Enhancing data literacy through (virtual) reality: A pedagogical intervention research on the sub-skill of data collection

Authors

  • Verena Witte Institute of Geoinformatics, University of Münster
  • Angela Schwering Institute of Geoinformatics, University of Münster
  • Daniel Frischemeier Institut für grundlegende und inklusive mathematische Bildung (GIMB), University of Münster

DOI:

https://doi.org/10.52041/serj.v24i2.861

Keywords:

Data Literacy, Citizen Science, Data Collection, Virtual Reality, K-12 Education

Abstract

Proficient handling of data is a skill gaining importance with the increasing amount and availability of data. Therefore, promoting data literacy should begin in everyday school life. The foundation for this is formed by competence models for data literacy, which include the sub-skill of data collection that has so far been inadequately considered in K–12 education. This study investigates the relevance of data collection through pedagogical intervention research with learners aged 14 to 17 years. The evaluation highlights the benefits of personal data collection—data collection in one’s own environment and in virtual reality—as part of a holistic approach to teaching data literacy. This work extends existing approaches regarding the importance of data collected by students for their own use through sensors for a reflective and critical understanding of data.

Author Biographies

Verena Witte, Institute of Geoinformatics, University of Münster

Verena Witte is a research associate and PhD candidate at the Institute for Geoinformatics, University of Münster. She received her bachelor’s and master’s degree in mathematics and geography for secondary education from University of Münster in 2021.
She leads the education department at the start-up re:edu. In this role and through her research, she aims to develop future-oriented STEM education. Her research in the field of data literacy among learners provides the theoretical foundation and serves as a cornerstone for project-oriented teaching within the framework of data-driven decision-making, as conceptualized and tested in the DBU-funded project ‘On the road - Adolescents develop concepts for cycling in their own city using digital geomedia.’

Angela Schwering, Institute of Geoinformatics, University of Münster

Angela Schwering is a distinguished professor of Geoinformatics at the University of Münster. She holds both a Bachelor’s and Master’s degree in Information Systems from the University of Münster, where she also earned her PhD in Geoinformatics in 2006. Her research focuses on spatial intelligence and learning, designing systems that enhance spatial orientation skills and wayfinding through innovative methods.

During her doctoral studies, she worked for the British national mapping agency, Ordnance Survey. She completed her post-doctoral research in the Artificial Intelligence Lab at the Institute of Cognitive Science in Osnabrück. In 2008, she accepted a junior professorship at the University of Münster. At the age of 33, she was offered a full professorship, which she accepted at the University of Münster following a counteroffer.

Prof. Schwering is also the founder of openSenseLab gGmbH, a non-profit organization dedicated to promoting geospatial technologies in environmental education. She leads the MExLab ExperiMINTe, an extracurricular learning center for STEM subjects at the University of Münster.

Throughout her career, Prof. Schwering has successfully secured numerous research grants, including an ERC Starting Grant. Her mentorship has guided several postdocs to professorships, and her research initiatives have led to innovative projects in wayfinding, sketch mapping, navigational map reading, and physical computing. Her contributions have been widely recognized, earning her various awards such as the Geospatial World Excellence Award, the Open Data Award, and the CeBit Innovation Award for the senseBox team.

 

References

American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. American Educational Research Association.

Arnold, P., & Franklin, C. (2021). What makes a good statistical question? Journal of Statistics and Data Science Education, 29(1), 122–130. https://doi.org/10.1080/26939169.2021.1877582

Atenas, J., Havemann, L., & Priego, E. (2021). Open data as open educational resources: Towards transversal skills and global citizenship. Open Praxis, 7(4), 377–389. https://doi.org/10.5944/openpraxis.7.4.233

Bargagliotti, A., Franklin, C., Arnold, P., Gould, R., Johnson, S., Perez, L., & Spangler, D. A. (2020). Pre-K–12 guidelines for assessment and instruction in statistics education II (GAISE II). American Statistical Association; National Council of Teachers of Mathematics.

Ben-Zvi, D., & Garfield, J. (2004). Statistical literacy, reasoning, and thinking: Goals, definitions, and challenges. In D. Ben-Zvi & J. Garfield (Eds.), The challenge of developing statistical literacy, reasoning and thinking (pp. 3–15). Springer. https://doi.org/10.1007/1-4020-2278-6_1

Ben-Zvi, D., Gravemeijer, K., & Ainley, J. (2018). Design of statistics learning environments. In D. Ben-Zvi, K. Makar, & J. Garfield (Eds.), International handbook of research in statistics education (pp. 473–502). Springer. https://doi.org/10.1007/978-3-319-66195-7_16

Biehler, R., Frischemeier, D., Gould, R., & Pfannkuch, M. (2023). Impacts of digitalization on content and goals of statistics education. In B. Pepin, G. Gueudet, & J. Choppin (Eds.), Handbook of digital resources in mathematics education (pp. 547–583). Springer. https://doi.org/10.1007/978-3-030-95060-6_20-1

Biehler, R., Frischemeier, D., Podworny, S., Wassong, T., Budde, L., Heinemann, B., & Schulte, C. (2018). Data science and big data in upper secondary schools: A module to build up first components of statistical thinking in a data science curriculum. Archives of Data Science, Series A, 5(1). https://doi.org/10.5445/KSP/1000087327/28

Biocca, F., & Levy, M. R. (Eds.). (1995). Communication in the age of virtual reality. Lawrence Erlbaum Associates.

Burrill, G., & Pfannkuch, M. (2024). Emerging trends in statistics education. ZDM Mathematics Education, 56(1), 19–29. https://doi.org/10.1007/s11858-023-01501-7

Büscher, C. (2022). Design principles for developing statistical literacy in middle schools. Statistics Education Research Journal, 21(1), Article 8. https://doi.org/10.52041/serj.v21i1.80

Carmi, E., Yates, S. J., Lockley, E., & Pawluczuk, A. (2020). Data citizenship: Rethinking data literacy in the age of disinformation, misinformation, and malinformation. Internet Policy Review, 9(2). https://doi.org/10.14763/2020.2.1481

De Luca, V., & Lari, N. (2011). The GRIDC project: Developing students’ thinking skills in a data-rich environment. Journal of Technology Education, 23(1), 5–18. https://doi.org/10.21061/jte.v23i1.a.2

De Oliveira Souza, L., Espasandin Lopes, C., & Fitzallen, N. (2020). Creative insubordination in statistics teaching: Possibilities to go beyond statistical literacy. Statistics Education Research Journal, 19(1), 73–91. https://doi.org/10.52041/serj.v19i1.120

Engel, J. (2017). Statistical literacy for active citizenship: A call for data science education. Statistics Education Research Journal, 16(1), 44–49. https://doi.org/10.52041/serj.v16i1.213

Engel, J., Nicholson, J., & Louie, J. (2022). Preparing for a data-rich world: Civic statistics across the curriculum. In J. Ridgway (Ed.), Statistics for empowerment and social engagement (pp. 445–475). Springer. https://doi.org/10.1007/978-3-031-20748-8_18

Erwin, R. W. (2015). Data literacy: Real-world learning through problem-solving with datasets. American Secondary Education, 43(2), 18–26. http://www.jstor.org/stable/43694208

European Commission. (2024). ESCO. https://esco.ec.europa.eu/en

Fadel, C., Bialik, M., Trilling, B., & Schleicher, A. (2015). Four-dimensional education: The competencies learners need to succeed. Center for Curriculum Redesign.

Fransson, G., Holmberg, J., & Westelius, C. (2020). The challenges of using head mounted virtual reality in K–12 schools from a teacher perspective. Education and Information Technologies, 25(4), 3383–3404. https://doi.org/10.1007/s10639-020-10119-1

Friedrich, A., Schreiter, S., Vogel, M., Becker-Genschow, S., Brünken, R., Kuhn, J., Lehmann, J., & Malone, S. (2024). What shapes statistical and data literacy research in K–12 STEM education? A systematic review of metrics and instructional strategies. International Journal of STEM Education, 11(1), Article 58. https://doi.org/10.1186/s40594-024-00517-z

Frischemeier, D. (2020). Building statisticians at an early age—Statistical projects exploring meaningful data in primary school. Statistics Education Research Journal, 19(1), 39–56. https://doi.org/10.52041/serj.v19i1.118

Gal, I. (2002). Adults’ statistical literacy: Meanings, components, responsibilities. International Statistical Review, 70(1), 1–25. https://doi.org/10.1111/j.1751-5823.2002.tb00336.x

German Informatics Society. (2024, March 25). TrainDL Interactive Policy Monitor. https://traindl-policymonitor.ocg.at/

Gould, R. (2017). Data literacy is statistical literacy. Statistics Education Research Journal, 16(1), 22– 25. https://doi.org/10.52041/serj.v16i1.209

Gould, R. (2021). Toward data-scientific thinking. Teaching Statistics, 43(S1), 11–22. https://doi.org/10.1111/test.12267

Gould, R., Bargagliotti, A., & Johnson, T. (2017). An analysis of secondary teachers reasoning with participatory sensing data. Statistics Education Research Journal, 16(2), 305–334. https://doi.org/10.52041/serj.v16i2.194

Gould, R., Machado, S., Ong, C., Johnson, T., Molyneux, J., Nolen, S., Tangmunarunkit, H., Trusela, L., & Zanontian, L. (2016). Teaching data science to secondary students: The mobilize introduction to data science curriculum. In J. Engel (Ed.), Promoting understanding of statistics about society. Proceedings of the Roundtable Conference of the International Association of Statistics Education (IASE). ISI/IASE. https://iase-web.org/documents/papers/rt2016/Gould.pdf

Hamilton, D., McKechnie, J., Edgerton, E., & Wilson, C. (2021). Immersive virtual reality as a pedagogical tool in education: A systematic literature review of quantitative learning outcomes and experimental design. Journal of Computers in Education, 8(1), 1–32. https://doi.org/10.1007/s40692-020-00169-2

IDSSP Curriculum Team. (2019). Curriculum frameworks for introductory data science. http://idssp.org/files/IDSSP_Frameworks_1.0.pdf

Institute of Geoinformatics. (2024). GeoGami App (Version 5.1.1) [Mobile app]. https://geogami.ifgi.de/app_en.html

Kraft, M. A. (2020). Interpreting effect sizes of education interventions. Educational Researcher, 49(4), 241–253. https://doi.org/10.3102/0013189X20912798

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(S1), 7–35. https://doi.org/10.1007/s13394-023-00449-x

Lee, H., Mojica, G., Thrasher, E., & Baumgartner, P. (2022). Investigating data like a data scientist: Key practices and processes. Statistics Education Research Journal, 21(2), Article 3. https://doi.org/10.52041/serj.v21i2.41

Liu, D., Bhagat, K. K., Gao, Y., Chang, T.-W., & Huang, R. (2017). The potentials and trends of virtual reality in education. In D. Liu, C. Dede, R. Huang, & J. Richards (Eds.), Virtual, augmented, and mixed realities in education (pp. 105–130). Springer. https://doi.org/10.1007/978-981-10-5490-7_7

Maresky, H. S., Oikonomou, A., Ali, I., Ditkofsky, N., Pakkal, M., & Ballyk, B. (2019). Virtual reality and cardiac anatomy: Exploring immersive three-dimensional cardiac imaging, a pilot study in undergraduate medical anatomy education. Clinical Anatomy, 32(2), 238–243. https://doi.org/10.1002/ca.23292

Matovu, H., Ungu, D. A. K., Won, M., Tsai, C.-C., Treagust, D. F., Mocerino, M., & Tasker, R. (2023). Immersive virtual reality for science learning: Design, implementation, and evaluation. Studies in Science Education, 59(2), 205–244. https://doi.org/10.1080/03057267.2022.2082680

Matthews, P. (2016). Data literacy conceptions, community capabilities. The Journal of Community Informatics, 12(3), 47–56. https://doi.org/10.15353/joci.v12i3.3277

McBride, N. (2016). Intervention research. Springer. https://doi.org/10.1007/978-981-10-1011-8

McGrath, J. L., Taekman, J. M., Dev, P., Danforth, D. R., Mohan, D., Kman, N., Crichlow, A., & Bond, W. F. (2018). Using virtual reality simulation Environments to assess competence for emergency medicine learners. Academic Emergency Medicine: Official Journal of the Society for Academic Emergency Medicine, 25(2), 186–195. https://doi.org/10.1111/acem.13308

Melinda, V., & Widjaja, A. E. (2022). Virtual reality applications in education. International Transactions in Educational Technology, 1(1), 68–72. https://doi.org/10.33050/itee.v1i1.194

Miguel-Alonso, I., Checa, D., Guillen-Sanz, H., & Bustillo, A. (2024). Evaluation of the novelty effect in immersive virtual reality learning experiences. Virtual Reality, 28(1), Article 27. https://doi.org/10.1007/s10055-023-00926-5

Ministry for Schools and Education of the State of North Rhine-Westphalia. (2022). Kernlehrplan für die Sekundarstufe 1, Gesamtschule/Sekundarschule in Nordrhein-Westfalen. Mathematik. [Core curriculum for secondary level 1, comprehensive school/secondary school in North Rhine-Westphalia. Mathematics].

Ministry for Schools and Education of the State of North Rhine-Westphalia. (2019). Kernlehrplan für die Sekundarstufe I, Gymnasium in Nordrhein-Westfalen. Erdkunde. [Core curriculum for secondary level 1, comprehensive school/secondary school in North Rhine-Westphalia. Geography].

Ministry for Schools and Education of the State of North Rhine-Westphalia. (2025). School social index. The school-specific social index. https://www.schulministerium.nrw/schulsozialindex.

Papanastasiou, G., Drigas, A., Skianis, C., Lytras, M., & Papanastasiou, E. (2019). Virtual and augmented reality effects on K–12, higher and tertiary education students’ twenty-first century skills. Virtual Reality, 23(4), 425–436. https://doi.org/10.1007/s10055-018-0363-2

Pesch, M., Bartoschek, T., & Schwering, A. (2022). Presenting showcases for “senseBox and openSenseMap” as a learning suite for computer-, data- and scientific literacy. ISLS Annual meeting, Hiroshima, Japan.

Pfeil, M., Bartoschek, T., & Wirwahn, J. A. (2015). Opensensemap—A citizen science platform for publishing and exploring sensor data as open data. Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings, 15(39), 122–139. https://doi.org/10.7275/R56971SW

Podworny, S., Hüsing, S., & Schulte, C. (2022). A place for a data science project in school: Between statistics and epistemic programming. Statistics Education Research Journal, 21(2), Article 6. https://doi.org/10.52041/serj.v21i2.46

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. (2010). Pupils reasoning with information and misinformation. In C. Reading (Ed.), Data and context in statistics education: Towards an evidence-based society. Proceedings of the 8th International Conference on Teaching Statistics. ISI/IASE. https://iase-web.org/documents/papers/icots8/ICOTS8_9A3_RIDGWAY.pdf

Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Bliemel, M., Irvine, D., Kelley, D., Matwin, S., & Wuetherick, B. (2015). Strategies and best practices for data literacy education [Knowledge Synthesis Report]. Dalhousie University.

Robertson, J., & Tisdall, E. K. M. (2020). The importance of consulting children and young people about data literacy. Journal of Media Literacy Education, 12(3), 58–74. https://doi.org/10.23860/JMLE-2020-12-3-6

Rubin, A. (2021). What to consider when we consider data. Teaching Statistics, 43(S1), S23–S33. https://doi.org/10.1111/test.12275

Schield, M. (1999). Statistical literacy: Thinking critically about statistics. Of Significance. 1(1), 15–21.

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(1), 31–45. https://doi.org/10.1007/s11858-023-01531-1

Schüller, K. (2022). Data and AI literacy for everyone. Statistical Journal of the IAOS, 38(2), 477–490. https://doi.org/10.3233/SJI-220941

Serrano-Ausejo, E., & Mårell-Olsson, E. (2024). Opportunities and challenges of using immersive technologies to support students’ spatial ability and 21st-century skills in K–12 education. Education and Information Technologies, 29(5), 5571–5597. https://doi.org/10.1007/s10639-023-11981-5

Shute, V., Rahimi, S., & Emihovich, B. (2017). Assessment for learning in immersive environments. In D. Liu, C. Dede, R. Huang, & J. Richards (Eds.), Virtual, augmented, and mixed realities in education (pp. 71–87). Springer. https://doi.org/10.1007/978-981-10-5490-7_5

Slater, M., & Sanchez-Vives, M. V. (2016). Enhancing our lives with immersive virtual reality. Frontiers in Robotics and AI, 3. https://doi.org/10.3389/frobt.2016.00074

Snee, R. D. (1993). What’s missing in statistical education? The American Statistician, 47(2), 149–154. https://doi.org/10.1080/00031305.1993.10475964

Song, I.-Y., & Zhu, Y. (2016). Big data and data science: What should we teach? Expert Systems, 33(4), 364–373. https://doi.org/10.1111/exsy.12130

Steinmayr, R., & Spinath, B. (2009). The importance of motivation as a predictor of school achievement. Learning and Individual Differences, 19(1), 80–90. https://doi.org/10.1016/j.lindif.2008.05.004

Stranger-Johannessen, E., & Fjørtoft, S. O. (2021). Implementing virtual reality in K–12 classrooms: Lessons learned from early adopters. In V. L. Uskov, R. J. Howlett, & L. C. Jain (Eds.), Smart education and e-Learning 2021 (Vol. 240, pp. 139–148). Springer. https://doi.org/10.1007/978-981-16-2834-4_12

Teixeira, S., Campos, P., & Trostianitser, A. (2022). Data sets: Examples and access for civic statistics. In J. Ridgway (Ed.), Statistics for empowerment and social engagement (pp. 127–151). Springer. https://doi.org/10.1007/978-3-031-20748-8_6

Ucar, S., & Trundle, K. C. (2011). Conducting guided inquiry in science classes using authentic, archived, web-based data. Computers & Education, 57(2), 1571–1582. https://doi.org/10.1016/j.compedu.2011.02.007

Unity Technologies. (2025). Unity Engine [Computer software] (Version 6.0). https://unity.com/products?c=unity+engine

Van Audenhove, L., Vermeire, L., Van Den Broeck, W., & Demeulenaere, A. (2024). Data literacy in the new EU DigComp 2.2 framework how DigComp defines competences on artificial intelligence, internet of things and data. Information and Learning Sciences, 125(5/6), 406–436. https://doi.org/10.1108/ILS-06-2023-0072

Villena-Taranilla, R., Tirado-Olivares, S., Cózar-Gutiérrez, R., & González-Calero, J. A. (2022). Effects of virtual reality on learning outcomes in K-6 education: A meta-analysis. Educational Research Review, 35, Article 100434. https://doi.org/10.1016/j.edurev.2022.100434

Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2—The digital competence framework for citizens: With new examples of knowledge, skills and attitudes (EUR, Issue JRC128415). Publications Office of the European Union. https://doi.org/10.2760/115376

Wild, C. J., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–248. https://doi.org/10.1111/j.1751-5823.1999.tb00442.x

Wisenöcker, A. S., Binder, S., Holzer, M., Valentic, A., Wally, C., & Große, C. S. (2024). Mathematical problems in and out of school: The impact of considering mathematical operations and reality on real-life solutions. European Journal of Psychology of Education, 39(2), 767–783. https://doi.org/10.1007/s10212-023-00718-0

Witte, V., Schwering, A., Bartoschek, T., & Pesch, M. (2023). Zukunftsweisender MINT-Unterricht mit dem senseBox-Ökosystem. Die Plattform für partizipative Data Science mit Physical Computing. [Future-oriented STEM education with the senseBox ecosystem. The platform for participatory data science with physical computing]. MNU-Journal, 76(4), 296–301.

Witte, V., Schwering, A., & Frischemeier, D. (2024). Strengthening data literacy in K-12 education: A scoping review. Education Sciences, 15(1). https://doi.org/10.3390/educsci15010025

Wolff, A., Gooch, D., Cavero Montaner, J. J., Rashid, U., & Kortuem, G. (2016). Creating an understanding of data literacy for a data-driven society. The Journal of Community Informatics, 12(3), 9–26. https://doi.org/10.15353/joci.v12i3.3275

Wu, B., Yu, X., & Gu, X. (2020). Effectiveness of immersive virtual reality using head-mounted displays on learning performance: A meta-analysis. British Journal of Educational Technology, 51(6), 1991–2005. https://doi.org/10.1111/bjet.13023

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Published

2025-11-09

Issue

Vol. 24 No. 2 (2025)

Section

Regular Articles