Beyond Pattern Recognition: Rethinking Graph Comprehension Through Data Provenance

Authors

  • Christiane Lieb Institute for Sustainable Teaching and Learning, Albstadt-Sigmaringen University, Sigmaringen, Germany；Department of Media Education, Weingarten University of Education, Weingarten, Germany Author https://orcid.org/0000-0003-0257-8722
  • Wolfgang Mueller Department of Media Education, Weingarten University of Education, Weingarten, Germany Author
  • Carola Pickhardt Department of Life Sciences, Albstadt-Sigmaringen University, Sigmaringen, Germany Author

DOI:

https://doi.org/10.55578/fepr.2607.011

Keywords:

Visual Literacy, Data Literacy, Data Provenance, Graph Comprehension, Artificial Intelligence

Abstract

No abstract required. 

Author Biographies

  • Wolfgang Mueller, Department of Media Education, Weingarten University of Education, Weingarten, Germany

    Media Education (Prof. Dr.-Ing.)

  • Carola Pickhardt, Department of Life Sciences, Albstadt-Sigmaringen University, Sigmaringen, Germany

    Faculty of Life Sciences (Prof. Dr.)

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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Published

2026-07-03

Data Availability Statement

Data available on request from the corresponding author.

Issue

Vol. 2 No. 3 (2026)

Section

Articles

License

Copyright (c) 2026 Christiane Lieb, Wolfgang Mueller, Carola Pickhardt (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Beyond Pattern Recognition: Rethinking Graph Comprehension Through Data Provenance. (2026). Frontiers in Educational Practice and Research, 2(3), 154-159. https://doi.org/10.55578/fepr.2607.011