Playing, Moving and Designing with Data: Exploring Young Students' Data Literacy Skills in Embodied Classification Games"

Abstract

Recent educational research and agendas highlight the importance of cultivating data literacy in K-12 education to prepare students for AI-driven challenges. This can be leveraged by the development of educational digital designs that would engage students with data literacy practices such as identifying data patterns, critically analysing data and understanding how AI algorithms classify data and represent information. However, few tools address socio-technical aspects like algorithmic classification or dataset bias. In this paper, we discuss the design and evaluation of an online game authoring system, called SorBET, that aims to engage young students with data literacy through the collaborative play and design of classification games. Understanding classification not as a neutral process, but as a constructed, contestable activity, is central to developing critical data literacy. SorBET integrates three diverse computational affordances to allow users' engagement with data as they play or design digital games. As players, they collaborate to classify falling objects into predefined categories using hand gestures and voice commands. As designers, they access and modify the game data with an interactive database and the game rules with block-based programming. A two-cycle design-based study with secondary school students revealed that SorBET's design features effectively supported data literacy practices by enabling dynamic data analysis, interpretation, and manipulation. The embodied interaction modality fostered collaboration and prompted critical discussions around classification systems. These findings suggest that multimodal, editable environments can transform data literacy from passive skill acquisition to active, socially negotiated practice.