DEVELOPING DATA ANALYSIS CAPACITY FOR STUDENTS IN TEACHING STATISTICS 12 BASED ON THE PPDAC MODEL
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DOI:
https://doi.org/10.34238/tnu-jst.14565Abstract
In the context of the 2018 General Education Program emphasizing the development of students' competencies, this study examines the application of the Problem-Plan-Data-Analysis-Conclusion (PPDAC) model in teaching the Statistics topic in grade 12 to develop data analysis skills. The research was conducted using a pedagogical experimental method, with learning activities organized according to the five-step PPDAC process, ensuring students fully participate in the stages of problem formulation, data collection, processing, analysis, and drawing conclusions from data. Research data was collected from students' learning outcomes and competency manifestations during the learning process. The results show significant progress in students' ability to formulate statistical questions, organize data, and interpret results. Therefore, the study affirms that PPDAC is a suitable approach that contributes to improving the effectiveness of teaching Statistics in a competency-oriented manner.Downloads
References
National Assembly of Viet Nam, Law on Education (Law No. 43/2019/QH14). National Political Publishing House, 2019.
Government of Viet Nam, Resolution No. 71/NQ-CP on the Strategy for the Development of Vietnamese Education in the period 2025–2035, with a vision to 2050, 2025.
D. Ben-Zvi and J. Garfield, “Statistical literacy, reasoning, and thinking: Goals, definitions, and challenges,” Journal of Statistics Education, vol. 28, no. 1, pp. 1–10, 2020, doi: 10.1080/10691898.2020.1713935.
American Statistical Association, Pre-K–12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II): A framework for statistics and data science education, 2020.
J. Friedrich, R. Peck, and R. Gould, “Data literacy and statistics in K-12 mathematics curricula: A systematic review,” Educational Studies in Mathematics, vol. 112, no. 2, pp. 233–255, 2023, doi: 10.1007/s10649-022-10175-6.
A. Friedrich, T. Fuhr, F. Vogel, S. Schukajlow, and R. Brünken, “What shapes statistical and data literacy research in K-12 STEM education? A systematic review,” International Journal of STEM Education, vol. 11, 2024, Art. no. 72, doi: 10.1186/s40594-024-00559-7.
Ministry of Education and Training (MOET), General education curriculum for mathematics (Issued with Circular No. 32/2018/TT-BGDĐT), 2018.
A. Q. Nguyen, “Teaching conditional probability in Grade 12 under the 2018 General Education Mathematics Curriculum,” Vietnam Journal of Education Sciences, vol. 18, no. 11, pp. 40–46, 2022.
J. Ridgway, J. Nicholson, and S. McCusker, “Strengthening data literacy in K-12 education: Implications for curriculum and pedagogy,” International Journal of Mathematical Education in Science and Technology, vol. 56, no. 1, pp. 45–63, 2025, doi: 10.1080/0020739X.2024.2298451.
C. J. Wild and M. Pfannkuch, “Statistical thinking in empirical enquiry,” International Statistical Review, vol. 67, no. 3, pp. 223–248, 1999, doi: 10.1111/j.1751-5823.1999.tb00442.x.
J. M. Watson, “Developing statistical literacy using investigative cycles,” Statistics Education Research Journal, vol. 20, no. 2, pp. 1–17, 2021.
P. Arnold, A. Leung, and S. Tan, “Using technology-supported data investigations to enhance students’ statistical reasoning,” Computers & Education, vol. 179, 2022, Art. no. 104405, doi: 10.1016/j.compedu.2021.104405.
P. Schreiter, A. Friedrich, T. Fuhr, S. Malone, R. Brünken, J.-T. Kuhn, and F. Vogel, “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, no. 56, pp. 31–45, 2024, doi: 10.1007/s11858-023-01531-1.
S. Budgett, “Teaching statistical investigation: The PPDAC cycle,” Teaching Statistics, vol. 40, no. 1, pp. 5–11, 2018, doi: 10.1111/test.12149.
OECD, PISA 2018 assessment and analytical framework: Mathematics, reading, science, and financial literacy. OECD Publishing, 2019, doi: 10.1787/b25efab8-en.
S. Kuntze, E. Aizikovitsh-Udi, and D. Clarke, “Hybrid task design: Connecting learning opportunities related to critical thinking and statistical thinking,” ZDM–Mathematics Education, no. 49, pp. 923–935, 2017, doi: 10.1007/s11858-017-0874-4.
