Visualisation literacy positively predicts task accuracy in both conditions, and data comics show no equalising effect for lower-literacy students¶

Claim · All claims
Evidence · 1 study · causal r2 · q3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? Visualisation literacy was a significant positive predictor of correct rate (β1 = 0.371, p = 0.004), and the VL × condition interaction was not significant (β3 = 0.059, p = 0.745), so the literacy–accuracy relationship was consistent across conditions. → Zirui Shan 2026

Evidence¶

Zirui Shan 2026¶

Zirui Shan, Vanessa Echeverria, Yuheng Li, Yi-Shan Tsai, and Roberto Martinez-Maldonado. (2026). Data Comics for Education: Evaluating Effectiveness, Benefits, and the Ethics of AI-Assisted Creation. https://arxiv.org/abs/2607.02361 doi:10.48550/arxiv.2607.02361

q3 · i? · causal · r2

Linear regression (RQ3) with correct rate as dependent variable and VL correct rate, condition and their interaction as predictors; the model explained significant variance (R2 = 0.37, F(3,116) = 22.82, p < 0.001). The interaction "was not significant, indicating that the positive association between VL and effectiveness was consistent across both conditions".

"the interaction term (VL×Condition[DC];β 3 = 0.059,t(116) = 0.327,p= 0.745) was not significant, indicating that the positive association between VL and effectiveness was consistent across both conditions"

Discussion¶

Learner Variables¶

• Statistical Reasoning — predictor: learners who differ on it differ in outcomes

Related Claims¶

• University students achieved a significantly higher correct rate answering data questions with data comics than with conventional visualisations — related
• Data comics outperformed conventional visualisations on both information retrieval and comprehension tasks, with the largest gains in comprehension, particularly multiple-insight questions — related
• Information retrieval questions were consistently easier than comprehension questions across both visualisation formats — related