Connecting the Dots: Visual Accessibility and Depictions of Change in Statistics Education Graphs

Data and statistical findings can be communicated effectively using graphs, often better than could be done with numbers or words. In pretest-posttest designs, graphs can be especially useful for visualizing patterns of change. In this study, we sought to examine the quantity of graphs, how variability and error were visualized, and the visual accessibility of graphs used in statistics education literature. Data came from all articles with a two-timepoint design published in three statistics education research journals (Journal of Statistics and Data Science Education, Statistics Education Research Journal, Teaching Statistics) between 2012 and 2024. Results indicate that over half of all published articles implementing pretest-posttest designs had an associated graph. Variability was frequently depicted using distributional methods, but individual-level data and trajectories were rarely shown, limiting the ability to fully represent change over time. Visual accessibility was often poor: small text size and missing or uninformative alt-text were the most common barriers, and many graphs were inaccessible to individuals with color vision deficiencies or reduced contrast sensitivity. To support improved graphing practices, we developed an open-source Shiny web application that allows users to create publication-ready, accessible graphs for pretest–posttest data and provides guidance on effective visualization practices.