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.
翻译:在过去几十年里,机器学习技术的无数实际应用都显示了大量计算领域的数据驱动方法的潜力。机器学习越来越多地被纳入高等教育的计算课程中,而在K-12计算教育中,机器学习也在迅速增加。随着机器学习进入K-12计算教育,了解这种系统背景下的直觉和体力是如何发展成为一个关键的研究领域。但是,由于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,理解K-12教学机器学习背后的挑战对于计算教育研究来说是一个更加艰巨的挑战。尽管机器学习在现代计算领域处于中心位置,计算机教育文献的计算研究机构也正在扩展。随着机器学习进入K-12计算教育领域,人们如何学会如何培训、测试、改进和部署机器学习系统的研究数量也非常少。K-12课程空间尤其如此。这篇文章描绘了教育实践、理论和技术方面新出现的轨迹,与K-12教学中的机器学习有关,了解K-12教学方面的现有工作对于计算机教育研究是一个更加艰巨的挑战。尽管现代计算学领域的机器学习处于中心位置,但计算机教学师资在现代计算领域的中心思维中应该考虑到的一些差异。在面临这一阶段的转变。