Data visualisation literacy in higher education: An exploratory study of understanding of a learning dashboard tool
Open accessAbstract
The visualisation of data has become ubiquitous. Visualisations are used to represent data in a way that is easy to understand and useful in our lives. Each data visualisation needs to be suitable to extract the correct information to complete a task and make an informed decision while minimising the impact of biases. To achieve this, the ability to create and read visualisations has become as important as the ability to read and write. Therefore, the Information Visualisation community is applying more attention to literacy and decision making in data vis-ualisations. Until recently, researchers lacked valid and reliable test in-struments to measure the literacy of users or the taxonomy to detect biased judgement in data visualisations. A literature review showed there is relatively little research on data visualisations for different user data literacy levels in authentic settings and a lack of studies that pro-vide evidence for the presence of cognitive biases in data visualisa-tions. This exploratory research study was undertaken to develop a method to assess perceived usefulness and confidence in reporting dashboards within higher education by adapting existing research in-struments. A survey was designed to test perceived usefulness, per-ceived skill and 24 multiple-choice test items covering six data visuali-sations based on eight tasks. The study was sent to 157 potential par-ticipants, with a response rate of 20.38%. The results showed data vis-ualisations are useful, but the purpose of some data visualisations is not always understood. Also, we showed there is a consensus that re-spondents perceive their data visualisation literacy is higher than they believe their peers to be. However, the higher their overconfidence, the lower their actual data visualisation literacy score. Finally, we discuss the benefits, limitations and possible future research areas.
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Many students experience difficulties comprehending complex visual learning analytics because of limited data visualisation literacy.
“Yet many students experienced difficulties in comprehending complex VLA due to their limited data visualisation literacy.”
From The effects of generative AI agents and scaffolding on enhancing students’ comprehension of visual learning analytics (2025) · cites this paper as Donohoe & Costello, 2020
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414 · see citing works
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · see citing works
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035 · see citing works
User Acceptance of Computer Technology: A Comparison of Two Theoretical Models
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989) · Management Science · doi:10.1287/mnsc.35.8.982 · Cited by 25,709 · see citing works
Research Methods for Business Students
(2000) · Qualitative Market Research An International Journal · doi:10.1108/qmr.2000.3.4.215.2 · Cited by 5,631 · see citing works
Dunn, T. J., Baguley, T. S., & Brunsden, V. (2013) · British Journal of Psychology · doi:10.1111/bjop.12046 · Cited by 3,758 · see citing works
Test Theory: A Unified Treatment
Zwick, R., & McDonald, R. P. (2000) · Journal of the American Statistical Association · doi:10.2307/2669496 · Cited by 3,637 · see citing works
Technology acceptance model: a literature review from 1986 to 2013
Marangunić, N., & Granić, A. (2015) · Universal Access in the Information Society · doi:10.1007/s10209-014-0348-1 · Cited by 2,458 · see citing works
The trouble with overconfidence.
Moore, D. A., & Healy, P. J. (2008) · Psychological Review · doi:10.1037/0033-295x.115.2.502 · Cited by 2,358 · see citing works
The Visual Display of Quantitative Information.
Zelterman, D., & Tufte, E. R. (1984) · Journal of the American Statistical Association · doi:10.2307/2288294 · Cited by 2,088 · see citing works
The weighing of evidence and the determinants of confidence
Griffin, D., & Tversky, A. (1992) · Cognitive Psychology · doi:10.1016/0010-0285(92)90013-r · Cited by 1,796 · see citing works
Preference and belief: Ambiguity and competence in choice under uncertainty
Heath, C., & Tversky, A. (1991) · Journal of Risk and Uncertainty · doi:10.1007/bf00057884 · Cited by 1,654 · see citing works
Real world research : a resource for users of social research methods in applied settings
Robson, C. (2011) · Cited by 1,239 · see citing works
A review of technology acceptance and adoption models and theories
Taherdoost, H. (2018) · Procedia Manufacturing · doi:10.1016/j.promfg.2018.03.137 · Cited by 1,162 · see citing works
Gjersing, L., Caplehorn, J. R. M., & Clausen, T. (2010) · BMC Medical Research Methodology · doi:10.1186/1471-2288-10-13 · Cited by 909 · see citing works
Measurement and Evaluation in Psychology and Education.
Jarrett, R. F., Thorndike, R. L., & Hagen, E. (1956) · Journal of the American Statistical Association · doi:10.2307/2281462 · Cited by 572 · see citing works
Perceiving Learning at a Glance: A Systematic Literature Review of Learning Dashboard Research
Schwendimann, B. A., Rodríguez‐Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., Gillet, D., & Dillenbourg, P. (2016) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2016.2599522 · Cited by 551 · see citing works
Wickham, H. (2009) · Journal of Computational and Graphical Statistics · doi:10.1198/jcgs.2009.07098 · Cited by 400 · see citing works
Moore, D. A., & Cain, D. M. (2006) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2006.09.002 · Cited by 344 · see citing works
VLAT: Development of a Visualization Literacy Assessment Test
Lee, S., Kim, S.-H., & Kwon, B. C. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2598920 · Cited by 292 · see citing works
A Principled Way of Assessing Visualization Literacy
Boy, J., Rensink, R. A., Bertini, E., & Fekete, J. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346984 · Cited by 280 · see citing works
Toward Better Understanding and Use of Business Intelligence in Organizations
Olszak, C. M. (2016) · Information Systems Management · doi:10.1080/10580530.2016.1155946 · Cited by 166 · see citing works
A Task-Based Taxonomy of Cognitive Biases for Information Visualization
Dimara, E., Franconeri, S., Catherine Plaisant, B., Bezerianos, A., & Dragicevic, P. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2872577 · Cited by 165 · see citing works
Peck, E. M., Ayuso, S. E., & El-Etr, O. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300474 · Cited by 156 · see citing works
Patrick Cavanagh, B. (2011) · Vision Research · doi:10.1016/j.visres.2011.01.015 · Cited by 147 · see citing works
Low, D., Clark, N., Soar, J., Padkin, A., Stoneham, A., Perkins, G. D., & Nolan, J. P. (2011) · Anaesthesia · doi:10.1111/j.1365-2044.2011.06649.x · Cited by 130 · see citing works
Frames and Slants in Titles of Visualizations on Controversial Topics
Kong, H., Liu, Z., & Karahalios, K. G. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174012 · Cited by 123 · see citing works
West, R. F., & Stanovich, K. E. (1997) · Psychonomic Bulletin & Review · doi:10.3758/bf03210798 · Cited by 119 · see citing works
Diamond, J. J., & Evans, W. (1973) · Review of Educational Research · doi:10.2307/1169935 · Cited by 118 · see citing works
Formula Scoring of Multiple‐Choice Tests (Correction for Guessing)
Frary, R. B. (1988) · Educational Measurement Issues and Practice · doi:10.1111/j.1745-3992.1988.tb00434.x · Cited by 107 · see citing works
Accommodating color blind computer users
Jefferson, L., & Harvey, R. (2006) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/1168987.1168996 · Cited by 98 · see citing works
Performance Overconfidence: Metacognitive Effects or Misplaced Student Expectations?
Clayson, D. E. (2005) · Journal of Marketing Education · doi:10.1177/0273475304273525 · Cited by 92 · see citing works
Three‐dimensional visualisation improves understanding of surgical liver anatomy
Beermann, J., Tetzlaff, R., Bruckner, T. M., Schöebinger, M., Müller-Stich, B. P., Gutt, C. N., Meinzer, H.-P., Kadmon, M., & Fischer, L. (2010) · Medical Education · doi:10.1111/j.1365-2923.2010.03742.x · Cited by 86 · see citing works
McKeown, C. (2010) · Ergonomics · doi:10.1080/00140130903458285 · Cited by 84 · see citing works
Data Visualization Literacy: Investigating Data Interpretation Along the Novice–Expert Continuum
Maltese, A. V., Harsh, J. A., & Svetina, D. (2015) · The journal of college science teaching · doi:10.2505/4/jcst15_045_01_84 · Cited by 82 · see citing works
The eye of the beholder' -Designing for colour-blind users
Rrigden, C. (1999) · British telecommunications engineering · Cited by 77 · see citing works
Nurbekova, Z. K., Гриншкун, В. В., Aimicheva, G., Nurbekov, B., & Tuyenbayeva, K. (2020) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v15i08.12335 · Cited by 70 · see citing works
Application of 3D Visualization in Landscape Design Teaching
Jiang, W., & Zhang, Y. (2019) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v14i06.10156 · Cited by 68 · see citing works
Visualization of Uncertainty and Reasoning
Zuk, T., & Carpendale, S. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73214-3_15 · Cited by 67 · see citing works
Visualizations of Online Course Interactions for Social Network Learning Analytics
Hernández-García, Á., González-González, I., Jiménez‐Zarco, A. I., & Chaparro-Peláez, J. (2016) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v11i07.5889 · Cited by 30 · see citing works
The IT performance evaluation in the construction industry
Li, H., Irani, Z., & Love, P. E. D. (2005) · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.1109/hicss.2000.926927 · Cited by 26 · see citing works
In MOOCs we Trust: Learner Perceptions of MOOC Quality via Trust and Credibility
Costello, E., Brunton, J., Brown, M., & Daly, L. (2018) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v13i06.8447 · Cited by 24 · see citing works
Self-citation2001: Information Technology and the U.S. Productivity Revival: a Review of the Evidence
Stiroh, K. J. (2016) · Palgrave Macmillan US eBooks · doi:10.1007/978-1-137-57251-6_28 · Cited by 21 · see citing works
Assessing the Evaluation Models of Business Intelligence Maturity and Presenting an Optimized Model
Tavallaei, R., Shokohyar, S., Moosavi, S. M., & Sarfi, Z. (2015) · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
GLAT: The generative AI literacy assessment test
Jin, Y., Martínez‐Maldonado, R., Gašević, D., & Yan, L. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100436 · Cited by 119 · see citing works
Yan, L., Martinez-Maldonado, R., Jin, Y., Echeverria, V., Milesi, M., Fan, J., Zhao, L., Alfredo, R., Li, X., & Gašević, D. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105322 · Cited by 85 · see citing works
Interactive visualization literacy: The state-of-the-art
Firat, E. E., Joshi, A., & Laramee, R. (2022) · Information Visualization · doi:10.1177/14738716221081831 · Cited by 81 · see citing works
Scaffolding online peer critiquing to develop students’ epistemic criteria of data visualization
Chang, H. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104863 · Cited by 47 · see citing works
Shao, H., Martinez-Maldonado, R., Echeverria, V., Yan, L., & Gasevic, D. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3643022 · Cited by 45 · see citing works
Pozdniakov, S., Martinez-Maldonado, R., Tsai, Y.-S., Echeverria, V., Srivastava, N., & Gasevic, D. (2023) · LAK23: 13th International Learning Analytics and Knowledge Conference · doi:10.1145/3576050.3576063 · Cited by 44 · see citing works
Yan, L., Zhao, L., Echeverria, V., Jin, Y., Alfredo, R., Li, X., Gaševi’c, D., & Martinez-Maldonado, R. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-64299-9_13 · Cited by 40 · see citing works
Strategies and difficulties during students’ construction of data visualizations
Chang, H., Chang, Y.-J., & Tsai, M. (2024) · International Journal of STEM Education · doi:10.1186/s40594-024-00463-w · Cited by 41 · see citing works
ALGORITHMIC LITERACY: Generative Artificial Intelligence Technologies for Data Librarians
Semeler, A. R., Pinto, A., Koltay, T., Dias, T., Oliveira, A., Gonzalez, J., & Rozados, H. B. F. (2024) · EAI Endorsed Transactions on Scalable Information Systems · doi:10.4108/eetsis.4067 · Cited by 23 · see citing works
Science teachers’ and students’ metavisualization in scientific modeling
Chang, H. (2021) · Science Education · doi:10.1002/sce.21693 · Cited by 15 · see citing works
The effects of visualisation literacy and data storytelling dashboards on teachers’ cognitive load
Liu, Y., Pozdniakov, S., & Martínez‐Maldonado, R. (2024) · Australasian Journal of Educational Technology · doi:10.14742/ajet.8988 · Cited by 13 · see citing works
Pozdniakov, S., Martínez‐Maldonado, R., Tsai, Y., Srivastava, N., Liu, Y., & Gašević, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42682-7_23 · Cited by 12 · see citing works
Pozdniakov, S., Martínez‐Maldonado, R., Tsai, Y., Echeverría, V., Swiecki, Z., & Gašević, D. (2024) · Journal of Learning Analytics · doi:10.18608/jla.2024.8471 · Cited by 10 · see citing works
Data-driven recommendations for enhancing real-time natural hazard warnings
Saunders, K. R., Forbes, O., Hopf, J. K., Patterson, C. R., Vollert, S. A., Brown, K., Browning, R., Canizares, M. A., Cottrell, R. S., Li, L., et al. (2025) · One Earth · doi:10.1016/j.oneear.2025.101274 · Cited by 13 · see citing works
Milesi, M. E., & Martínez‐Maldonado, R. (2024) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3636555.3636865 · Cited by 8 · see citing works
Beyond Numeracy, a Data Literacy Topical Scoping Review (2011–2023)
Vermeire, L., den Broeck, W. V., Petersen, F., & AUDENHOVE, L. V. (2025) · Media and Communication · doi:10.17645/mac.9237 · Cited by 7 · see citing works
The width and depth of literacies for tackling the COVID-19 infodemic
Koltay, T. (2022) · Journal of Documentation · doi:10.1108/jd-01-2022-0007 · Cited by 7 · see citing works
Sosa, P., Kharrazi, H. H. K., & Lehmann, H. P. (2025) · Public Health · doi:10.1016/j.puhe.2024.12.053 · Cited by 6 · see citing works
Díaz, B., Lynch, C. F., Delgado, C., & Han, K. K. (2025) · International Journal of STEM Education · doi:10.1186/s40594-025-00538-2 · Cited by 6 · see citing works
Chang, H., Binali, T., & Chang, Y.-J. (2022) · Research in Science & Technological Education · doi:10.1080/02635143.2022.2128325 · Cited by 5 · see citing works
Supporting Statistical Literacy with ICT-based Teaching Scenario
Rizou, O., Klonari, A. I., & Kavroudakis, D. (2021) · International Journal of Education (IJE) · doi:10.5121/ije.2021.9405 · Cited by 4 · see citing works
Boström, L., & Sjöström, M. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11139-9 · Cited by 4 · see citing works
Rho, J., Rau, M. A., & Zhu, X. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105508 · Cited by 3 · see citing works
Paristiowati, M., Syaadah, R. S., Amalia, A., & Fitriana, N. (2025) · Journal of Physics Conference Series · doi:10.1088/1742-6596/3139/1/012091 · Cited by 1 · see citing works
The role of data visualization literacy in public administration in the era of AI adoption
Montalvá, I., Pérez, V., & Pavía, J. M. (2026) · International Journal of Engineering Business Management · doi:10.1177/18479790261427072 · Cited by 1 · see citing works
Luna-Gijón, G., & Ruíz-Morales, Y. E. (2023) · Acta Universitaria · doi:10.15174/au.2023.3572 · Cited by 1 · see citing works
Tell Me Without Telling Me: Two-Way Prediction of Visualization Literacy and Visual Attention
Chang, M., Wang, Y., Wang, H. W., Zhou, Y., Bulling, A., & Xiong, C. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3634815 · Cited by 1 · see citing works
Tools, Techniques, and Applications of Data Visualization in Education and Machine LearningPreprint
Jacob, M., Tshedza, M. P., & Hulisani, M. (2025) · Research Square · doi:10.21203/rs.3.rs-7560670/v1 · Cited by 1 · see citing works
New literacies in the age of convergence
Koltay, T. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-824475-3.00005-9
Ismail, M. A., Noorahim, N. N., Said, F. N. H. M., & Affandi, H. M. (2022) · Jurnal Dunia Pendidikan · doi:10.55057/jdpd.2022.4.3.28
Az infodémia és az írástudások
Koltay, T. (2022) · Performa · doi:10.24361/performa.2022.13.7
Kholis, N., Suprianto, B., & Munoto, M. (2022) · doi:10.2991/978-94-6463-100-5_4
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Sari, M. E., Wahidin, W., Mikaresti, P., Ardiansyah, N. W., Kartiwi, A. P., Mesterjon, M., Susanto, E., Saputra, C. E., Fahmi, M., Utama, N. V., et al. (2025) · Edum Journal · doi:10.31943/edumjournal.v7i2.249
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Chang, M., Wang, Y., Wang, H. W., Zhou, Y., Bulling, A., & Xiong, C. (2025) · doi:10.31219/osf.io/hj96a_v1
Chang, H.-Y., Chang, Y.-J., & Tsai, M.-J. (2024) · Figshare · doi:10.6084/m9.figshare.26680822
Gohel, J., Patel, B., & Prajapati, V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-27160-0_8
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Rudenko, Y., Півторайко, В. В., Антонченко, М. (., Sierykh, L., Saenko, O., & Dehtiarova, N. (2026) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro70003.2026.11591565
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- Authors
- Donohoe, D., & Costello, E.
- Affiliations
- Dublin City University (IE)
- ORCID
- Costello, Eamon
- Venue
- International Journal of Emerging Technologies in Learning (iJET)
- Published
- 2020-09-11
- Volume
- 15
- Issue
- 17
- Pages
- 115
- ISSN
- 1863-0383 (ISSN-L)
- OpenAlex
- W3085184300
- Cited by
- 38
- References
- 44
- Citations by year
- 2026: 4 · 2025: 12 · 2024: 8 · 2023: 4 · 2022: 7 · 2021: 3
- FWCI
- 1.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Emerging Technologies in Learning (iJET) (publishedVersion, cc-by)
- Dublin City University Open Access Institutional Repository (Dublin City University) (submittedVersion, cc-by-nc-nd)
- Arrow@dit (Dublin Institute of Technology) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Social Sciences › Library and Information Sciences › Library Science and Information Literacy
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Literacy, Media, and Education
- Keywords
- learning analytics dashboards, higher education, cognitive biases, overconfidence, perceived usefulness, information visualization, assessment instruments, decision-making
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