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In the educational context, graph literacy describes the competence to read, comprehend, and interpret formative assessment data in terms of data-based decision-making (DBDM) in order to derive and justify individual adaptations of instruction based on them. Since misconceptions may arise in predicting a future learning progress due to the characteristics of the data base as well as the approach to graph literacy, effective supports are needed, especially for inexperienced teachers. We present two interrelated studies to broaden the field of support in graph literacy. In Study I, graph literacy procedures are collected from N = 196 university student teachers using an online survey that includes six case vignettes with learning progress prediction tasks. Results show that both regular and special education student teachers intuitively neglect important data points in interpretation and they do not use a consistent strategy in prediction across the case vignettes (Fleiss’ κ = 0.071; p < 0.001). Building on the results of Study I, a 3-min video intervention for linear trend identification using Tukey Tri-Split was developed. Study II tested the efficacy of the video intervention on the accuracy of future learning progress among student teachers and in-service teachers (N = 198) using randomized group assignment compared to a brief text hint. With a large effect size of Cohens’ f = 0.39, the video instruction shows high efficacy compared to the text hint. The increasing importance of DBDM in inclusive and special education is discussed.