A Descriptive-Comparative Analysis of Numeracy Skills Among Junior High School Students
Aila Salinas | Bryan L Cancio
Discipline: Education
Abstract:
Numeracy is a foundational skill essential for academic success and everyday functioning; however, national and global assessments such as PISA 2018 continue to reflect poor mathematics performance among Filipino learners. This study aimed to (1) assess the level of numeracy skills among junior high school students in terms of computational ability and problem-solving skills; (2) determine their motivation and self-confidence in learning numeracy; (3) evaluate the supportiveness of their learning environment; and (4) identify significant differences in numeracy skills when grouped according to sex, grade level, and socio-economic status. Grounded in Bandura’s Self-Efficacy Theory and Vygotsky’s Sociocultural Theory, the research employed a descriptive-comparative design, surveying 50 students from the Lupon East District through stratified random sampling. A validated 4-point Likert-scale instrument assessed computational ability (M = 2.87, SD = 0.61), problem-solving skills (M = 2.35, SD = 0.58), motivation and self-confidence (M = 2.28, SD = 0.62), and learning environment (M = 3.02, SD = 0.55). One-way ANOVA results showed significant differences in numeracy skills by grade level (F(2,297) = 5.41, p < .01) and socio-economic status (F(2,297) = 6.02, p < .01), but not by sex (t(298) = 1.12, p = .26). These findings emphasize the combined impact of internal beliefs and external supports on numeracy development and highlight the need for targeted interventions that address motivation, instructional strategies, and resource disparities.
References:
- American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
- Anderson, J. R. (1983). The architecture of cognition. Harvard University Press.
- Babbie, E. R. (2020). The practice of social research (15th ed.). Boston, MA: Cengage Learning.
- Best, J. W., & Kahn, J. V. (2006). Research in education (10th ed.). Pearson Education.
- Boaler, J. (2016). Mathematical mindsets: Unleashing students’ potential through creative math, inspiring messages and innovative teaching. Jossey-Bass.
- Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Thousand Oaks, CA: Sage.
- Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.
- Geiger, V., Goos, M., & Forgasz, H. (2015). A rich interpretation of numeracy for the 21st century: A survey of the state of the field. ZDM, 47(4), 531–548.
- OECD. (2019). PISA 2018 results (Volume I): What students know and can do. OECD Publishing.
- Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (7th ed.). New York, NY: Routledge.
- Rensis, L. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 1–55.
- Schunk, D. H., & Zimmerman, B. J. (2006). Motivation and self-regulated learning: Theory, research, and applications. Lawrence Erlbaum Associates
- Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453.
- Stratified Random Sample: Definition, Examples. (2024, May 12). Statistics How To. https://www.statisticshowto.com/probability-and-statistics/sampling-in-statistics/stratified-random-sample
- Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.