Innovative Approaches In Nonlinear Pedagogy For Biomathematics Education

Subhash Alha; Sangeeta Choudhary; Lata Chanchlani; Pratibha Manohar; Amit G. Gawde; Prof. Nand Kishor; Eliza B. Ayo

doi:10.29284/rx790a65

Authors

Subhash Alha Assistant Professor, Maulana Azad National Urdu University, Gachibowli, Hyderabad -500032, Telangana, India
Sangeeta Choudhary Associate Professor, Swami Keshvanand Institute of Technology, Management and Gramothan, Jaipur, Rajasthan, India
Lata Chanchlani Assistant Professor, University of Rajasthan, Jaipur, Rajasthan, India
Pratibha Manohar Assistant Professor, SKN College of Agriculture, SKN Agriculture University, Jobner, Jaipur, Rajasthan, India.
Amit G. Gawde Head, Mathematics, Patkar-Varde College, Goregaon West, Mumbai University: Mumbai University
Prof. Nand Kishor Professor, Department of Teacher Education, School of Education
Eliza B. Ayo Head, Computer Education Department, Centro Escolar University

DOI:

https://doi.org/10.29284/rx790a65

Keywords:

Biomathematics education, Nonlinear pedagogy, Mathematical modeling in biology, Complexity-based learning, Quantitative biology education.

Abstract

This research proposes a nonlinear curriculum framework for biomathematics instruction in which learners engage with feedback-controlled biological systems such as population dynamics, epidemiology, and systems biology. Traditional linear approaches to mathematics education emphasize fixed sequences, standardized procedures, and predictable outcomes, which are insufficient for addressing the recursive, adaptive, and multi-scale reasoning required to understand complex biological processes. In contrast, biomathematical learning is inherently iterative, involving cycles of model construction, interpretation, validation, and revision. Such processes demand pedagogical structures that reflect the nonlinear and dynamic nature of biological systems. Grounded in complexity theory, constructivist learning principles, and systems thinking, this study adopts a conceptual-analytical approach to examine nonlinear learning in biomathematics. Learning is conceptualized as an emergent process shaped by feedback loops, contextual interactions, and evolving cognitive structures rather than as a linear progression from instruction to assessment. The proposed framework identifies four interconnected components of nonlinear learning environments: dynamic learner systems, recursive reflection, interconnected knowledge networks, and adaptive understanding. These components interact through continuous, non-sequential learning cycles that closely resemble authentic biological modeling practices. A schematic model illustrates how learners engage in recursive interactions while working with biological systems characterized by feedback, nonlinearity, and emergence. The framework offers practical guidance for curriculum design, teaching, and assessment in biomathematics and quantitative biology programs, contributing to educational innovation and supporting future empirical research in quantitative life sciences education.

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