Machine learning for middle schoolers: Learning through data-driven design

Computing Education Research Compiled: Keyword Trends, Building Blocks, Creators, and Dissemination

“…Indeed, while entire generations of people are growing up in the middle of machine learning (ML) systems, this development seems to have been given only minor attention in CER. A small but growing body of research shows concrete examples of teaching ML to beginners [119], [120], [127], [128]. New social and ethical dilemmas created by new AIsystems also call for reshaping of related training in AI ethics [129], [130].…”

“…Also in the context of CT, a large amount of CER seems to concentrate on rule-driven programming or, e.g., logic puzzles. Future recommendations [136] include changing the public misconception of "computer science = programming", changing the common stereotype that only social misfits can do programming, increasing basic training on machine learning in CT [120], and abandoning logic puzzles in favour of well-established and brilliant pedagogical toolkits, such as the CS-Unplugged [137], [138].…”

From a National Meeting to an International Conference: A Scientometric Case Study of a Finnish Computing Education Conference

“…The dominance of rule-based programming in the publications of Koli Calling and CER more generally opens up an important avenue for discussion and future research. Indeed, while our societies have become fully dependable on computational devices [43], and we have an entire generation of children growing in the middle of machine learning (ML) systems [44], this development does not seem to reflect in increase of related CER publications published in Koli Calling conference very much. It has been noted also elsewhere that this development has been given only minor attention in CER, especially in the context of schools, which still mainly focus on teaching the use of computer applications or rulebased programming [44]- [46].…”

“…Indeed, while our societies have become fully dependable on computational devices [43], and we have an entire generation of children growing in the middle of machine learning (ML) systems [44], this development does not seem to reflect in increase of related CER publications published in Koli Calling conference very much. It has been noted also elsewhere that this development has been given only minor attention in CER, especially in the context of schools, which still mainly focus on teaching the use of computer applications or rulebased programming [44]- [46]. Also, it has been estimated that the most groundbreaking technologies of the near future will be about understand-ing communities and their needs; sensing human networks and interactions, habits, behaviour, and culture; and crucial breakthroughs will increasingly not be programming breakthroughs but design breakthroughs, significantly increasing the relevance of design research [45], [47], [48], another theme that is not very much highlighted in the analysis of keyword trends of Koli Calling.…”

Learn to Machine Learn: Designing a Game Based Approach for Teaching Machine Learning to Primary and Secondary Education Students

“…Similarly, Vartiainen et al implemented design-based workshops, grounded on Papert's constructionist approach where 12-13 year old students designed their own ML applications. The workshops were grounded on realworld problems close to the students' experience and interests, using Google Teachable Machine and the researchers' own educational application for object recognition [17]. The results were promising; although the students' conceptions of ML seemed to be closely linked to their own applications and the tools used, the workshops were a good entry point for exploring ML concepts, exhibit empathy about other people's needs, and engage in inductive reasoning about the quality of their datasets and accuracy of their models.…”