Home⇛Psychology and Education: A Multidisciplinary Journal⇛vol. 40 no. 9 (2025)

5I’s Approach in Conceptual Understanding, Interests, and Beliefs in Statistics among Grade XI Students of Milbuk National High School

Stephen Flores Jr. | Reynaldo Dalayap

Discipline: Statistics

 

Abstract:

Understanding Statistics was essential for data-driven decision-making, yet students often struggle with its abstract intended nature, leading to low engagement and misconceptions. This study investigated the impact of the 5I’s Approach – Impress, Identify, Inspire, Inspect, and Invoke – on students conceptual understanding, interest, and belief in Statistics. Rooted in constructivist and student-centered learning paradigms, the 5I’s Approach was designed to make statistical concepts more engaging, meaningful, and relevant to learners. A quasi-experimental design involving control and experimental groups was employed among Grade XI students. Data were collected through pre-test and post-test, attitudinal surveys, and focus group discussions to assess cognitive and affective learning outcomes. Results indicated that the students exposed to 5I’s Approach showed statistically significant improvements in conceptual understanding and reported heightened interest and more positive beliefs toward Statistics. The approach effectively bridged the gap between theoretical knowledge and real-world application, encouraging active learning and critical thinking. The findings highlighted the pedagogical value of the 5I’s framework in reshaping Statistics instruction and enhancing students’ overall learning experience.



References:

  1. Bandura, A. (1997). Self-efficacy: the exercise of control. W.H. Freeman
  2. Biggs, J., & Tang, C. (2011). Teaching for quality learning at University: What the student does (4th Edition). Open University Press.
  3. Bloom, B.S. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. David McKay.
  4. Bruner, J.S. (1960). The process of education. Harvard University Press.
  5. Chick, H. L. & Pierce, R. U (2012). Teaching Statistics at the school level: Beliefs, practices, and issues. In B.K. M. Kendal & T.A.C.C. Biehler (Eds.) Teaching statistical in school Mathematics-Challenges for teaching and teacher education (pp. 1-20). Springer. https://doi.org/10.1007/978-94-007-1131-0_1.
  6. Cobb, G.W. (2007). The introductory Statistics course: A Ptolemaic Curriculum? Technology Innovations in Statistics Education, 1(1). https://doi.org/10.5070/T511000029.
  7. Curcio, F.R. (1987). Comprehension of mathematical relationships expressed in graphs. Journal for Research in Mathematics Education, 18(5), 382-393. https://doi.org/10.2307/749086.
  8. Engel, J. (2017). Statistical literacy for active citizenship: A call for data Science education. Statistics Education Research Journal, 16(1). 44-49.
  9. Ernest, P. (1989). The impact of beliefs on the teaching Mathematics. In P. Ernest (Ed.), Mathematics teaching: The state of the art (pp.249-254). Falmer Press.
  10. Fennema, E., & Sherman, J.A. (1976). Fennema Sherman Mathematics Attitudes Scales: Instruments designed to measure attitudes toward the learning of Mathematics by females and males. Journal for Research in Mathematics Education, 7(5), 324-326.
  11. 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.
  12. Garfield, J., & Ben Zvi, D. (2008). Developing students’ statistical reasoning: Connecting research and teaching practice. Springer.
  13. Garfield, J., & Chance, B. (2000). Assessment in statistics education: Issues and challenges. Mathematical Thinking and Learning, 2(1-2), 99-125. https://doi.org/10.1207/S15327833MTL0202_5
  14. George, D., & Mallery, P. (2019). IBM SPSS Statistics 26 step by step. A simple guide and reference (16th Edition) Routledge. https://doi.org/10.4324/9780429056765
  15. Groth, R.E., & Meletiou-Mavrotheris, M. (2018). Research on statistics teachers’ reasoning about teaching. In D. Ben Zvi, K. Makar, J. Garfield (Eds.), International Handbook of Research in Statistics Education (pp.327-355). Springer. https://doi.org/10.1007/978-3-319-66195-7_10.
  16. Jonassen, D. H. (1991). Objectivism versus constructivism: Do we need a new philosophical paradigm? Educational Technology Research and Development, 39(3), 5-14. https://doi.org/10.1007/BF02296434.
  17. Kilpatrick, J., Swafforf, J., & Findell, B. (Eds.) (2001). Adding it up: Helping children learn mathematics. National Academy Press.
  18. Lovett, M.C., & Greenhouse, J.B. (2000). Applying cognitive theory to statistics instruction. The American Statistician, 54(3), 196-206. https://doi.org/10.1080/00031305.2000.10474545.
  19. Makar, K., Bakker, A., & Ben-Zvi, D. (2011). The reasoning behind informal statistical inference. Mathematical Thinking and Learning, 13(2-3), 152-173. https://doi.org/10.1080/10986065.2011.583375.
  20. Moore, D. S. (1997). New pedagogy and new content: the case of Statistics. International Statistical Review, 65(2), 123-137. https://doi.org/10.1111/j.1751-5823.1997.tb00390.x.
  21. Paparistodemou, E., & Meletiou-Mavrotheris, M. (2008). Developing students’ reasoning about samples and sampling in the context of informal inference. Statistics Education Research Journal, 7(2), 83-106.
  22. Petocz, P., & Reid, A. (2005). Something strange useless: Service students’ conceptions of Statistics. International Journal of Mathematical Education in Science and Technology, 36(7), 789-800. https://doi.org/10.1080/00207390500271143
  23. Phillip, R. A. (2007). Mathematics teachers’ beliefs and affect. In F.K. Lester Jr. (Ed.), Second handbook of research on Mathematics teaching and learning (pp. 257-315). Information Age Publishing.
  24. Pierce, R., Stacey, K., & Barkatsas, A. (2007). A scale for monitoring students’ attitudes to learning Mathematics with technology. Computer & Education, 48(2), 285-300. https://doi.org/10.1016/j.compedu.2005.01.006.
  25. Schau, C. (2003). Students’ attitudes: The other important outcome in statistics education. STATS: The Magazine for Students of Statistics, (37), 2-9.
  26. Schau, C., Stevens, J., Dauphinee, T. L., & Del Vecchio, A. (1995). The development and validation of the Survey of Attitudes Towards Statistics. Educational and Psychological Measurement, 55(5), 868-875. https://doi.org/10.1177/0013164495055005022.
  27. Shaughnessy, J. M. (2007). Research on Statistics learning and reasoning F.K. Lester Jr. (Ed.), Second handbook of research on mathematics teaching and learning (pp. 957-1009). Information Age Publishing.
  28. Smith, G. (1998). Learning Statistics by doing Statistics. Journal of Statistics Education, 6(3). https://doi.org/10.1080/10691898.1998.11910617.
  29. Vygotsky, L. S. (1978). Mind in society: the development of higher psychological processes. Harvard University Press.
  30. Watson, J. M., & Callingham, R. A. (2003). Statistical literacy: A complex hierarchical construct. Statistics Education Research Journal, 2(2), 3-46.

Tools


Copyright © 2025  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic