Teaching Students Interdisciplinary Knowledge through Compilation of Differential Models within the Framework of Course Projects

Asian Journal of University Education, 2023

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DOI 10.24191/ajue.v19i3.24300
Data confidence 70 medium
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Abstract

In the process of educating future teachers of mathematics to build differential models, there are significant difficulties associated with the lack of natural science knowledge among students, the inability to represent the task in the context of interdisciplinary connections and functional relationships.Thus, the need to study and solve these problems, their theoretical and practical significance in the formation of interdisciplinary knowledge among future mathematics teachers determines the relevance of the research topic.The following article proposes an algorithm for the implementation of interdisciplinary learning, the compilation of differential models on the example of the implementation of an integrated course project by studentsfuture teachers of mathematics, chemistry and physics.In 2020, an experimental study was conducted on the basis of two universities of the Republic of Kazakhstan.The study used empirical research methods: written work, questionnaires, conversations with teachers and future teachers.Data analysis revealed that 87% of students and 80% of teachers lacked knowledge and skills to create mathematical models and establish interdisciplinary connections.In addition, in the process of teaching students to compile differential models, there are difficulties in establishing dependencies between interdisciplinary concepts, in compiling and solving differential models.An experimental study showed that higher education teachers face the following learning issues: the development of a new content of mathematical disciplines aimed at creating the ability of future teachers to compose mathematical models, at ameliorating their mathematical thinking, interdisciplinary knowledge; development of a methodology for teaching future teachers of mathematics interdisciplinary knowledge, to compiling differential models.