The role of mathematical representation in enhancing data and statistical literacy: A systematic review
Marthinus Yohanes Ruamba, Yohanes Leonardus Sukestiyarno, Rochmad & Tri Sri Noor Asih
Abstract
This study aims to investigate the role of mathematical representations in enhancing data and statistical literacy among students, with a focus on the integration of interactive data visualization. Using a systematic literature review approach, this study analyzed articles from leading international databases published between 2014 and 2024. The findings highlight that mathematical representations, such as graphs, tables, and diagrams, play a significant role in enhancing students’ understanding and decision-making abilities. Interactive data visualizations not only enhance conceptual understanding but also engage students in exploratory learning processes. A significant contribution of this study lies in the proposal of an easily accessible open-source technology to bridge the gap in schools with limited resources and integrate mathematical representations into real-world contextual applications. Practical implications include teacher training programs for effective visualization design, curriculum development that emphasizes data literacy, and cost-effective adoption of the technology. Future research should evaluate the long-term impact of this strategy, explore inclusive approaches to address individual differences, and assess the scalability of the proposed solution across educational contexts.
Keywords
interactive data visualization, pedagogical strategies, constructivist learning, educational technology integration
Author information & Contribution
Marthinus Yohanes Ruamba. Corresponding author. Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Negeri Semarang, Semarang, Indonesia. Email: marthinusruamba94@students.unnes.ac.id
Yohanes Leonardus Sukestiyarno. Department of Mathematics, Faculty of Mathematics and Natural Science, Semarang State University. Email: sukestiyarno@mail.unnes.ac.id
Rochmad. Department of Mathematics, Faculty of Mathematics and Natural Science, Semarang State University. Email: rochmad@mail.unnes.ac.id
Tri Sri Noor Asih. Department of Mathematics, Faculty of Mathematics and Natural Science, Semarang State University. Email: inung.mat@mail.unnes.ac.id
"All authors equally contributed to the conception, design, preparation, data gathering and analysis, and writing of the manuscript. All authors read and approved the final manuscript."
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was not supported by any funding.
Institutional Review Board Statement
Not Applicable.
AI Declaration
AI tools were not used in writing this paper.
Notes
Acknowledgement
References
Abulela, M. A. A., & Harwell, M. M. (2020). Data analysis: Strengthening inferences in quantitative education studies conducted by novice researchers. Educational Sciences: Theory & Practice, 20(1), 59–78. https://doi.org/10.12738/jestp.2020.1.005
Ainsworth, S. (2014). The principle of multiple representations in multimedia learning. In Cambridge handbook of multimedia learning (p. 464). https://doi.org/10.1017/CBO9781139547369.024
Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. https://doi.org/10.1016/S0079-7421(08)60422-3
Avila-Garzon, C., & Bacca-Acosta, J. (2025). Curriculum, pedagogy, and teaching/learning strategies in data science education. Education Sciences, 15(2), 186. https://doi.org/10.3390/educsci15020186
Bacangallo, L. B., Buella, R. T., Rentasan, K. Y., Pentang, J. T., & Bautista, R. M. (2022). Creative thinking and problem-solving: Can preservice teachers think creatively and solve statistics problems? Studies in Technology and Education, 1(1), 13–27. https://doi.org/10.55687/ste.v1i1.23
Binali, T., Chang, C.-H., & Chang, Y.-J. (2024). High school and college students’ graph-interpretation competence in scientific and daily contexts of data visualization. Science & Education, 33, 763–785. https://doi.org/10.1007/s11191-022-00406-3
Bhattacherjee, A. (2012). Social science research: Principles, methods, and practices. University of South Florida. https://doi.org/10.26192/q7w89
Börner, K., Bueckle, A., & Ginda, M. (2019). Data visualization literacy: Definition, conceptual framework, exercises, and assessment. Proceedings of the National Academy of Sciences, 116(6), 1857–1864. https://doi.org/10.1073/pnas.1807180116
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. http://www.jstor.org/stable/249008
Dito, S. B., & Pujiastuti, H. (2021). The impact of the Industrial Revolution 4.0 on the education sector: A literature review of digital learning in primary and secondary education. Journal of Science and Science Education, 4(2), 59–65. https://doi.org/10.24246/juses.v4i2p59-65
Deming, W. E. (1986). Out of the crisis. MIT Press. https://doi.org/10.7551/mitpress/11457.001.0001
Estrella, S. (2018). Data representation in early statistics: Data understanding, meta-representational competence, and transnumeration. In Statistics in early childhood and elementary education: Supporting early statistical and probabilistic thinking (pp. 239–256). https://doi.org/10.1007/978-981-13-1044-7_14
García-Carmona, A. (2020). From inquiry-based science education to the approach based on scientific practices: A critical analysis and suggestions for science teaching. Science & Education, 29(2), 443–463. https://doi.org/10.1007/s11191-020-00108-8
Goldin, G. A. (2020). Mathematical representation. In Encyclopedia of mathematics education (pp. 566–572). https://doi.org/10.1007/978-3-030-15789-0_103
Herrera, L. M. M., Juárez Ordóñez, S., & Ruiz-Loza, S. (2024). Enhancing mathematical education with spatial visualization tools. Frontiers in Education, 9, Article 1229126. https://doi.org/10.3389/feduc.2024.1229126
İlhan, A. (2021). The impact of game-based, modeling, and collaborative learning methods on the achievements, motivations, and visual mathematical literacy perceptions. SAGE Open, 11(1). https://doi.org/10.1177/21582440211003567
Kandeel, R. A. A. (2019). Students’ academic difficulties in learning a statistics and probability course: The instructors’ view. Journal of Education and Practice, 10(9), 43–52. https://doi.org/10.7176/JEP/10-9-05
Kolb, D. A. (1984). Experiential learning: Experience as a source of learning and development. Prentice Hall. https://www.researchgate.net/publication/235701029
Kraus, S., Breier, M., Lim, W. M., Dabić, M., Kumar, S., Kanbach, D., & Ferreira, J. J. (2022). Literature reviews as independent studies: Guidelines for academic practice. Review of Managerial Science, 16(8), 2577–2595. https://doi.org/10.1007/s11846-022-00588-8
Kurnia, A. B., Lowrie, T., & Patahuddin, S. M. (2023). The development of high school students’ statistical literacy across grade level. Mathematics Education Research Journal, 36(1), 7–35. https://doi.org/10.1007/s13394-023-00449-x
Lave, J., & Wenger, E. (1991). Situational learning: Legitimate peripheral participation. Cambridge University Press. https://doi.org/10.1017/CBO9780511815355
Lukman, L., Wahyudin, W., Suryadi, D., Dasari, D., & Prabawanto, S. (2022). Studying student statistical literacy in statistics lectures on higher education using grounded theory approach. Infinity, 11(1), 163–176. https://doi.org/10.22460/infinity.v11i1.p163-176
Malesevic, M., Jovovic, J., & Banjac, S. (2015). The role of data visualization in education. Procedia – Social and Behavioral Sciences, 186, 130–137. https://doi.org/10.48550/arXiv.1511.07087
Man, Y. L., Asikin, M., & Sugiman. (2022). Systematic literature review: Students’ mathematical representation ability in mathematics learning. Jurnal Didaktik Matematika, 10(1), 1–14. https://doi.org/10.26858/jdm.v10i1.26821
Maryati, T., & Monica, A. (2021). The effect of inquiry-based learning on students’ data literacy. Mosharafa: Journal of Mathematics Education, 17(2), 112–123. https://doi.org/10.31980/mosharafa.v10i2.666
Mayer, J. D., Salovey, P., Caruso, D. R., & Sitarenios, G. (2001). Emotional intelligence as standard intelligence. https://doi.org/10.1037/1528-3542.1.3.232
Miller, A., Thompson, R., & Evans, L. (2022). Interactive data visualization and its impact on student comprehension. Computers & Education, 190, 104607. https://doi.org/10.1016/j.compedu.2022.104607
Miller, K. M. (2022). Developing pedagogical content knowledge for STEM integration through data literacy: A case study of high school science teachers (Doctoral dissertation, University of Pennsylvania). https://repository.upenn.edu/entities/publication/a8e4097b-ca87-48d9-8400-4d16e06a9fb5
Mongeon, P., & Paul-Hus, A. (2016). Coverage of Web of Science and Scopus journals: A comparative analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/s11192-015-1765-5
Muliastrini, N. K. E. (2019). Strengthening new literacy (data, technology, and human resource literacy/humanism) in elementary school teachers in responding to the challenges of the Industrial Revolution 4.0 era. Ganaya: Journal of Social Sciences and Humanities, 2(2–1), 88–102. https://doi.org/10.23887/jisd.v3i3.14116
Muliani, A., Nurjanah, S., & Nurhasanah, S. (2021). The importance of the role of digital literacy for students in the era of the Industrial Revolution 4.0 for Indonesia’s progress. Journal of Education and Technology, 1(2), 87–90. https://jurnalilmiah.org/journal/index.php/jet/article/download/61/58
Overton, M., & Kleinschmit, S. (2022). Data science literacy: Toward a philosophy of accessible and adaptable data science skill development in public administration programs. Teaching Public Administration, 40(3), 354–365. https://doi.org/10.1177/01447394211004990
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., & Moher, D. (2021). PRISMA statement 2020: Updated guidelines for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Piaget, J. (1970). Educational science and child psychology. Orion Press.
Prihastari, D., Sukestiyarno, Y., & Kartono, S. (2022). Evaluation of statistical literacy of high school students in Indonesia. Indonesian Journal of Mathematics Education, 13(2), 89–102. https://doi.org/10.30653/003.202282.250
Rahmania, F., & Nuriadin, I. (2025). Analysis of students’ statistical literacy ability based on learning anxiety: A phenomenological study in high school students. Jurnal Paedagogy, 12(2), 123–135. https://e-journal.undikma.ac.id/index.php/pedagogy/article/view/15210/7084
Ridgway, J. (2016). Implications of the data revolution for statistics education. International Statistical Review, 84(3), 528–549. https://doi.org/10.1111/insr.12110
Ridgway, J., Nicholson, J., & McCusker, S. (2022). Global challenges in teaching data literacy. Educational Studies in Mathematics, 109(4), 365–381. https://doi.org/10.1007/978-94-007-1131-0_30
Sadoski, M., & Paivio, A. (2004). A theoretical model of dual-code reading. In Theoretical models and reading processes (5th ed., pp. 1329–1362). https://doi.org/10.1598/0872075028.47
Schreiter, S., Friedrich, A., Fuhr, H., Malone, S., Brünken, R., Kuhn, J., & Vogel, M. (2024). Teaching for statistical and data literacy in K–12 STEM education: A systematic review on teacher variables, teacher education, and impacts on classroom practice. ZDM – Mathematics Education, 56, 31–45. https://doi.org/10.1007/s11858-023-01531-1
Serianti, P., Yusian, D. R. T. B., & Albar, R. (2024). Improving digital literacy of high school students through information technology utilization training in the Industrial Revolution 4.0 era. Community Service Journal (INOTEC), 6(1), 45–50. https://jurnal.uui.ac.id/index.php/jpkmi/article/view/4119/0
Sierra-Correa, P. C., & Kintz, J. R. C. (2014). Ecosystem-based adaptation for improving coastal planning for sea-level rise: A systematic review for mangrove coasts. Marine Policy, 51, 385–393. https://doi.org/10.1016/j.marpol.2014.09.013
Singhal, A., & Rogers, E. M. (2003). The status of entertainment-education worldwide. In Entertainment-education and social change (pp. 25–42). Routledge. https://doi.org/10.4324/9781410609595
Stovold, E., Beecher, D., Foxlee, R., & Noel-Storr, A. (2014). Study flow diagrams in Cochrane systematic review updates: An adapted PRISMA flow diagram. Systematic Reviews, 3, 54. https://doi.org/10.1186/2046-4053-3-54
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
Takaria, J., & Talakua, S. (2018). The importance of statistical literacy in mathematics education. Indonesian Journal of Mathematics Education, 6(4), 45–53. https://journal.uny.ac.id/index.php/jk/article/view/18768/pdf
Uyen, B. P., Tong, D. H., Loc, N. P., & Thanh, L. N. P. (2021). The effectiveness of applying realistic mathematics education approach in 7th grade statistics learning on students’ mathematics skills. Journal of E-Learning Education and Research, 8(2), 185–197. https://doi.org/10.20448/journal.509.2021.82.185.197
Van Dijk, J. A. G. M. (2020). The digital divide. Polity Press. https://www.wiley.com/en-us/The+Digital+Divide-p-9781509534456
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. https://www.hup.harvard.edu/books/9780674576292
Wulandari, I., & Isnarto, I. (2023). Mathematical representation ability in terms of students’ learning independence in project-based learning model assisted by Google Sites. Unnes Journal of Mathematics Education, 12(3), 225–236. https://doi.org/10.15294/ujme.v12i3.78881
Xiao, Y., & Watson, M. (2019). A guide to conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93–112. https://doi.org/10.1177/0739456X17723971
Xie, J., Gao, R., Nijkamp, E., Zhu, S.-C., & Wu, Y. N. (2020). Representation learning: A statistical perspective. Annual Review of Statistics and Its Application, 7, 179–203. https://doi.org/10.1146/annurev-statistics-031219-041104
Zawacki-Richter, O., Kerres, M., Bedenlier, S., Bond, M., & Buntins, K. (2020). Systematic reviews in educational research: Methodology, perspectives and applications. Springer Nature. https://doi.org/10.1007/978-3-658-27602-7
Cite this article:
Ruamba, M.Y., Sukestiyarno, Y.L., Rochmad & Asih, T.S.N. (2025). The role of mathematical representation in enhancing data and statistical literacy: A systematic review. International Journal of Science, Technology, Engineering and Mathematics, 5(4), 1-20. https://doi.org/10.53378/ijstem.353268
License:
![]()
This work is licensed under a Creative Commons Attribution (CC BY 4.0) International License.
Related articles:
Most read articles
- Social media usage and the academic performance of Filipino junior high school students
- Exploring the factors influencing commuters’ satisfaction and the use of public utility buses in Quezon City, Philippines
- A narrative exploration of romantic experiences and ideal relationship standards among Filipino Gen Z
- Students’ exposure to social media and their radical involvement on the societal issues in the Philippines
- ChatGPT integration significantly boosts personalized learning outcomes: A Philippine study
- Tiktok made me book it: The impact of Tiktok on tourism destination selection of generation Z and millennials in Manila
- Senior high school students’ awareness and literacy on computer software applications
- Self-perception of ABM students towards their academic, social and emotional college preparedness
- Enhancing financial literacy among Pantawid Pamilyang Pilipino Program beneficiaries
- Emotional intelligence and leadership efficacy of university student leaders





