Senior High School Students’ Statistical Discourse in Interpreting JAMOVI-Generated Outputs: A Commognitive Case Study
Authors
North Eastern Mindanao State University, Philippines (Philippines)
North Eastern Mindanao State University, Philippines (Philippines)
Article Information
DOI: 10.51244/IJRSI.2026.1307000012
Subject Category: Education
Volume/Issue: 13/7 | Page No: 172-188
Publication Timeline
Submitted: 2026-07-03
Accepted: 2026-07-08
Published: 2026-07-21
Abstract
Statistical software can broaden students’ access to data analysis while also shaping what they notice, say, and treat as evidence. This qualitative case study examined how Senior High School students constructed statistical meaning while interpreting outputs generated in JAMOVI. Guided by Sfard’s commognitive theory, the analysis focused on four features of discourse: word use, visual mediators, narratives, and routines. Three purposively selected student groups completed four structured tasks involving descriptive statistics, histograms and boxplots, correlation, and a paired-samples t test. The data corpus comprised completed worksheets, audio-recorded group discussions, and the corresponding JAMOVI outputs. Data were coded iteratively in NVivo and analyzed through commognitive discourse analysis. The students used increasingly formal terms—including mean, standard deviation, correlation coefficient, and p-value—but their explanations remained predominantly procedural. Across tasks, JAMOVI outputs frequently operated as epistemic anchors: students read values directly from tables or graphs, named the statistic, and moved quickly to a conclusion. Visual mediators supported participation but also encouraged surface-level interpretations based on salience, shape, or threshold rules. Four recurring explanatory patterns were identified: procedural repetition, definition-by-repetition, negotiated meaning, and value-based interpretation. Reasoning routines were stable, typically following a read–name–interpret–conclude sequence. The findings suggest an emerging but incomplete shift from ritualized toward exploratory statistical discourse. Because the study involved three groups in one bounded context and did not compare JAMOVI with another platform, the findings are analytically transferable rather than statistically generalizable and cannot isolate software effects from conceptual difficulty. The study highlights the need for teacher-led prompts that position software outputs as objects for explanation, comparison, and critique rather than as final answers.
Keywords
commognition, JAMOVI, statistical discourse, statistical reasoning, technology-mediated learning
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References
1. Albano, G., Pierri, A., & Sabena, C. (2023). Grasping criteria for success: Engaging undergraduate students in formative feedback by means of digital peer workshops. Teaching Mathematics and Its Applications, 42(2), 187–203. https://academic.oup.com/teamat/article-abstract/42/2/187/6649845 [Google Scholar] [Crossref]
2. Biehler, R., Ben-Zvi, D., & Bakker, A. (2021). Technology-enhanced statistics education: Coordinating multiple representations in dynamic learning environments. Statistics Education Research Journal, 20(1), 1–18.* [Google Scholar] [Crossref]
3. Borba, M. C., Engelbrecht, J., Llinares, S., & Aguilar, M. (2022). Blended learning, online learning, and digital technologies in mathematics education. ZDM–Mathematics Education, 54(5), 1061–1074. https://doi.org/10.1007/s11858-022-01363-3 [Google Scholar] [Crossref]
4. Dindyal, J., & Beswick, K. (2021). The role of classroom discourse in supporting mathematical reasoning. Mathematics Education Research Journal, 33(3), 409–427. [Google Scholar] [Crossref]
5. Drijvers, P., Thurm, D., Vandervieren, E., Klinger, M., Moons, F., & Trouche, L. (2021). Digital tools in mathematics education: A meta-analysis of orchestration and discourse. Educational Studies in Mathematics, 106(3), 381–402.* [Google Scholar] [Crossref]
6. Engel, J. (2021). Students’ reasoning with dynamic data visualizations. Statistics Education Research Journal, 20(3), 45–63.* [Google Scholar] [Crossref]
7. Engel, J., & Ridgway, J. (2023). Data dashboards and statistical literacy: Discursive practices in digital data environments. Statistics Education Research Journal, 22(2), 1–20.* [Google Scholar] [Crossref]
8. Garfield, J., & Ben-Zvi, D. (2021). Supporting statistical reasoning in the era of big data and technology. Statistics Education Research Journal, 20(2), 1–18. [Google Scholar] [Crossref]
9. Greefrath, G., Li, S., Radmehr, F., & Swidan, O. (2025). Theorizing digital curriculum resources: Recent cases from research in mathematics education. In Proceedings of the Fifth International Conference on Mathematics Education. https://hal.science/hal-05411587/document [Google Scholar] [Crossref]
10. Groth, R. E., & Bergner, J. A. (2021). High school students’ reasoning about variability and distribution. Journal of Statistics and Data Science Education, 29(3), 205–218. https://doi.org/10.1080/26939169.2021.1927803 [Google Scholar] [Crossref]
11. Herbel-Eisenmann, B., Wagner, D., Johnson, K., Suh, H., & Figueras, H. (2022). Positioning and authority in mathematics classrooms: Discursive practices and participation. Journal for Research in Mathematics Education, 53(4), 256–284.* [Google Scholar] [Crossref]
12. Kinnear, G., Iannone, P., & Davies, B. (2025). Student approaches to generating mathematical examples: Comparing e-assessment and paper-based tasks. Educational Studies in Mathematics. https://link.springer.com/article/10.1007/s10649-024-10361-1 [Google Scholar] [Crossref]
13. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press. [Google Scholar] [Crossref]
14. Leavy, A. M., Hourigan, M., & Carroll, C. (2022). Adolescents’ reasoning about comparing distributions: Challenges and opportunities. Educational Studies in Mathematics, 110(2), 243–262. https://doi.org/10.1007/s10649-021-10124-7 [Google Scholar] [Crossref]
15. Listiani, T., Wustqa, D. U., Hernawati, K., & Setyaningrum, W. (2026). Mapping the research landscape of statistical reasoning in education using PRISMA: A bibliometric analysis. International Research Journal of Mathematics Studies. https://www.irjms.com/wp-content/uploads/2026/01/Manuscript_IRJMS_08191_WS.pdf [Google Scholar] [Crossref]
16. Macchioni, E. (2025). Capturing struggling students’ mathematics difficulties holistically: Towards a characterization of mathematics discourse participation profiles (Doctoral dissertation). https://tesidottorato.depositolegale.it/handle/20.500.14242/216259 [Google Scholar] [Crossref]
17. Planas, N., & Morgan, C. (2021). Classroom discourse in multilingual mathematics classrooms. Educational Studies in Mathematics, 108(1–2), 7–24.* [Google Scholar] [Crossref]
18. Prediger, S., Bikner-Ahsbahs, A., & Arzarello, F. (2023). Dialogic teaching and conceptual discourse in mathematics classrooms. ZDM–Mathematics Education, 55(2), 315–330.* [Google Scholar] [Crossref]
19. Rabardel, P. (2002). People and technology: A cognitive approach to contemporary instruments. Université Paris 8. [Google Scholar] [Crossref]
20. Ridgway, J. (2023). Statistical literacy and reasoning in secondary education: Challenges in modern data contexts. Statistics Education Research Journal, 22(1), 45–62.* [Google Scholar] [Crossref]
21. Sfard, A. (2008). Thinking as communicating: Human development, the growth of discourses, and mathematizing. Cambridge University Press. [Google Scholar] [Crossref]
22. Tabach, M., & Nachlieli, T. (2020). Co-constructing mathematical narratives in classroom discourse: A commognitive perspective. Educational Studies in Mathematics, 105(3), 321–338.* [Google Scholar] [Crossref]
23. Viirman, O. (2022). Mathematical discourse and visual mediators in university mathematics. Educational Studies in Mathematics, 110(1), 1–20.* [Google Scholar] [Crossref]
24. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. [Google Scholar] [Crossref]
25. Wei, H., Bos, R., & Drijvers, P. (2026). An embodied approach to covariational reasoning: A hand-tracking study. Educational Studies in Mathematics. https://link.springer.com/article/10.1007/s10649-025-10435-8 [Google Scholar] [Crossref]
26. 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 [Google Scholar] [Crossref]
27. Zieffler, A., Garfield, J., & Fry, E. (2020). Students’ reasoning about variability in simulation-based learning environments. Journal of Statistics Education, 28(2), 123–135. https://doi.org/10.1080/10691898.2020.1730722 [Google Scholar] [Crossref]
28. Borba, M. C., Engelbrecht, J., Llinares, S., & Aguilar, M. (2022). Blended learning, online learning, and digital technologies in mathematics education. ZDM–Mathematics Education, 54(5), 1061–1074. https://doi.org/10.1007/s11858-022-01363-3 [Google Scholar] [Crossref]
29. Drijvers, P., Thurm, D., Vandervieren, E., Klinger, M., Moons, F., & Trouche, L. (2021). Digital tools in mathematics education: A meta-analysis of orchestration and discourse. Educational Studies in Mathematics, 106(3), 381–402. https://doi.org/10.1007/s10649-020-10011-8 [Google Scholar] [Crossref]
30. Engel, J., & Ridgway, J. (2023). Statistical literacy in the age of data visualization. Statistics Education Research Journal, 22(1), 45–62. [Google Scholar] [Crossref]
31. Garfield, J., & Ben-Zvi, D. (2021). Supporting statistical reasoning in the era of big data and technology. Statistics Education Research Journal, 20(2), 1–18. [Google Scholar] [Crossref]
32. Greefrath, G., Li, S., Radmehr, F., & Swidan, O. (2025). Theorizing digital curriculum resources: Recent cases from research in mathematics education. In Proceedings of the Fifth International Conference on Mathematics Education. [Google Scholar] [Crossref]
33. Groth, R. E., & Bergner, J. A. (2021). High school students’ reasoning about variability and distribution. Journal of Statistics and Data Science Education, 29(3), 205–218. https://doi.org/10.1080/26939169.2021.1927803 [Google Scholar] [Crossref]
34. Herbel-Eisenmann, B., Wagner, D., Johnson, K., Suh, H., & Figueras, H. (2022). Positioning and authority in mathematics classrooms: Discursive practices and participation. Journal for Research in Mathematics Education, 53(4), 256–284. [Google Scholar] [Crossref]
35. Kinnear, G., Iannone, P., & Davies, B. (2025). Student approaches to generating mathematical examples: Comparing e-assessment and paper-based tasks. Educational Studies in Mathematics. Advance online publication. https://doi.org/10.1007/s10649-024-10361-1 [Google Scholar] [Crossref]
36. Leavy, A. M., Hourigan, M., & Carroll, C. (2022). Adolescents’ reasoning about comparing distributions: Challenges and opportunities. Educational Studies in Mathematics, 110(2), 243–262. https://doi.org/10.1007/s10649-021-10124-7 [Google Scholar] [Crossref]
37. Macchioni, E. (2025). Capturing struggling students’ mathematics difficulties holistically: Towards a characterization of mathematics discourse participation profiles (Doctoral dissertation). [Google Scholar] [Crossref]
38. Sfard, A. (2008). Thinking as communicating: Human development, the growth of discourses, and mathematizing. Cambridge University Press. [Google Scholar] [Crossref]
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