Measured Without Meaning: Data Subject Consciousness in Technology-Mediated STEM Learning Environments
Journal of Research in Science, Mathematics and Technology Education, Volume 9, Issue SI, June 2026, pp. 95-110
OPEN ACCESS VIEWS: 20 DOWNLOADS: 21 Publication date: 15 Jun 2026
OPEN ACCESS VIEWS: 20 DOWNLOADS: 21 Publication date: 15 Jun 2026
ABSTRACT
Across STEM education, healthcare, and workplace settings, individuals increasingly learn and perform within data-rich environments where measurement systems generate information about their bodies and behaviors that they cannot access or interpret. This study introduces data subject consciousness, the epistemological condition of knowing oneself as a data source while being structurally excluded from interpreting that data, through phenomenological analysis of a paradigmatic case: an elite athlete navigating sports science technology. Through a 60-minute narrative interview with a Brazilian Olympic sprinter (400m) now pursuing doctoral training in Exercise Physiology, and following Moustakas’s (1994) phenomenological methodology, five themes illuminate how learners experience the gap between being measured and making meaning: data opacity, the displacement of embodied knowing by objective measurement, the knowledge mediator, the paradox of wearable technology, and resource constraints limiting interpretive access. The study argues that sport provides an analytically clear case for understanding a broader epistemic condition increasingly visible in STEM learning environments, including science classrooms, learning analytics systems, and AI-mediated instruction, where learners and teachers face structurally similar exclusions. Data subject consciousness offers a diagnostic framework for researchers and designers of educational technology who seek to build systems that support rather than foreclose interpretive participation by learners.
KEYWORDS
data literacy, data subject consciousness, epistemic access, epistemic injustice, learning analytics, STEM education, technology-mediated learning
CITATION (APA)
Jonas, J. (2026). Measured Without Meaning: Data Subject Consciousness in Technology-Mediated STEM Learning Environments. Journal of Research in Science, Mathematics and Technology Education, 9(SI), 95-110. https://doi.org/10.31756/jrsmte.514SI
REFERENCES
- Akenhead, R., & Nassis, G. P. (2016). Training load and player monitoring in high-level football: Current practice and perceptions. International Journal of Sports Physiology and Performance, 11(5), 587-593. https://doi.org/10.1123/ijspp.2015-0331
- Allen-Collinson, J. (2009). Sporting embodiment: Sports studies and the (continuing) promise of phenomenology. Qualitative Research in Sport and Exercise, 1(3), 279-296. https://doi.org/10.1080/19398440903192340
- Baek, Y., Min, E., Yun, S., & Saad, M. (2023). Computer science framework to teach community-based environmental literacy and data literacy to diverse students. Journal of Research in Science Teaching, 60(4), 812-843. https://doi.org/10.1002/tea.21826
- Clandinin, D. J., & Connelly, F. M. (2000). Narrative inquiry: Experience and story in qualitative research. Jossey-Bass.
- Dale, G. A. (1996). Existential phenomenology: Emphasizing the experience of the athlete in sport psychology research. The Sport Psychologist, 10(4), 307-321. https://doi.org/10.1123/tsp.10.4.307
- Dreyfus, H. L. (2002). Intelligence without representation: Merleau-Ponty’s critique of mental representation. Phenomenology and the Cognitive Sciences, 1(4), 367-383. https://doi.org/10.1023/A:1021351606209
- EDUCAUSE. (2022). 2022 EDUCAUSE Horizon Report: Data and analytics edition. EDUCAUSE. https://www.educause.edu/horizon-report-da
- EDUCAUSE. (2024). 2024 EDUCAUSE Horizon Report: Teaching and learning edition. EDUCAUSE. https://www.educause.edu/horizon-report-tl
- EDUCAUSE. (2025). EDUCAUSE Horizon Action Plan: Supporting agency, trust, transparency, and involvement. EDUCAUSE. https://www.educause.edu/horizon-action-plan
- Foucault, M. (1975). Discipline and punish: The birth of the prison. Vintage Books.
- Fricker, M. (2007). Epistemic injustice: Power and the ethics of knowing. Oxford University Press.
- Gould, R. (2017). Data literacy is statistical literacy. Statistics Education Research Journal, 16(1), 22-25. https://doi.org/10.52041/serj.v16i1.209
- Hockey, J., & Allen-Collinson, J. (2007). Grasping the phenomenology of sporting bodies. International Review for the Sociology of Sport, 42(2), 115-131. https://doi.org/10.1177/1012690207078459
- Hooshyar, D., Pedaste, M., Yang, Y., Malva, L., Hwang, G.-J., & Huang, R. (2025). Towards responsible AI for education: Mapping challenges and interventions across transparency, accountability, privacy, safety, and ethics. Computers and Education: Artificial Intelligence, 8, 100318. https://doi.org/10.1016/j.caeai.2025.100318
- Ihde, D. (1990). Technology and the lifeworld: From garden to earth. Indiana University Press.
- Jin, Y., Echeverria, V., Pijeira-Diaz, H. J., Haataja, E., Malmberg, J., & Järvelä, S. (2024a). FATE in MMLA: A student-centred exploration of fairness, accountability, transparency, and ethics in multimodal learning analytics. British Journal of Educational Technology, 55(2), 441-461. https://doi.org/10.1111/bjet.13393
- Jin, Y., Echeverria, V., & Järvelä, S. (2024b). Chatting with a learning analytics dashboard: Understanding student experiences with chatbot-assisted dashboards. Computers & Education, 210, 104953. https://doi.org/10.1016/j.compedu.2023.104953
- Johnston, H., & Jendoubi, H. (2024). Evaluating pedagogical incentives in undergraduate computing: Combining learning analytics with student perspectives. Journal of Learning Analytics, 11(1), 55-72. https://doi.org/10.18608/jla.2024.7996
- Jones, B., & Sheffield, D. (2019). Modern pentathlon: An example of effective athlete monitoring in a multi-disciplinary sport. International Journal of Sports Physiology and Performance, 14(8), 1034-1038. https://doi.org/10.1123/ijspp.2018-0525
- Kaliisa, R., Misiejuk, K., Lopez-Pernas, S., Khalil, M., & Saqr, M. (2023). Have learning analytics dashboards lived up to the hype? A systematic review of impact, limitations, and the road ahead. British Journal of Educational Technology, 54(5), 1184-1207. https://doi.org/10.1111/bjet.13338
- Makar, K., & Rubin, A. (2009). A framework for thinking about informal statistical inference. Statistics Education Research Journal, 8(1), 82-105. https://doi.org/10.52041/serj.v8i1.457
- Mandinach, E. B., & Gummer, E. S. (2016). What does it mean for teachers to be data literate? Teaching and Teacher Education, 60, 366-376. https://doi.org/10.1016/j.tate.2016.07.011
- Mears, S. A., Dickinson, K., Bergin, K., Colley, S., & Nevill, A. M. (2019). Monitoring of sleep and rest-activity in relation to recovery in soccer players. Science and Medicine in Football, 3(1), 10-17. https://doi.org/10.1080/24733938.2018.1508394
- Merleau-Ponty, M. (1962). Phenomenology of perception. Routledge.
- Moustakas, C. (1994). Phenomenological research methods. Sage.
- Ngoon, T. J., McKenzie, G., Martelaro, N., & Hammer, J. (2024). ClassInSight: Exploring design opportunities for classroom analytics to support teacher reflection on instructional discourse in high school science. ACM CHI Conference on Human Factors in Computing Systems, 1-20. https://doi.org/10.1145/3613904.3642154
- Pope, L., Marques, B., & Popescu, A. (2024). An educational tool for learning about social media tracking, profiling, and recommendation. Computers & Education, 211, 104990. https://doi.org/10.1016/j.compedu.2023.104990
- Postma, W. M., & Dees, M. (2024). Data-driven decision making in sport: Lessons from the football pitch. Journal of Applied Sport Psychology. Advance online publication. https://doi.org/10.1080/10413200.2024.2318765
- Prinsloo, P., & Slade, S. (2017). An elephant in the learning analytics room: The obligation to act. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 46-55). Association for Computing Machinery. https://doi.org/10.1145/3027385.3027406
- Robertson, S., Bartlett, J. D., & Gastin, P. B. (2017). Red, amber, or green? Athlete monitoring in team sport. International Journal of Sports Physiology and Performance, 12(Suppl 2), S2-73-S2-79. https://doi.org/10.1123/ijspp.2016-0410
- Roth, W.-M., & Roychoudhury, A. (1993). The development of science process skills in authentic contexts. Journal of Research in Science Teaching, 30(2), 127-152. https://doi.org/10.1002/tea.3660300204
- Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400. https://doi.org/10.1177/0002764213498851
- Tadimalla, S., & Maher, M. L. (2024). AI literacy for all: Adjustable interdisciplinary socio-technical curriculum for teaching AI literacy in higher education. International Journal of Artificial Intelligence in Education, 34(2), 451-479. https://doi.org/10.1007/s40593-023-00357-x
- van Manen, M. (2016). Researching lived experience: Human science for an action sensitive pedagogy (2nd ed.). Routledge.
- Varela, F. J., Thompson, E., & Rosch, E. (1991). The embodied mind: Cognitive science and human experience. MIT Press.
- Vartiainen, H., Tedre, M., & Kahila, J. (2025). Classroom activities and new classroom apps for enhancing children’s understanding of social media mechanisms. Computers & Education, 224, 105149. https://doi.org/10.1016/j.compedu.2024.105149
- Verbeek, P.-P. (2005). What things do: Philosophical reflections on technology, agency, and design. Pennsylvania State University Press.
- Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
- Zhou, Q., Suraworachet, W., & Cukurova, M. (2024). Harnessing transparent learning analytics for individualized support in collaborative learning environments. IEEE Transactions on Learning Technologies, 17, 412-425. https://doi.org/10.1109/TLT.2023.3339217
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