Henriikka Vartiainen

TL;DR: This case study explored how six very young children taught and explored Google’s Teachable Machine in nonschool settings and illustrated the quick-paced and embodied nature of the child-computer interaction that also supported children to reason about the relationship between their own bodily expressions and the output of an interactive ML-based tool.

TL;DR: An overview of some computational mechanisms of today’s media is provided and ways of intertwining media literacy education with computing education in order to improve students’ readiness to cope with modern media and to become critical and skilled actors to navigate in the today's media landscape are suggested.

TL;DR: In this article, the authors chart the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education, and describe some differences that K -12 computing educators should take into account when facing this challenge.

TL;DR: Results show that multigraph configuration produces the most consistent and robust centrality measures, and degree centralities calculated with the multigraph methods are reliable indicators for students’ participatory efforts as well as a consistent predictor of their performance.

TL;DR: The findings demonstrate that certain interaction variables are indicative of a well-performing group; particularly the quantity of interactions, active and reciprocal interactions among students, and group cohesion measures (transitivity and reciprocity).