Enhancing Data Analysis Skills of STEM Students through Targeted Seminar Workshop
Authors
Teacher II, Department of Education (Philippines)
MS Student, Central Mindanao University (Philippines)
Article Information
DOI: 10.47772/IJRISS.2026.100500253
Subject Category: Data Science, Information Science
Volume/Issue: 10/5 | Page No: 3699-3706
Publication Timeline
Submitted: 2026-04-28
Accepted: 2026-05-05
Published: 2026-05-28
Abstract
Given the limited proficiency in data analysis among senior high school students, this study explored the effectiveness of a targeted seminar-workshop in enhancing data analysis skills among 72 Grade 12 STEM students at Dangcagan National High School. Using a one-group quasi-experimental design conducted over a six-week period, participants received intensive instruction on core statistical concepts and hands-on training in using SPSS for data processing and interpretation. Pre-intervention scores indicated moderate proficiency (M = 12.04, SD = 2.18), which significantly improved after the intervention (M = 16.10, SD = 1.95), with a mean gain of 4.06 points (SD = 2.28, p = .001). The computed Cohen’s d = 1.78 indicated a large effect size. Further analysis showed no statistically significant difference in performance between male and female students (p = .20), suggesting equitable learning outcomes. Comparison of immediate (M = 16.10, SD = 1.95) and delayed (M = 16.91, SD = 2.11) post-test scores after six weeks demonstrated strong retention, with a small effect size (Cohen’s d = 0.34). These findings highlight the value of short, focused interventions in strengthening data analysis competencies. To sustain proficiency, the study recommends integrating similar workshops before Capstone Projects, alongside structured instruction, supplementary modules, retrieval-based assessments, and ongoing evaluation.
Keywords
Data Analysis Skills, Targeted Seminar-Workshop, STEM Education
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References
1. Allauigan, M. A., Melad, K., Beltran, O., Jamoral, L., & Bete, A. (2023). Kolb’s experiential learning theory application in home-based laboratory activities of science major students in microbiology. Jurnal Pendidikan Progresif, 13(2), 580–596. https://doi.org/10.23960/jpp.v13.i2.202333 [Google Scholar] [Crossref]
2. Becker, K., & Park, K. (2011). Effects of integrative approaches among science, technology, engineering, and mathematics (STEM) subjects on students’ learning: A preliminary meta-analysis. Journal of STEM Education: Innovations and Research, 12(5–6), 23–37. [Google Scholar] [Crossref]
3. Khalafi, A., Fallah, Z., & Sharif-Nia, H. (2024). The effect of spaced learning on the learning outcome and retention of nurse anesthesia students: A randomized-controlled study. BMC Medical Education, 24, 322. https://doi.org/10.1186/s12909-024-05290-9 [Google Scholar] [Crossref]
4. Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice-Hall. [Google Scholar] [Crossref]
5. Lucas, M., Bem-haja, P., Zhang, Y., Llorente-Cejudo, C., & Palacios-Rodríguez, A. (2025). A comparative analysis of pre-service teachers’ readiness for AI integration. Computers and Education: Artificial Intelligence, 8, 100396. https://doi.org/10.1016/j.caeai.2025.100396 [Google Scholar] [Crossref]
6. Maala, G. L. I. P., Montoya, L. M. D., Pampan, F. N. L., Cahapin, E. L., Anciro, E. C., & Malabag, B. A. (2025). Exploring the impact of AI tools on student learning through text mining. Journal of Educational Technology Systems. Advance online publication. https://doi.org/10.1177/00472395241234567 [Google Scholar] [Crossref]
7. Mokkapati, A., & Mada, P. (2018). Effectiveness of a teacher training workshop: An interventional study. Journal of Clinical and Diagnostic Research, 12(2), 10–13. https://doi.org/10.7860/JCDR/2018/30165.11219 [Google Scholar] [Crossref]
8. Molina, R. (2021). Research competencies of Science, Technology, Engineering, and Mathematics (STEM) students in a state college in Zamboanga City, Philippines. Eurasian Journal of Educational Research, 94, 359–378. https://doi.org/10.14689/ejer.2021.94.16 [Google Scholar] [Crossref]
9. OECD. (2023). PISA 2022 Results (Volume I): The State of Learning and Equity in Education. OECD Publishing. https://doi.org/10.1787/53f2383c-en [Google Scholar] [Crossref]
10. Ow-Yeong, Y. K., Yeter, I. H., & Ali, F. (2023). Learning data science in elementary school mathematics: A comparative curriculum analysis. International Journal of STEM Education, 10(1), 1–22. https://doi.org/10.1186/s40594-023-00397-9 [Google Scholar] [Crossref]
11. Roediger, H. L., & Butler, A. C. (2011). The critical role of retrieval practice in long-term retention. Trends in Cognitive Sciences, 15(1), 20–27. https://doi.org/10.1016/j.tics.2010.09.003 [Google Scholar] [Crossref]
12. Rowland, C. A. (2014). The effect of testing versus restudy on retention: A meta-analytic review of the testing effect. Psychological Bulletin, 140(6), 1432–1463. https://doi.org/10.1037/a0037559 [Google Scholar] [Crossref]
13. Santhosh, M., Farooqi, H., Ammar, M., Siby, N., Bhadra, J., Al-Thani, N. J., Sellami, A., Fatima, N., & Ahmad, Z. (2023). A meta-analysis to gauge the effectiveness of STEM informal project-based learning: Investigating the potential moderator variables. Journal of Science Education and Technology, 32(5), 671–685. https://doi.org/10.1007/s10956-023-10063-y [Google Scholar] [Crossref]
14. Schiepe-Tiska, A., & Schmidtner, S. (2023). Integrating statistical and data literacy into K-12 STEM curricula: A systematic review. ZDM – Mathematics Education, 55, 1025–1039. https://doi.org/10.1007/s11858-023-01531-1 [Google Scholar] [Crossref]
15. Wang, M.-T., & Degol, J. L. (2017). Gender gap in science, technology, engineering, and mathematics (STEM): Current knowledge, implications for practice, policy, and future directions. Educational Psychology Review, 29(1), 119–140. https://doi.org/10.1007/s10648-015-9355-x [Google Scholar] [Crossref]
16. Wrigley-Asante, C., Owusu, A. B., & Oteng-Ababio, M. (2023). Gender disparities in STEM education: Evidence from senior high schools in Ghana. Asia Pacific Education Review. Advance online publication. https://doi.org/10.1007/s43545-023-00608-8 [Google Scholar] [Crossref]
17. Yildirim, I., & Selvi, K. (2016). The effect of STEM education practices on students’ attitudes and achievements: A meta-analysis study. Journal of Technology and Science Education, 13(1), 73–86. https://doi.org/10.3926/jotse.1790 [Google Scholar] [Crossref]
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