TEACHING FLOWCHART CONSTRUCTION FOR THE DEVELOPMENT OF ALGORITHMIC THINKING IN FUTURE COMPUTER SCIENCE TEACHERS
DOI:
https://doi.org/10.31110/2616-650X-vol13i8-002Keywords:
algorithmic thinking, flowchart, visual modeling, computational methods, professional training, future computer science teachers, didactic tools, methods of teaching programming, digital environments, vocational educationAbstract
The article presents the results of a study aimed at substantiating the feasibility and effectiveness of using flowcharts in the study of computational methods as a means of developing algorithmic thinking in future computer science teachers. The main focus is placed on the specifics of formulating and solving typical problems from the course on algorithmization and programming (in particular, the bisection method, the chord method, and Newton’s method), which have a clear iterative structure and allow the visualization of the logic of computations through flowcharts. Based on practical testing, it is proven that including the stage of constructing a flowchart before code implementation contributes to a deeper understanding of each step of the computation, the correct determination of iteration termination conditions, code structuring, and the development of analytical skills. A methodological approach was developed and tested to compare alternative algorithms on the same example, enabling students to evaluate the efficiency of the method, the number of iterations required for convergence, sensitivity to initial conditions, as well as implementation complexity and didactic accessibility for learners. The study also summarized a set of methodological solutions for integrating flowcharts into students’ professional training. In particular, a step-by-step structure of tasks, reflective analysis of constructed diagrams, the creation of educational materials based on them, and the modeling of lessons using flowcharts as an explanatory tool were proposed. It is argued that constructing flowcharts performs a dual function – cognitive-visual (as a tool for mastering an algorithm) and didactic (as a means of explanation and analysis). The conclusion emphasizes the necessity of systematically integrating such tasks into the training of future educators as a means of forming algorithmic thinking, pedagogical foresight, and methodological readiness for teaching computer science in general secondary education institutions.
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