Beyond Pattern Recognition: Rethinking Graph Comprehension Through Data Provenance
DOI:
https://doi.org/10.55578/fepr.2607.011Keywords:
Visual Literacy, Data Literacy, Data Provenance, Graph Comprehension, Artificial IntelligenceAbstract
The increasing use of artificial intelligence (AI) in generating and processing data visualizations challenges traditional understandings of graph comprehension and visual literacy. Existing models primarily emphasize pattern recognition and the interpretation of graphical representations but often overlook reflection on the provenance of the underlying data. This perspective argues that, although pattern recognition is a necessary prerequisite, it is not sufficient for critical graph comprehension. We propose a conceptual extension of visual literacy that explicitly integrates reflection on data provenance as a core competence. This includes critical consideration of data origin, collection, processing, quality, bias, and AI-mediated influences on data generation and visualization. A preliminary analytical framework is introduced to distinguish data provenance reflection from related constructs such as statistical literacy, contextual knowledge, and critical thinking. The paper further discusses implications for research and higher education, arguing that educational interventions should foster learners' ability to critically evaluate not only what visualizations show but also how they are produced. Finally, a research agenda is outlined that calls for the theoretical refinement of data provenance reflection, the development of competence models, and the validation of assessment instruments to support its integration into future conceptualizations of visual literacy.
References
1. Börner, K., Bueckle, A., & Ginda, M. (2019). Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments. Proceedings of the National Academy of Sciences, 116(6), 1857-1864. http://dx.doi.org/10.1073/pnas.1807180116
2. Driessen, J. E. P., Vos, D. A. C., Smeets, I. & Albers, C. J. (2022) Misleading graphs in context: Less misleading than expected. PLoS ONE 17(6): e0265823. https://doi.org/10.1371/journal.pone.0265823
3. Lo, L. Y. H., Gupta, A., Shigyo, K., Wu, A., Bertini, E. & Qu, H. (2022). Misinformed by visualization: What do we learn from misinformative visualizations? Computer Graphics Forum 41(3), 515-525. https://doi.org/10.48550/arXiv.2204.09548
4. Friel, S. N., Curcio, F. R. & Bright, G. W. (2001). Making Sense of Graphs: Critical Factors Influencing Comprehension and Instructional Implications. Journal for Research in Mathematics Education 32(2), 124-158. https://doi.org/10.2307/749671
5. Firat, E. E., Denisova, A. & Laramee, R. S. (2020). Treemap Literacy: A Classroom-Based Investigation. In M. Romero & B. Sousa Santos (Hrsg.), Eurographics 2020 – Education Papers, 29-38. https://doi.org/10.2312/eged.20201032
6. Lieb, C. & Pickhardt, C. (2025). A didactic intervention to strengthen critical thinking in the interpretation of visualized data. Front. Educ. 10:1680396. https://doi.org/10.3389/feduc.2025.1680396
7. Shah, P., & Hoeffner, J. (2002). Review of graph comprehension research: Implications for instruction. Educational Psychology Review, 14(1), 47–69. https://doi.org/10.1023/A:1013180410169
8. Carpenter, P. A., & Shah, P. (1998.). A Model of the Perceptual and Conceptual Processes in Graph Comprehension. Journal of Experimental Psychology: Applied, 4(2), 75-100. https://doi.org/10.1037/1076-898x.4.2.75
9. Vancisin, T., Clarke, L. & Hinrichs, M. O. (2023). Provenance visualization: Tracing people, processes, and practices through a data-dricen approach to provenance. Digital Scholarship in the Humanities, 38(3), 1-18. https://doi.org/10.1093/llc/fqad020
10. Beschi, S., Falessi, D., Golia, S. and Locoro, A. (2025). Characterizing Data Visualization Literacy for Standardization: A Systematic Literature Review, IEEE Access, 13, 65704-65725. https://doi.org/10.1109/ACCESS.2025.3559298
11. Li, Z., Miao, H., Pascucci, V. and Liu, S. (2024). Visualization Literacy of Multimodal Large Language Models: A Comparative Study. arXiv preprint arXiv:2407.10996. https://doi.org/10.48550/arXiv.2407.10996
12. Hong, J., Seto, C., Fan, A. & Maciejewski, R. (2025). Do LLMs Have Visualization Literacy? An Evaluation on Modified Visualizations to Test Generalization in Data Interpretation. IEEE Transactions on Visualization and Computer Graphics, 31(10), 7004-7018. https://doi.org/10.1109/TVCG.2025.3536358
13. Narechania, A., Guo, S., Koh, E., Endert, A. & Hoffswell, J. (2025). Utilizing Provenance as an Attribute for Visual Data Analysis: A Design Probe With ProvenanceLens. IEEE Transactions on Visualization and Computer Graphics, 31, 8452-8465. https://doi.org/10.1109/TVCG.2025.3571708
14. Källström, K. (2025). GIFT-AI: Damn! Jpg-visual literacy through image-making with generative AI. Front. Educ. 10:1621207. https://doi.org/10.3389/feduc.2025.1621207
15. Hedayati, M. & Kay, M. (2024). What University Students Learn In Visualization Classes. IEEE Transactions on Visualization and Computer Graphics, 31(1), 1072-1082. https://doi.org/10.1109/TVCG.2024.3456291
16. Brockbank, E., Verma, A., Lloyd, H., Huey, H., Padilla, L. & Fan, J. E. (2025). Evaluating convergence between two data visualization literacy assessments. Cognitive Research: Principles and Implications, 10(15). https://doi.org/10.1186/s41235-025-00622-9
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Copyright (c) 2026 Christiane Lieb, Wolfgang Mueller, Carola Pickhardt (Author)
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