Sensing the Shape of Data: Non-Visual Exploration of Statistical Concepts in Histograms with Blind and Low-Vision Learners

Abstract: Statistical concepts often rely heavily on visual cues for comprehension, presenting challenges for individuals who face difficulties using visual information, such as the blind and low-vision (BLV) community. While prior work has explored making data visualizations accessible, limited research examines how BLV individuals conceptualize and learn the underlying statistical concepts these visualizations represent. To better understand BLV individuals' learning strategies for potentially unfamiliar statistical concepts, we conducted a within-subjects experiment with 7 BLV individuals, controlling for vision condition using blindfolds. Each participant leveraged three different non-visual representations (Swell Touch tactile graph (STGs), shaped data patterns on a refreshable display (BDPs), sonification) to understand three different statistical concepts in histograms (skewness, modality, kurtosis). We collected quantitative metrics (accuracy, completion time, self-reported confidence levels) and qualitative insights (gesture analysis) to identify participants' unique meaning-making strategies. Results revealed that the braille condition led to the most accurate results, with sonification tasks being completed the fastest. Participants demonstrated various adaptive techniques when exploring each histogram, often developing alternative mental models that helped them non-visually encode statistical visualization concepts. Our findings reveal important implications for statistics educators and assistive technology designers, suggesting that effective learning tools must go beyond simple translation of visual information to support the unique cognitive strategies employed by BLV learners.