Hybrid evolutionary algorithm for effective adaptive teaching of medical students

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Dmytro I. Uhryn
Andrii Y. Masikevych
Oleksii D. Iliuk

Abstract

The article investigates three evolutionary algorithms are analyzed: genetic algorithm (GA), particle swarm algorithm (PSO) and ant colony algorithm (ACO) to assess their ability to adapt curriculum to different characteristics of students, including their level of knowledge, learning style, practical skills and pace of study. The study compares effectiveness for each evolutionary algorithm creating flexible curricula that meet the individual needs of each student. Based on the analysis, the author proposes a hybrid algorithm that combines the advantages of each of the approaches considered. The article discusses the features of the hybrid algorithm, its ability to quickly adapt the learning process, improve individual learning efficiency and improve the quality of medical training. The proposed hybrid approach was tested in simulation conditions, which demonstrated its advantages in ensuring effective personalization of learning, avoiding local minima, and responding flexibly to changes in students' performance.

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Section

Information technologies in socio-economic, organisational and technical systems

Authors

Author Biographies

Dmytro I. Uhryn, Yuriy Fedkovych Chernivtsi National University, 2, Kotsyubynskyy Str, Chernivtsi, 58002, Ukraine

Doctor of Engineering Sciences, Associate professor, Computer Science Department

Scopus Author ID: 57163746300

Andrii Y. Masikevych, Bukovinian State Medical University, 2, Teatral'na Square, Chernivtsi, 58002, Ukraine

Doctor of Engineering Sciences, Associate professor, Department of Hygiene and Ecology

Scopus Author ID: 57214332363

Oleksii D. Iliuk, Temabit, 1В, Pavlo Tychyna Ave, Kyiv, 02000, Ukraine

senior business&system analyst

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