Statistical literacy involves understanding, interpreting, and critically evaluating statistical information in a contextually grounded way. Current instructional practices rely heavily on visual techniques, which renders them inaccessible to students who are blind or have low vision (BLV). To bridge this gap, we formed an extended co-design partnership with a statistics teacher, a teacher for students with visual impairments (TVI), and two BLV students to develop accessibility-first practices for building statistical literacy. Through several months of collaboration that included discussion, exploration, design, and evaluation, we identified specific approaches to promote comprehension and engagement. The enactive approaches we designed, using scaffolding and timely feedback, fostered insights through pattern recognition and analogical reasoning. Additionally, inquiry-based methods promoted contextually situated reasoning and reflection on how statistics can improve students' lives and communities. We present these findings alongside participants’ experiences and discuss their implications for inclusive learning frameworks and tools.
https://dl.acm.org/doi/10.1145/3706598.3713333
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)