Elements of computer modelling: 12 studies for beginners. Study 1: Models and modelling

Illia O. Teplytskyi; Serhiy O. Semerikov

doi:10.55056/seq.963

Authors

Illia O. Teplytskyi Kryvyi Rih State Pedagogical University Author https://orcid.org/0000-0002-9416-3630
Serhiy O. Semerikov Kryvyi Rih State Pedagogical University Translator https://orcid.org/0000-0003-0789-0272

DOI:

https://doi.org/10.55056/seq.963

Keywords:

computer modelling, mathematical models, modelling theory, model classification, information models, educational technology, interdisciplinary education, scientific literacy, knowledge representation, computational thinking

Abstract

This paper presents the foundational concepts of computer modelling as both a cognitive method and educational approach, drawing from the pedagogical principles developed over decades of teaching practice. We examine the theoretical underpinnings of modelling as a scientific method, beginning with a conceptual framework for understanding models as simplified representations of reality that maintain essential characteristics of original phenomena. The paper provides a systematic classification of models (material vs. ideal, physical vs. analogue, intuitive vs. information-based) with illustrative examples demonstrating their application across disciplines. We establish mathematical modelling as the bridge to computer modelling and argue for its introduction in educational contexts through accessible tools like spreadsheets. The pedagogical approach advocated progresses from intuitive examples to structured model development, emphasising the iterative cycle of model creation, verification, experimentation, and refinement. This framework supports the development of interdisciplinary thinking and modern scientific literacy, positioning computer modelling as a key component in contemporary education. The principles presented form the foundation for a broader pedagogical system aimed at developing students' modelling competencies through progressively complex studies across various knowledge domains.

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