Email this article Synergizing the GASING Method and Quantum Learning: An Effort to Optimize Elementary School Students' Mathematical Problem-Solving Abilities
The mathematical problem-solving abilities of elementary school students are still relatively low; this is caused by the high cognitive load they have to face when learning new complex materials and the lack of mental readiness to learn. This study aims to evaluate the effectiveness of the synergy between the GASING (Easy, Fun, Enjoyable) method and the Quantum Learning approach in improving elementary school students' mathematical problem-solving abilities. This study used a quantitative, quasi-experimental design (non-equivalent control group design) involving 140 3rd-grade elementary school participants in Singkawang City, with six schools selected through purposive sampling. The research instrument was a validated test item on mathematical problem-solving abilities. Data were obtained through pretest and posttest assessments, then analyzed quantitatively using Paired Sample T-Test and Quade's Rank Analysis of Covariance (ANCOVA) to address non-normally distributed residual data. The results of the study indicate that the experimental group experienced a significant increase in average scores, from 29.14 to 77.21 (p < 0.001). The results of the Quade's Rank ANCOVA test showed a significant difference between the two groups (F = 73.067, p < 0.001), with an effect size of 38.3% (R2 = 0.383) in the large category. Analysis of the learning trajectory using a scatter plot showed that all students experienced a steady increase in their skills, with no extreme outliers. This study concludes that the synergy of the GASING method and the Quantum Learning approach can reduce students' cognitive load through systematic deconstruction of material while creating a learning environment that supports their mental readiness. This framework provides an effective teaching method for educational practitioners to address mastery of classical mathematics competencies.
Keywords: GASING method, quantum learning, problem-solving abilities, mathematical competence, elementary school.
Aiken, L. R. (1985). Three coefficients for analyzing the reliability and validity of ratings. Educational and Psychological Measurement, 45(1), 131–142. http://doi.org/10.1177/0013164485451012
Adu-Gyamfi, K., Chandler, K., & Thompson, A. (2025). Algebra story problem: The nature of students’ obstacles. School Science and Mathematics, 125(2), 140–153. https://doi.org/10.1111/ssm.12659
Andrade, C. (2021). The inconvenient truth about convenience and purposive samples. Indian Journal of Psychological Medicine, 43(1), 86–88. https://doi.org/10.1177/0253717620977000
Axmedova, L. B. (2025). Modern approaches in preparing future primary school teachers: Integration of theory and practice. International Journal of Pedagogics, 5(8), 17–19. https://doi.org/10.37547/ijp/volume05issue08-04
Badan Standar, Kurikulum, dan Asesmen Pendidikan. (2024). Kajian Akademik Kurikulum Merdeka. Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi.
Brown, C. H., Hedeker, D., Gibbons, R. D., Duan, N., Almirall, D., Gallo, C., Burnett-Zeigler, I., Prado, G., Young, S. D., Valido, A., & Wyman, P. A. (2022). Accounting for context in randomized trials after assignment. Prevention Science, 23(8), 1321–1332. https://doi.org/10.1007/s11121-022-01426-9
Callaman, R., & Palompon, D. (2026). Development of three-dimensional resiliency theory in solving mathematical problem. Journal of Education and Learning, 20(1), 608–621. https://doi.org/10.11591/edulearn.v20i1.22738
Chen, Y. Q., & Zhang, S. X. (2025). Intrinsic preservation of plasticity in continual quantum learning. arXiv preprint arXiv: 2511.17228. https://doi.org/10.48550/arxiv.2511.17228
Chiofalo, M. L., Foti, C., Michelini, M., Santi, L., & Stefanel, A. (2022). Games for teaching/learning quantum mechanics: A pilot study with high-school students. Education Sciences, 12(7), 1–34. https://doi.org/10.3390/educsci12070446
Cruz, C. R. O. D. (2025). Estrategias de resolución de problemas matemáticos en estudiantes: una revisión sistemática [Strategies for mathematical problem-solving in students: a systematic review]. REVISTA INVECOM: Estudios Transdisciplinarios en Comunicación y Sociedad, 5(1), 1–10. https://doi.org/10.5281/zenodo.12659918
Csernicsko, I., Kuchinka, K., & Dorovtsi, A. (2025). Analysis of mathematical competence among students of Transcarpathian schools. Herald of Khmelnytskyi National University, 510(4), 153–158. https://doi.org/10.31891/2307-5732-2025-355- 22
Dalughu, D., & Kurniawati, S. (2025). Effectiveness of GASING learning method on improving understanding concept of economics: A systematic literature review. The International Conference on Sustainable Economics Management and Accounting (ICSEMA 2025) Proceedings, (pp. 1830-1846). UPI Press. https://doi.org/10.32424/icsema.1.1.157
Devi, S. B., Jain, P., & Tyagi, G. (2024). Blended-learning-environment for mathematical skill acquisition among higher education learners using principal component analysis and structural equation modelling. Educational Administration: Theory and Practice, 30(5), 5970–5977. https://doi.org/10.53555/kuey.v30i5.3888
Domu, I., & Mangelep, N. O. (2024). Factors that influence students’ ability to solve mathematics story problems. International Journal of Mathematics and Science Education, 1(3), 1–9. https://doi.org/10.62951/ijmse.v1i3.19
Engin, M. Ç., Gençdoğan, B., & Engin, A. O. (2024). A taxonomic approach on learning areas. European Journal of Education and Pedagogy, 5(3), 8–14. https://doi.org/10.24018/ejedu.2024.5.3.583
Fan, C., & Zhang, D. (2017). Rank repeated measures analysis of covariance. Communications in Statistics-Theory and Methods, 46(3), 1158–1183. https://doi.org/10.1080/03610926.2015.1014106
Faulkner, F., Breen, C., Prendergast, M., & Carr, M. (2021). Profiling mathematical procedural and problem-solving skills of undergraduate students following a new mathematics curriculum. International Journal of Mathematical Education in Science and Technology, 54(8), 1–30. https://doi.org/10.1080/0020739X.2021.1953625
George, D., & Mallery, P. (2019). IBM SPSS Statistics 26 step by step: A simple guide and reference. Routledge.
Gopalan, M., Rosinger, K. O., & Ahn, J. B. (2020). Use of quasi-experimental research designs in education research: Growth, promise, and challenges. Review of Research in Education, 44(1), 218–243. https://doi.org/10.3102/0091732X20903302
Habib, M., Amjad, A. I., Aslam, S., Saleem, Z., & Saleem, A. (2024). Navigating math minds: Unveiling the impact of metacognitive strategies on 8th grade problem-solvers abilities. International Electronic Journal of Elementary Education, 17(1), 135–144. https://doi.org/10.26822/iejee.2024.368
Hawthorne, B. S., Vella-Brodrick, D. A., & Hattie, J. (2019). Well-being as a cognitive load reducing agent: A review of the literature. Frontiers in Education, 4(121), 1–11. https://doi.org/10.3389/feduc.2019.00121
Hendriana, H., Charitas, R. P. I., & Hidayat, W. (2019). The innovation of learning trajectory on multiplication operation for rural area students in Indonesia. Journal on Mathematics Education, 10(3), 397–408. https://jme.ejournal.unsri.ac.id/index.php/jme/article/view/3802
Huang, Y. (2018). Influence of instructional design to manage intrinsic cognitive load on learning effectiveness. EURASIA Journal of Mathematics, Science and Technology Education, 14(6), 2653–2668. https://doi.org/10.29333/ejmste/90264
Hurrell, D. (2021). Conceptual knowledge or procedural knowledge or conceptual knowledge and procedural knowledge: Why the conjunction is important to teachers. Australian Journal of Teacher Education, 46(2), 57–71. https://doi.org/10.14221/ajte.2021v46n2.4
Hussein, Y. F. (2022). Conceptual knowledge and its importance in teaching mathematics. Middle Eastern Journal of Research in Education and Social Sciences, 3(1), 50–65. DOI: https://doi.org/ 10.47631/mejress.v3i1.445
Icuspit, M. J. (2025). Uncovering the multifaceted challenges and practical strategies in teaching elementary mathematics. Divine Word International Journal of Management and Humanities, 4(3), 1959–1971. https://doi.org/10.62025/dwijmh.v4i3.173
Jordan, J., Wagner, J., Manthey, D. E., Wolff, M., Santen, S., & Cico, S. J. (2020). Optimizing lectures from a cognitive load perspective. AEM Education and Training, 4(3), 306–312. https://doi.org/10.1002/aet2.10389
Julita, Darhim, & Herman, T. (2019). Capability of mathematical strategic thinking through quantum learning based on creative problem solving. Journal of Physics: Conference Series, 1320(012099), 1–6. https://doi.org/10.1088/1742-6596/1320/1/012099
Kathayat, B. B. (2024). Metacognitive skills in mathematics learning: A systematic review of literature. Journal of Musikot Campus, 2(1), 1–6. https://doi.org/10.3126/jmc.v2i1.70785
Kelly, K. (2022). Contextualizing curriculum for a multi-course classroom: A case study. Curriculum and Teaching, 37(2), 39–53. https://doi.org/10.7459/ct/37.2.04
Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi. (2024). Laporan hasil Asesmen Nasional Berbasis Komputer (ANBK) tahun 2023. https://anbk.kemendikbud.go.id/
Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi. (2025). Rapor Pendidikan 2024: Kota Singkawang. Pusat Standar dan Kebijakan Pendidikan. https://anbk.kemendikbud.go.id/
Kim, H. (2020). Concreteness fading strategy: A promising and sustainable instructional model in mathematics classrooms. Sustainability, 12(6), 1–18. https://doi.org/10.3390/su12062211
Kim, H., & Clasing-Manquian, P. (2023). Quasi-experimental methods: Principles and application in higher education research. In J. Huisman & M. Tight (Eds.), Theory and method in higher education research: Vol. 9 (pp. 43–62). Emerald Publishing Limited. https://doi.org/10.1108/S2056-375220230000009003
Kim, T. K., & Park, J. H. (2019). More about the basic assumptions of t-test: normality and sample size. Korean Journal of Anesthesiology, 72(4), 331–335. https://doi.org/10.4097/kja.d.18.00292
Martín-Requejo, K., González-Andrade, A., Álvarez-Bardon, A., & Santiago-Ramajo, S. (2023). Implicación de las funciones ejecutivas, la inteligencia emocional y los hábitos y técnicas de estudio en la resolución de problemas matemáticos y el cálculo en la escuela primaria [Involvement of executive functions, emotional intelligence, and study habits in mathematical problem-solving and calculation in elementary school]. Revista de Psicodidáctica, 28(2), 145–152. https://doi.org/https://doi.org/10.1016/j.psicod.2023.06.003
Martinez-Padron, O. J. (2021). El afecto en la resolución de problemas de matemática. Revista Caribeña de Investigación Educativa (RECIE), 5(1), 86–100. https://doi.org/10.32541/recie.2021.v5i1.pp86-100
Maslihah, S., Waluya, S. B., Rochmad, & Suyitno, A. (2020). The role of mathematical literacy to improve high order thinking skills. Journal of Physics: Conference Series, 1539(1), 1–6. https://doi.org/10.1088/1742-6596/1539/1/012085
Mayer, R. E. (2024). The past, present, and future of the cognitive theory of multimedia learning. Educational Psychology Review, 36(8), 1–25. https://doi.org/10.1007/s10648-023-09842-1
Memon, M. A., Thurasamy, R., Ting, H., & Cheah, J. H. (2025). Purposive sampling: A review and guidelines for quantitative research. Journal of Applied Structural Equation Modeling, 9(1), 1–23. https://doi.org/10.47263/JASEM.9(1)01
Mukherjee, F. (2025). Exploring teaching methodologies that shape cognitive and psychological approaches to abstract mathematical learning. The Social Science Review, a Multidisciplinary Journal, 3(5), 32–37. https://doi.org/10.70096/tssr.250305007
Nahar, S., Suhendri, Zailani, & Hardivizon. (2022). Improving students’ collaboration thinking skill under the implementation of the quantum teaching model. International Journal of Instruction, 15(3), 451–464. https://doi.org/10.29333/iji.2022.15325a
Ngu, B. H., & Phan, H. P. (2024). Instructional approach and acquisition of mathematical proficiency: Theoretical insights from learning by comparison and cognitive load theory. Asian Journal for Mathematics Education, 3(3), 357–379. https://doi.org/10.1177/27527263241266765
Nnachi, A. B., Arinze, E. D., & Uchechukwu, A. J. (2024). Exploring the frontiers of data analysis: A comprehensive review. INOSR Applied Sciences, 12(1), 62–68. https://doi.org/10.59298/inosras/2024/12.1.62680
OECD. (2023). PISA 2022 Results (Volume I & II) – Country Notes: Indonesia. OECD Publishing. https://www.oecd.org/en/publications/pisa-2022-results-volume-i-and-ii-country-notes_ed6fbcc5-en/indonesia_c2e1ae0e-en.html
Olimova, M. (2025). The role of scaffolding in education: Enhancing learning through structured support. Journal of Software Engineering and Applied Sciences, 1(3), 69–73. https://doi.org/10.5281/zenodo.15176151
Prabawanto, S. (2013). Peningkatan kemampuan pemecahan masalah, komunikasi, dan self-efficacy matematis mahasiswa melalui pembelajaran dengan pendekatan metacognitive scaffolding (Doctoral dissertation, Universitas Pendidikan Indonesia). UPI Repository. https://repository.upi.edu/3641/
Quade, D. (1967). Rank analysis of covariance. Journal of the American Statistical Association, 62(320), 1187–1200. https://doi.org/10.2307/2283769
Rahman, M. M. (2019). 21st century skill “problem solving”: Defining the concept. Asian Journal of Interdisciplinary Research, 2(1), 71–81. https://doi.org/10.34256/ajir1917
Raisinghani, K. A., & Kesur, B. N. (2024). Harnessing suggestopedia: Multisensory approaches for language learning and environmental literacy. Asian Research Journal of Arts & Social Sciences, 22(10), 26–34. https://doi.org/10.9734/arjass/2024/v22i10582
Sardar, P. P. R. (2024). Twenty-first century skills for employability: a critical exploration. Lex Localis-Journal of Local Self-Government, 22(S4), 12–19. https://doi.org/10.52152/64bv6q02
Sharma, L. R., & Jha, S. (2023). Applying major parametric tests using SPSS in research. International Research Journal of MMC, 4(2), 85–97. https://doi.org/10.3126/irjmmc.v4i2.56017
Shimizu, Y. (2022). The content specificity and generality of the relationship between mathematical problem solving and affective factors. Psych, 4(3), 574–588. https://doi.org/10.3390/psych4030044
Spybrook, J., Zhang, Q., Kelcey, B., & Dong, N. (2020). Learning from cluster randomized trials in education: An assessment of the capacity of studies to determine what works, for whom, and under what conditions. Educational Evaluation and Policy Analysis, 42(3), 354–374. https://doi.org/10.3102/0162373720929018
Surya, Y., & Surya, M. A. (2026). Gasing the fastest numeracy transformation ever demonstrated: Fondasi: filosofi, teori dan konteks (Vol. 1). Gasing Academy Press
Sweller, J. (2023). Cognitive load theory. In R. J. Tierney, F. Rizvi, & K. Ercikan. (Eds.), International encyclopedia of education (4th ed., pp. 127–134). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-12-818630-5.14020-5
Wang, H., Cruz, L., & Shank, M. (2020). The affect effect: Integrating student emotions into the design of engineering technology courses with optimization method. IEEE Frontiers in Education Conference (FIE) (pp. 1–8). IEEE. https://doi.org/10.1109/FIE44824.2020.9274279
William, S. K., & Maat, S. M. (2020). Understanding students’ metacognition in mathematics problem solving: A systematic review. International Journal of Academic Research in Progressive Education and Development, 9(3), 115–127. https://doi.org/10.6007/ijarped/v9-i3/7847
Yigiter, M. S. (2023). Does quantum learning model increase academic achievement? A meta-analysis study. Cumhuriyet International Journal of Education, 12(3), 568–582. https://dx.doi.org/10.30703/cije.1216203
Yildiz, B., & Bayram, H. (2025). The effect of the quantum learning on students’ academic achievements in social studies. Kastamonu Education Journal, 33(3), 523–534. https://doi.org/10.24106/kefdergi.1748575
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