Abstract
Over the past decades, numerous practical applications of machine learning techniques have shown the potential of AI-driven and 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 and AI 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.
Highlights
C OMPUTER-BASED automation of jobs has driven changes in the labor markets since the stored-program computer revolution started to gain momentum in the 1950s
As machine learning is making its way to becoming mainstream technology [49] and core computing knowledge [81], [82], and as ML applications have become commonplace [9], computing educators have anticipated a shift in K–12 computing education, too [15]
The nascent body of literature on teaching ML in K–12 education is rapidly developing along a route that is in a number of ways different from the traditional programming and computational thinking oriented computing curricula
Summary
C OMPUTER-BASED automation of jobs has driven changes in the labor markets since the stored-program computer revolution started to gain momentum in the 1950s. The first jobs to disappear were routine tasks of a symbolic or numerical nature that were amenable to computing-based automation through explicitly stated sets of rules. Governments used computers for large-scale information processing needs, such as compiling and tabulating national census data and welfare state record-keeping [1]. Scientists gradually adopted the new technology for tasks that involved large-scale computations, such as X-ray crystallography and fluid dynamics [1], [2]. Ever-increasing volumes of digitized data and increasing processing power drove the rise of scientific computing, culminating during the 1980s in computational sciences movements in multiple fields and the emergence of e-science on national political agendas [12]
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