Tensions and trade-offs of participatory learning in the age of machine learning

Abstract

While much has been written about the personal, social, and democratic benefits of networked communities and participatory learning, critics have begun to draw attention to the ubiquitous data collection and computational processes behind mass user platforms. Personal and behavioral data have become valuable material for statistical and machine learning techniques that have the potential to profile, infer, and predict people's needs, values, and behavior. As a response, researchers are calling for data literacies and computational thinking to facilitate people's capacity and volition to make informed actions in their digital world. Yet, efforts and curricula towards a greater understanding of computational mechanisms of new media ecology are sorely missing from K12-education as well as from teacher education. This paper provides an overview of tensions that teachers and educators will face when they attempt to bridge participatory learning with a more robust understanding of machine learning and algorithmic production of social and cultural practices.

A systematic review of teaching and learning machine learning in K-12 education

“…Resulting from our approach, we discovered that most of the reviewed papers of studies leaned towards teaching ML to high schoolers. Literature (Su & Yang, 2022) has shown that limited discussion exists about AI activities appropriate for younger children, however, studies have been carried out to introduce ML at early years of pupils' generation of ML knowledge (Druga et al, 2019;Vartiainen et al, 2020aVartiainen et al, , 2020b. Nevertheless, no basis could be identified to assume that high schools are the levels that requires ML knowledge, it is essential to conduct research on ML projects suitable for kindergarten, elementary and middle schools.…”

“…It can be deduced from the literature that ML has been taught across K-12 levels from primary to high school. These have also been carried out in various settings such as in laboratory (Essinger & Rosen, 2011), classroom (Chai et al, 2020), summer camp , workshop (e.g., Druga et al, 2019) and at home (Vartiainen et al, 2020a(Vartiainen et al, , 2020b. The objective of the identified studies focuses on how to teach central machine learning concepts with a paper (Vachovsky et al, 2016) concerned with increasing girls' interest in AI.…”

“…Active-based learning activities were also introduced to the students through class exercises and facilitated discussions (e.g., Estevez et al, 2019). The use of participatory learning and collaborative methods is also evident in the literature while children co-teach a computer through bodily expressions (Vartiainen et al, 2020a(Vartiainen et al, , 2020b. Problem-based learning was also utilized in the study of Essinger and Rosen, (2011) and Evangelista et al (2018).…”

“…Empirical evaluation of studies and project is pertinent even though several appraisal approaches have been utilized (Giannakos et al, 2020) while a solid rubric should be formulated to measure participants' learning gains. Longitudinal pedagogical research should also be considered as it can expand on the understanding of how computational thinking and understanding of machine learning develop in a process of learning (Vartiainen et al, 2020a(Vartiainen et al, , 2020b. Furthermore, most studies center on supervised machine learning, particularly on classification tasks, which is regarded as the simplest ML form.…”

“…Gathering the understandings of teachers and their experiences in co-designing and scaffolding AI in classrooms from various cultural contexts will help generate more useful insight (Chiu & Chai, 2020). In order to democratize access to AI literacy, Wan et al (2020) recommend co-design workshops with K-12 schoolteachers, Vartiainen et al, (2020aVartiainen et al, ( , 2020b) also suggest co-design for children with the support of more experienced peers or adults to build their own machine learning applications. With this, children can gradually develop a deepening understanding of, for instance, different machine learning techniques, data sets, under and over fitting, and testing and improving their systems.…”