Bridging Mathematics and Agriculture: An Interdisciplinary Approach to Teaching Mathematical Modelling in Kalomo District
Year: 2025
Type: article
Abstract: : Mathematics education in rural Zambia remains disconnected from real-world applications, limiting student engagement and problem-solving skills. This study investigates the integration of mathematical modelling into agricultural education to bridge this gap through an interdisciplinary approach. This study investigates the integration of mathematical modelling into agricultural education in Kalomo District, Zambia, as a means to enhance student engagement and problem-solving skills through an interdisciplinary approach. Despite mathematics being a fundamental discipline, its abstract nature often leads to disengagement among students, particularly in rural areas where education systems face resource constraints. Agriculture, being the primary economic activity in Kalomo District, provides a relevant context for applying mathematical concepts in real-world scenarios. However, traditional teaching methods rarely incorporate interdisciplinary approaches that connect mathematics to practical agricultural applications. This study addresses this gap by embedding mathematical modelling within the agricultural curriculum, enabling students to apply mathematical concepts to optimize crop yields, analyse soil conditions, and address climate variability. A mixed-methods explanatory sequential design was employed, combining pre- and post-test assessments, structured surveys, focus group discussions, classroom observations, and teacher interviews. Paired t-tests revealed a statistically significant improvement in student problem-solving performance (t(119) = 10.37, p < 0.001), with median test scores increasing from 52% to 75% post-intervention. ANOVA results indicated significant variations in post-test performance across different schools (F(2,117) = 4.62, p = 0.012), suggesting that school-specific factors influenced learning outcomes. Regression analysis demonstrated a strong positive correlation (r = 0.68, p < 0.001) between student motivation and performance improvement, confirming that engagement was a key predictor of mathematical proficiency. Qualitative findings from teacher interviews and student focus groups highlighted increased engagement, enhanced critical thinking skills, and greater confidence in applying mathematical concepts to agricultural problems. Teachers also reported a shift in instructional strategies, with interdisciplinary teaching fostering collaborative learning and higher student participation. The study underscores the transformative potential of interdisciplinary education in bridging the gap between theoretical knowledge and real-world applications. Findings suggest that contextualized learning enhances student engagement, strengthens problem-solving competencies, and prepares students for STEM careers in agriculture. The results advocate for scaling interdisciplinary approaches into rural mathematics curricula to improve learning outcomes. Future research should explore long-term impacts, scalability across diverse educational settings, and the role of digital tools in enhancing interdisciplinary teaching. Keywords: Mathematical Modelling, Agricultural Applications, Interdisciplinary Education, STEM Education, Rural Education, Kalomo District. Title: Bridging Mathematics and Agriculture: An Interdisciplinary Approach to Teaching Mathematical Modelling in Kalomo District Author: Kaziya Kadonsi International Journal of Recent Research in Interdisciplinary Sciences (IJRRIS) ISSN 2350-1049 Vol. 12, Issue 1, January 2025 - March 2025 Page No: 26-51 Paper Publications Website: www.paperpublications.org Published Date: 22-March-2025 DOI: https://doi.org/10.5281/zenodo.15068020 Paper Download Link (Source) https://www.paperpublications.org/upload/book/Bridging%20Mathematics%20and%20Agriculture-22032025-1.pdf
Author: Kaziya Kadonsi
Language: English
Subfield: Applied Mathematics
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893110015
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