SMART-ARAL Program: A Mathematics Intervention for Senior High School Learners

This action research evaluated the effectiveness of the SMART-ARAL Program as a math intervention for Grade 11 non-numerate learners at Luis Y. Ferrer Jr. Senior High School. The objectives were to measure proficiency before and after the intervention, assess improvement, identify challenges, and suggest program improvements. A quasi-experimental pretest–posttest design was used. Twenty-six learners were purposively selected from the TVL and HUMSS strands. The six-week intervention used modules, peer tutoring, guided practice, and weekly tests. Quantitative data were analyzed using means, standard deviations, and paired-sample t-tests. Qualitative data from focus group discussions were analyzed thematically. Results showed mean scores rose from 6.96 (pretest) to 19.85 (posttest). Statistical analysis confirmed a significant increase in the score, with a large effect size (Cohen’s d = 3.13), reflecting improved numeracy. Challenges included time constraints, limited resources, inconsistent attendance, learning gaps, and external barriers. The study concludes that SMART-ARAL is an effective, structured mathematics remediation program. The following recommendations are made: (1) Implement the SMART-ARAL program at all grade levels where students require mathematics remediation; (2) Increase the length and number of intervention sessions to address learning gaps more thoroughly; (3) Provide additional funding for instructional materials and professional development for teachers involved in the program; (4) Integrate interactive and adaptive teaching methods, such as technology-assisted lessons and individualized practice, to cater to diverse learner needs; and (5) Conduct future studies using larger participant groups and track students’ progress over extended periods to strengthen the evidence on program efficacy.

Mathematics Intervention, Learning Recovery, Numeracy Skills

1. Asian Development Bank. (2022). Philippines Education Sector Assessment, Strategy, and Road Map. https://doi.org/10.22617/TCS220285-2 [Google Scholar] [Crossref]

2. Balbuena, L. A., & Doronio, R. G. (2021). The Learning of Students in Mathematics: A Quasi-Experimental Study. Alegria National High School, DepEd Division of Agusan del Sur. [Google Scholar] [Crossref]

3. Braun, V., & Clarke, V. (2006). Using Thematic Analysis in Psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa [Google Scholar] [Crossref]

4. Carranza, D. A., & De Guzman, A. P. B. (2024). Conceptual Understanding Intervention for Learners Struggling in Mathematics. Luciente II National High School & Benigno V. Aldana National High School. [Google Scholar] [Crossref]

5. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd Ed.). Lawrence Erlbaum Associates. [Google Scholar] [Crossref]

6. Congressional Commission on Education (EdCom II). (2023). Philippine Education at a Turning Point: The Edcom II Interim Report. Senate of the Philippines & House of Representatives. https://edcom.ph/reports [Google Scholar] [Crossref]

7. Creswell, J. W. (2018). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (6th ed.). Pearson. [Google Scholar] [Crossref]

8. Department of Education. (2020). Most Essential Learning Competencies (MELCs). https://www.deped.gov.ph/k-to-12-most-essential-learning-competencies/ [Google Scholar] [Crossref]

9. Department of Education. (2023). National Achievement Test (NAT) Results: Summary Report. https://www.deped.gov.ph [Google Scholar] [Crossref]

10. Gregorio, S. O. (2025). Effects of the Three Teaching Strategies on the Performance In Mathematics Of Grade V Pupils of Ampayon Central Elementary School, Butuan City Division. Randwick International of Education and Linguistics Science Journal, 6(3), 1222. https://doi.org/10.47175/rielsj.v6i3.1222 [Google Scholar] [Crossref]

11. Linao, D. R., Jr. (2025). Innovation Strategies in Teaching Mathematics: A Quasi-Experimental Approach. International Journal of Research and Innovation in Social Science, 9(6), 5446–5461. https://doi.org/10.47772/IJRISS.2025.906000415 [Google Scholar] [Crossref]

12. Munda, N. P., Endrinal, J. R. H., & Nequinto, M. C. (2024). Effectiveness of Project COUNTS in Improving Students’ Numeracy Skills. International Journal of Science, Technology, Engineering and Mathematics, 4(1), 22–41. https://doi.org/10.53378/353038 [Google Scholar] [Crossref]

13. Organisation for Economic Co-operation and Development. (2019). PISA 2018 Results: Combined executive summaries. OECD Publishing. https://www.oecd.org/pisa/publications/pisa-2018-results.htm [Google Scholar] [Crossref]

14. Organisation for Economic Co-operation and Development. (2023). PISA 2022 Results: Learning in a digital world. OECD Publishing. https://doi.org/10.1787/1f0b44f5-en [Google Scholar] [Crossref]

15. Raosoft, Inc. (2004). Sample Size Calculator. http://www.raosoft.com/samplesize.html [Google Scholar] [Crossref]

16. Yazon, A. D., Manaig, K. A., Tamban, V. E., & Sapin, S. B. (2023). Effectiveness of Interactive Classroom Tool: A Quasi-Experiment in Mathematics 10 Using Classpoint. Applied Quantitative Analysis, 3(1), 1–15. [Google Scholar] [Crossref]

• Interplay of Students’ Emotional Intelligence and Attitude toward Mathematics on Performance in Grade 10 Algebra
• Numerical Simulation of Fitzhugh-Nagumo Dynamics Using a Finite Difference-Based Method of Lines
• Fixed Point Theorem in Controlled Metric Spaces
• Usage of Moving Average to Heart Rate, Blood Pressure and Blood Sugar
• Exploring Algebraic Topology and Homotopy Theory: Methods, Empirical Data, and Numerical Examples