teaching machine learning in K-12 is an even more daunting challenge for computing education research. Despite the central position of machine learning in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train, test, improve, and deploy machine learning systems. This is especially true of the K-12 curriculum space. This article charts the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education. The article situates the existing work in the context of computing education in general, and describes some differences that K-12 computing educators should take into account when facing this challenge. The article focuses on key aspects of the paradigm shift that will be required in order to successfully integrate machine learning into the broader K-12 computing curricula. A crucial step is abandoning the belief that rule-based"traditional"programming is a central aspect and building block in developing next generation computational thinking.
展望 K-12 的人工智能：每个孩子都应该了解哪些关于人工智能的知识？
DOI：
10.1609/aaai.v33i01.33019795
发表时间：
2019
期刊：
Proceedings of the AAAI Conference on Artificial Intelligence
影响因子：
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作者：
Touretzky, David;Gardner-McCune, Christina;Martin, Fred;Seehorn, Deborah
通讯作者：
Seehorn, Deborah