Effectiveness of Vedic Mathematics in Middle School Education: An Experimental Study
DOI:
https://doi.org/10.22159/ijoe.2026v14i3.59020Keywords:
Vedic mathematics, middle school education, computational fluency, mathematics achievement, student attitude, experimental studyAbstract
Mathematical proficiency is crucial for cognitive development and academic success, yet middle school students often struggle with arithmetic fluency, which can lead to anxiety and negative attitudes toward mathematics. Traditional teaching methods, which frequently emphasize procedural algorithms, may inadvertently increase cognitive load and exacerbate these challenges. This experimental study investigated the effectiveness of Vedic Mathematics techniques on academic achievement, computational speed, accuracy, and student attitudes. Using a single-group pre-test and post-test design, 30 middle school students from a government-aided school in Tamil Nadu, India, participated in a six-week intervention. Sessions were held for 45 minutes three times weekly, focusing on four Vedic sutras: Nikhilam Navatashcaramam Dashatah, Urdhva Tiryagbhyam, Ekadhikena Purvena, and Paravartya Yojayet. Data were collected using a Mathematics Achievement Test (α = 0.82), a Speed and Accuracy Worksheet (reliability = 0.80), and a Student Attitude Scale (α = 0.79). Results showed significant improvements across all measures. Mean achievement scores increased from 42.5 (SD = 6.2) to 68.4 (SD = 5.8), with a paired t-test indicating a statistically significant difference, t(29) = 14.62, p < .001, and a very large effect size (Cohen’s d = 2.67). Computational speed and accuracy also improved significantly, with mean gains of 11.3 and accuracy rising from 65% to 87%. Attitude scores increased by a mean of 14.30. These findings suggest that Vedic Mathematics techniques can serve as an effective supplementary instructional strategy, significantly enhancing both performance and positive attitudes toward mathematics in middle school education.
Downloads
References
Anderson, R. C. (1984). Some reflections on the acquisition of knowledge. Educational Researcher, 13(9), 5-10. https://doi.org/10.2307/1174873
ASER Centre. (2022). Annual Status of Education Report (Rural) 2021. Author.
Ashcraft, M. H., & Krause, J. A. (2007). Working memory, math performance, and math anxiety. Psychonomic Bulletin & Review, 14(2), 243-248. https://psycnet.apa.org/doi/10.3758/BF03194059
Ayres, P. (2006). Using subjective measures to detect variations of intrinsic cognitive load within problems. Learning and Instruction, 16(5), 389-400. https://psycnet.apa.org/doi/10.1016/j.learninstruc.2006.09.001
Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
Bharati Krishna Tirthaji. (1965). Vedic Mathematics. Motilal Banarsidass.
Butterworth, B., Varma, S., & Laurillard, D. (2011). Dyscalculia: From brain to education. Science, 332(6033), 1049-1053. https://doi.org/10.1126/science.1201536
Choudhary, R., & Singh, P. (2021). Mental calculation strategies and their impact on mathematics anxiety and confidence among middle school students. Indian Journal of Educational Psychology, 15(2), 112-124.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.
Cooper, G., & Sweller, J. (1987). Effects of schema acquisition and rule automation on mathematical problem-solving transfer. Journal of Educational Psychology, 79(4), 347–362. https://doi.org/10.1037/0022-0663.79.4.347
Das, A., & Acharya, S. (2022). Enhancing mathematical reasoning through structured computation intervention. Journal of Mathematics Education, 15(1), 78-92.
Dowker, A. (2005). Individual differences in arithmetic: Implications for psychology, neuroscience and education. Psychology Press. https://doi.org/10.4324/9780203324899
Evans, J. S. B. T. (2008). Dual-processing accounts of reasoning, judgment, and social cognition [Judgment]. Annual Review of Psychology, 59, 255–278. https://doi.org/10.1146/annurev.psych.59.103006.093629
Gupta, N., & Rao, V. (2018). Cognitive load during mathematical problem-solving: A comparison of conventional and Vedic approaches. Indian Journal of Cognitive Science, 12(3), 245–258.
Hembree, R. (1990). The nature, effects, and relief of mathematics anxiety. Journal for Research in Mathematics Education, 21(1), 33–46. https://doi.org/10.5951/jresematheduc.21.1.0033
Imbo, I., & LeFevre, J.-A. (2010). The role of phonological and visual working memory in complex arithmetic for Chinese- and Canadian-educated adults. Memory and Cognition, 38(2), 176–185. https://doi.org/10.3758/MC.38.2.176
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus & Giroux.
Kilpatrick, J., Swafford, J., & Findell, B. (Eds.). (2001). Adding it up: Helping children learn mathematics. National Academy Press.
Kumar, A. (2021). Reducing mathematics anxiety through Vedic techniques. Educational Studies Quarterly, 9(3), 60–68.
Ma, X., & Kishor, N. (1997). Assessing the relationship between attitude toward mathematics and achievement in mathematics: A meta-analysis. Journal for Research in Mathematics Education, 28(1), 26–47. https://doi.org/10.2307/749662
Nair, S., & Menon, R. (2019). Retention and transfer effects of multiple strategy instruction in arithmetic. Educational Psychology Review, 31(4), 891–908.
National Council of Teachers of Mathematics. (2000). Principles and standards for school mathematics. NCTM.
Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4. https://doi.org/10.1207/S15326985EP3801_1
Patel, K., & Shah, M. (2019). Comparative effectiveness of conventional and non-conventional computational strategies on standardized arithmetic performance. International Journal on Mathematics Education, 14(2), 156–172.
Rahman, M., & Sultana, T. (2021). Mental computation strategies in rural Bangladeshi classrooms: Effects on engagement and learning. Asian Journal of Education, 22(3), 301–318.
Reddy, P. (2017). Enhancing number flexibility through mental computation training. Journal of Numerical Cognition, 3(2), 234–249.
Sharma, R. (2020). Impact of Vedic Mathematics on students’ achievement. Journal of Mathematics Education, 13(2), 45–52.
Singh, A., & Gupta, R. (2018). Procedural fluency and conceptual understanding through pattern-based mathematics instruction. Indian Educational Review, 56(1), 67–82.
Singh, P., & Verma, S. (2019). Vedic Mathematics and mental calculation skills. International Journal of Educational Research, 7(1), 33–40.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312. https://doi.org/10.1016/0959-4752(94)90003-5
Thomas, M. (2020). Working memory enhancement through structured computation training. Cognitive Development, 54, 100–112.
Torbeyns, J., Verschaffel, L., & Ghesquière, P. (2006). The development of children’s adaptive expertise in the number domain 20 to 100. Cognition and Instruction, 24(4), 439–465. https://doi.org/10.1207/s1532690xci2404_2
Williams, K., & Gelles, M. (2017). Vedic Mathematics: Teacher’s manual. Inspiration Books.
Yadav, R. (2021). Speed and accuracy gains through alternative arithmetic methods. Journal of Educational Research and Practice, 11(1), 45–59.
Published
How to Cite
Issue
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.