The article discusses the importance of simulation education, which helps medical students acquire real-world experience and skills necessary for further professional development. The relevance of modern simulation education for the practical training of medical specialists is noted not only in senior, but also in junior courses. Along with traditional simulators for medical practice: phantoms, dummies, anatomical models, simulators, and others, the article pays special attention to computer simulators during practical and laboratory classes. Developed and used in the educational process in both on-line and off-line versions of seven simulator programs for laboratory work of varying complexity from the discipline "Medical and Biological Physics". The article, as an example, presents a simulator program for laboratory work "Study of liquid viscosity". First, students are invited to study the theoretical material on methods of measuring the coefficient of dynamic viscosity of a liquid, as well as to familiarize themselves with the design and principle of operation of the Ostwald viscometer. Next, the simulator implements the necessary tasks in accordance with the order of work specified in the methodical instructions. The simulator program records the results of the experiments in a table and the students take them for further processing. An analysis of the options for forming the results of the experiment using a simulator program versus measuring the values of these results directly on the computer screen was carried out. It is noted that due to the high probability of falsification or use of the results obtained by other students, the formation of the results of the experiment by the program is a more acceptable option. In addition, the result of experimental data processing in this case can be checked by a simulator program and evaluated accordingly. A technique for determining the range of the random number generator is proposed for the purpose of forming individual results of the experiment by a simulator program based on the calculation of the confidence interval. It has been proven that the minimum and maximum values for the random number generator should be chosen from confidence intervals, the probability of which is higher than 0.95.
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Kulyk et al. (2024) studied this question.
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