How to measure Mathematical Modelling in science education?
An atomistic approach.
DOI:
https://doi.org/10.25321/prise.2025.1529Abstract
Background: Models and mathematical modelling play pivotal roles in understanding complex scientific phenomena. Their application ranges widely across educational and scientific disciplines, notably in natural sciences and chemistry, facilitating the translation between conceptual understanding and practical applications.
Purpose: This study aims to develop and validate a test instrument for assessing mathematical modeling competencies in the natural sciences, with a particular focus on chemistry and biology, exploring the empirical substantiation of theoretically implied subcategories and their interactions.
Sample/setting: The study involved 390 STEM students from German universities, with a final dataset comprising 309 participants. The testing occurred during the winter semester of 2022/2023.
Design and Methods: Utilizing a quantitative design, the study employed Rasch analyses based on probabilistic test theory to ensure the reliability and objectivity of the test instrument. Items were crafted to reflect various aspects of the mathematical modelling process, categorized into understanding, simplifying, mathematizing, interpreting, validating, and communicating.
Results: Findings suggest that the test instrument effectively measures mathematical modelling competencies across the specified categories. The analysis confirmed the instrument's unidimensionality, reliability, and substantive validity of the mathematical modelling constructs it aims to assess.
Conclusions: The development of this test instrument holds significant potential for educational practice and future research by providing a reliable means to assess and understand students' competencies in mathematical modelling within the natural sciences.
Keywords: Mathematical Modeling, Natural Sciences, Chemistry Education, Test Instrument, Rasch Analysis, Assessment of competencies.
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