In “Air Pollution Visualizations for Promoting Data Literacy with Middle Schoolers and the Public,” project, we, a devoted AQI Data Literacy research team from the Technology Integrated Learning Science lab at Georgia Tech, are driving a mission to advance knowledge and best practices in promoting data literacy. Our specific focus lies on air quality and pollution visualizations. By leveraging the potency of a design-based research framework, our objective is to unravel the intricacies of how interactions between learners and data can act as a crucial stepping stone for a more profound understanding of scientific visualizations in informal learning settings. The choice to delve into air pollution as a subject arises from its dual nature—familiar to many, yet riddled with complexities that challenge non-scientists in deciphering scientific data.
At the forefront of this four-year interdisciplinary research and development initiative are experts from the School of Interactive Computing and the Center for Education Integrating Science, Mathematics, and Computing at Georgia Tech. As active contributors to the realms of learning sciences and visualization, our team is committed to bridging gaps in data literacy, recognizing it as a fundamental skill in these days. Acknowledging the challenges posed by decoding intricate data representations, especially when unfamiliar to viewers, our project posits that informal human-data interactions hold immense potential not only for sensemaking in the present but also for paving the way to future data interactions.
The research questions we’re tackling revolve around the pivotal role of data interactions in informal settings, preparing learners for future data interpretation, and designing interactions to bolster personal agency. Our objectives include the refinement and expansion of existing 1) data display prototypes, 2) crafting a curriculum for an immersive 2-week summer camp tailored for middle school students, and investigating design features that facilitate data exploration for non-expert learners. Ultimately, our project aspires to furnish evidence-based recommendations that will shape the landscape of future human-data interaction learning environments, ensuring accessibility and engagement for learners across diverse backgrounds. This article captures the essence of our collective pursuit to transform data literacy into an engaging and inclusive experience.