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.
In addition to gaming, there are many other activities around digital games. These metagame activities have so far been studied from the perspective of single metagame phenomena and rarely from the perspective of the children who play digital games. This exploratory, qualitative study provides an overview of children’s metagame activities. A total of 142 children’s essays and lists of their metagame activities were analyzed using qualitative content analysis. The children’s metagame activities included game-enabling activities, strategizing activities, discussing activities, information-seeking activities, creating and sharing activities, and consuming activities. The results contribute to the body of literature on metagaming and provide an overview of children’s metagame activities around digital games, as well as new perspectives on digital games and learning.
Previous research on learning-related digital games has focused on studying learning outcomes with mostly adult participants. This study explores what children have experienced they have learned by playing digital games, how these learning experiences relate to 21st-century skills, and in which contexts do the children benefit from playing digital games. The data were collected from children’s essays, which were analyzed using qualitative content analysis. Results reveal that children’s learning experiences are often related to 21st-century core subjects and skills, but they also reported improved physical abilities and sports competences from digital games. Children felt that the skills they had gained were beneficial in the contexts of school, sports, and friendships. The results contribute to our understanding of digital games and children by providing children’s perspective on digital games and learning.
With growing concerns over children’s data agencies, researchers have begun to draw attention to children’s and young people’s privacy practices in social media environments. However, little is known about the experiences of pre-service teachers who play a key role in educating future generations. This study aimed to address this gap by exploring Finnish pre-service teachers’ conceptions and experiences of data agency in social media environments. Drawing from in-depth interviews of pre-service teachers ( N = 14), the analysis revealed that pre-service teachers construct their data agency in terms of social frames and shared social norms, and they also recognize the lack of understanding regarding wider socio-technical systems within which data agencies are situated. This research argues that without a sophisticated understanding of algorithmic governance and commercial use of data, it is unlikely that these future teachers would be prepared to facilitate children’s and youth’s agentive actions in a data-driven society.
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