Designing Statistics Worked Examples for Online Versus In‐Person Learning Environments

ABSTRACT This study examined how worked examples should be designed to support statistics learning in online and in‐person contexts. About 114 undergraduate students completed surveys measuring comfort with quantitative subjects, mathematics anxiety, cognitive load, example preferences, and help‐seeking frequency. Students in both formats preferred structured step‐by‐step examples with progressive complexity. However, online students sought fewer additional examples, suggesting online environments create help‐seeking barriers. Comfort with quantitative subjects predicted greater help‐seeking and lower cognitive load, while mathematics anxiety predicted higher cognitive load with weaker effects on help‐seeking. Additionally, four student profiles emerged: (1) Struggling & Anxious, (2) Confident & Calm, (3) Moderately Anxious, and (4) Comfortable/Low Anxiety. Findings indicate effective statistics instruction requires both modality‐specific adaptations and universal design principles addressing students' affective preparation. Online environments benefit from proactive examples and structured help‐seeking mechanisms; in‐person environments can leverage real‐time interaction. Regardless of format, addressing student comfort and anxiety through scaffolded practice remains essential.