Training Methodology Research Articles

The lesson as a leading didactic unit for structuring the content of the methodological training of future teachers of biology and the basics of health in the conditions of blended learning

The realities of school practice put forward new requirements for the quality of methodical training of future teachers of biology and the basics of health. A combination of various forms of organization of the educational process, transitional stage of implementation of model training programs, completion of studies according to the State Standard of basic and full general secondary education are the main ones. Updating the content of methodical training, its structuring and design are the important conditions for ensuring high quality.This study aimed to investigate the features of structuring the content of methodical training of future teachers of biology and the basics of health in the conditions of blended learning. Research methods were used: a comprehensive analysis of scientific and methodological sources, an analysis of the school practice of teaching biology and the practice of training future teachers of biology and the basics of health in higher education institutions, synthesis, comparison, abstraction. This structuring is based on the select of the leading didactic unit – the lesson. Analysis of the experience of training future teachers makes it possible to state that the use of blended learning technologies is developing both in the process of professional and methodical training. The main task of the teacher in blended learning is methodical design of the educational component as a result of action and experience gained during its mastery.The select of didactic units of content structuring and further design of the methodical training process are the basic theoretical basis for the development of a modern system of methodical training of future teachers of biology and the basics of health. Methodical skills in designing and conducting lessons are main skills of a teacher. In our opinion, the leading didactic unit of structuring is the lesson as the main form of teaching biology. The peculiarities of structuring the content of laboratory classes on the methodology of teaching biology and human health in the conditions of blended learning were described.

The impact of boxing on the physical health of young athletes at the initial training stage

Modern socio-economic and political problems in Ukraine, associated with martial law, place high demands on the physical health, performance and active physical condition of schoolchildren. Boxing is currently very popular among many youth sports. The purpose of the study is to experimentally test the effect of boxing on the physical health of young athletes at the stage of initial training. Methodology. To solve the problems, the following research methods were used: analysis of scientific and methodological literature; sociological methods (survey, conversations, questionnaires); pedagogical methods (pedagogical observation, pedagogical experiment); physiometric and index methods; methods of mathematical statistics. The scientific novelty of the study lies in the improvement of the methodology for using boxing tools to improve the health level of young athletes at the stage of initial training. Results. The formation of the experimental methodology was based on the following pedagogical provisions: an integrated approach to the development of physical qualities; health-improving orientation of boxing classes, which was ensured by regular physical activity, compliance with general didactic principles of accessibility, gradualism and age-appropriate technical and physical tasks, as well as by conducting the vast majority of classes in the fresh air. Conclusions. The final assessment of the level of physical health of the test subjects in the experimental group corresponds to above the average level of health, while for the young boxers in the control group it corresponds to below the average level of health. This fact indicates the effectiveness of the introduced boxing training methodology at the initial training stage.

A Deep Learning Approach for Ship Detection Using Satellite Imagery

INTRODUCTION: This paper addresses ship detection in satellite imagery through a deep learning approach, vital for maritime applications. Traditional methods face challenges with large datasets, motivating the adoption of deep learning techniques.
 OBJECTIVES: The primary objective is to present an algorithmic methodology for U-Net model training, focusing on achieving accuracy, efficiency, and robust ship detection. Overcoming manual limitations and enhancing real-time monitoring capabilities are key objectives.
 METHOD: The methodology involves dataset collection from Copernicus Open Hub, employing run-length encoding for efficient preprocessing, and utilizing a U-Net model trained on Sentinel-2 images. Data manipulation includes run-length encoding, masking, and balanced dataset preprocessing.
 RESULT: Results demonstrate the proposed deep learning model's effectiveness in handling diverse datasets, ensuring accuracy through U-Net architecture, and addressing imbalances. The algorithmic process showcases proficiency in ship detection.
 CONCLUSION: In conclusion, this paper contributes a comprehensive methodology for ship detection, significantly advancing accuracy, efficiency, and robustness in maritime applications. The U-Net-based model successfully automates ship detection, promising real-time monitoring enhancements and improved maritime security.

How a Canadian federal organization integrated synoptic reporting and quality improvement tools to drive a national learning health system in cancer surgery.

Accurate and complete surgical and pathology reports are the cornerstone of treatment decisions and cancer care excellence. Synoptic reporting is a process for reporting specific data elements in a specific format in surgical and pathology reports. Since 2007, the Canadian Partnership Against Cancer has led the implementation of synoptic reporting mechanisms across multiple cancer disease sites and jurisdictions across Canada. While the implementation of synoptic reporting has been successful, its use to drive improvements in the quality of cancer care delivery has been lacking. Here we describe the 4-year, national multi-jurisdictional quality improvement initiative to catalyse the use synoptic data to drive cancer system improvements. Resources provided to the jurisdictions included operational funding, training in quality improvement methodology, national forums, expert coaches, and ad hoc monitoring and support. The program emphasized foundational concepts including data literacy, audit and feedback reports, communities of practice, and positive deviance methodology.

An Ergonomics Analysis of Archers through Motion Tracking to Prevent Injuries and Improve Performance.

Archery ranks among the sports with a high incidence of upper extremity injuries, particularly affecting the drawing shoulder and elbow, as well as inducing stress on the lower back. This study seeks to bridge the gap by integrating real-time human motion with biomechanical software to enhance the ergonomics of archers. Thirteen participants were involved in four tasks, using different bows with varied draw weights and shooting distances. Through the application of advanced integrative technology, this study highlights the distinct postures adopted by both males and females, which indicate the biomechanical differences between genders. Additionally, an analysis of the correlation between exposed spinal forces and these adopted postures provides insights into injury risk assessment during the key archery movements. The findings of this study have the potential to significantly enhance the application of training methodologies and the design of assistive devices. These improvements are geared towards mitigating injury risks and enhancing the overall performance of archers.

Adversarial robustness improvement for deep neural networks

Deep neural networks (DNNs) are key components for the implementation of autonomy in systems that operate in highly complex and unpredictable environments (self-driving cars, smart traffic systems, smart manufacturing, etc.). It is well known that DNNs are vulnerable to adversarial examples, i.e. minimal and usually imperceptible perturbations, applied to their inputs, leading to false predictions. This threat poses critical challenges, especially when DNNs are deployed in safety or security-critical systems, and renders as urgent the need for defences that can improve the trustworthiness of DNN functions. Adversarial training has proven effective in improving the robustness of DNNs against a wide range of adversarial perturbations. However, a general framework for adversarial defences is needed that will extend beyond a single-dimensional assessment of robustness improvement; it is essential to consider simultaneously several distance metrics and adversarial attack strategies. Using such an approach we report the results from extensive experimentation on adversarial defence methods that could improve DNNs resilience to adversarial threats. We wrap up by introducing a general adversarial training methodology, which, according to our experimental results, opens prospects for an holistic defence against a range of diverse types of adversarial perturbations.

Development of professional competence of future mathematics teachers in the HEI educational process

Relevance. The importance of this study is due to the evolution of modern education towards excellence and the need to constantly monitor the relationship between teacher�s self-education, knowledge culture, professional training and social requirements. For that reason, it is important to use modern approaches and methods to prepare a mathematics teacher. Purpose. The purpose of the study is to describe and diagnose the methodological and technical features of teacher training, to determine the level of competence of mathematics teachers in their work, their ability to use self-education techniques and the main methods of theoretical and methodological preparation for students' educational activities. Methodology. Typical methods used in research on this topic � analysis, classification, reasoning, synthesis and comparison of approaches � make it possible to identify and highlight key areas for improving the professional training of future teacher. Results. The study examines methods for developing students' competence, analyses the literature, determines the components of mathematics teacher competence, highlights the functions and levels of development, provides the structure of the highest levels of teacher professional competence, determines the content of professional competence of a future mathematics teacher, presents models for the development of methodological knowledge about the professional competence of students and improving educational culture, diagnoses problems, related to the professional competence of students. Conclusions. Professional development is an ongoing, cyclical process because professionalism requires continuous enhancement. The advancement of this process largely depends on an environment that encourages self-development. A key objective in the professional training of mathematics teachers is to cultivate professional competence. This competence reflects students' readiness to apply both theoretical and practical knowledge in science and education, as well as their ability to enhance and refine their qualifications. The study indicates that the professional competence of a future teacher encompasses several aspects: motivational, cognitive, operational, creative, and reflective. Keywords: mathematics teacher; educational process; quality of methodological training; methodological knowledge; methodological culture; professional competencies

Modeling prevention behaviors during the COVID-19 pandemic using Bayesian belief networks and protection motivation theory.

Prevention behaviors are important in mitigating the transmission of COVID-19. The protection motivation theory (PMT) links perceptions of risk and coping ability with the act of adopting prevention behaviors. The goal of this research is to test the application of the PMT in predicting adoption of prevention behaviors during the COVID-19 pandemic. Two research objectives are achieved to explore motivating factors for adopting prevention behaviors. (1) The first objective is to identify variables that are strong predictors of prevention behavior adoption. A data-driven approach is used to train Bayesian belief network (BBN) models using results of a survey of participants reporting risk perceptions and prevention behaviors during the COVID-19 pandemic. A large set of models are generated and analyzed to identify significant variables. (2) The second objective is to develop models based on the PMT to predict prevention behaviors. BBN models that predict prevention behaviors were developed using two approaches. In the first approach, a data-driven methodology trains models using survey data alone. In the second approach, expert knowledge is used to develop the structure of the BBN using PMT constructs. Results demonstrate that trust and experience with COVID-19 were important predictors for prevention measure adoption. Models that were developed using the PMT confirm relationships between coping appraisal, threat appraisal, and protective behaviors. Data-driven and PMT-based models perform similarly well, confirming the use of PMT in this context. Predicting adoption of social distancing behaviors provides insight for developing policies duringpandemics.

Deep learning for broadleaf weed seedlings classification incorporating data variability and model flexibility across two contrasting environments

The increasing deployment of deep learning models for distinguishing weeds and crops has witnessed notable strides in agricultural scenarios. However, a conspicuous gap endures in the literature concerning the training and testing of models across disparate environmental conditions. Predominant methodologies either delineate a single dataset distribution into training, validation, and testing subsets or merge datasets from diverse conditions or distributions before their division into the subsets. Our study aims to ameliorate this gap by extending to several broadleaf weed categories across varied distributions, evaluating the impact of training convolutional neural networks on datasets specific to particular conditions or distributions, and assessing their performance in entirely distinct settings through three experiments. By evaluating diverse network architectures and training approaches (finetuning versus feature extraction), testing various architectures, employing different training strategies, and amalgamating data, we devised straightforward guidelines to ensure the model's deployability in contrasting environments with sustained precision and accuracy.In Experiment 1, conducted in a uniform environment, accuracy ranged from 80% to 100% across all models and training strategies, with finetune mode achieving a superior performance of 94% to 99.9% compared to the feature extraction mode at 80% to 92.96%. Experiment 2 underscored a significant performance decline, with accuracy figures between 25% and 60%, primarily at 40%, when the origin of the test data deviated from the train and validation sets. Experiment 3, spotlighting dataset and distribution amalgamation, yielded promising accuracy metrics, notably a peak of 99.6% for ResNet in finetuning mode to a low of 69.9% for InceptionV3 in feature extraction mode. These pivotal findings emphasize that merging data from diverse distributions, coupled with finetuned training on advanced architectures like ResNet and MobileNet, markedly enhances performance, contrasting with the relatively lower performance exhibited by simpler networks like AlexNet. Our results suggest that embracing data diversity and flexible training methodologies are crucial for optimizing weed classification models when disparate data distributions are available. This study gives a practical alternative for treating diverse datasets with real-world agricultural variances.

RESEARCH ON DELIVERING OUTCOME-BASED LEARNING THROUGH BLENDED LEARNING METHODOLOGICAL TOOLS

As societal development progresses, the burgeoning demands and expectations for individualized education are on the rise. This necessitates a nuanced shift in educational policies and methodologies to cater to the evolving needs of learners. In recent years, there has been a discernible shift from teacher-centered learning to learner-centered learning. Consequently, the paramount concern lies in optimizing the efficacy of training methodologies. This study aims to investigate the potential of enhancing students' learning interest and engagement through the application of blended learning methods. The objective of the research is to systematically compare the outcomes, advantages, and disadvantages, and identify the key areas for further improvement.

Assessing Training Methods for Advanced Driver Assistance Systems and Autonomous Vehicle Functions: Impact on User Mental Models and Performance

Understanding the complexities of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle (AV) technologies is critical for road safety, especially concerning their adoption by drivers. Effective training is a crucial element in ensuring the safe and competent operation of these technologies. This study emphasises the critical role of training methodologies in shaping drivers’ mental models, defined as an individual’s cognitive frameworks for understanding and interacting with ADAS and AV systems. Their mental models substantially influence their interactions with those technologies. A comparative analysis of text-based and video-based training methods has been conducted to assess their influence on participants’ performance and the development of their mental models of ADAS and AV functionalities. Performance is evaluated in terms of the accuracy and reaction time of the participants as they interacted with ADAS and AV functions in a driving simulation. The findings reveal that video-based training yielded better performance outcomes, more accurate mental models, and a deeper understanding of ADAS functionalities among participants. These findings are crucial for policy makers, automotive manufacturers, and educational institutions involved in driver training. They underscore the necessity of developing tailored training programs to facilitate the proficient and safe operation of increasingly complex automotive technologies.

CAM based fine-grained spatial feature supervision for hierarchical yoga pose classification using multi-stage transfer learning

In this paper, we propose a technique for hierarchical yoga pose classification (YPC) in a multi-stage multi-tasking framework. We propose a three-stage transfer learning based end-to-end training methodology. Novelty lies in (a) proposed supervised contrastive combined loss function for stage-1 training, (b) proposed Encoder–Decoder network architecture with attention mechanism for stage-3 training, (c) proposed spatial context aware multi-tasking combined loss function for stage-3 training. Firstly, for stage-1 training, we propose the usage of linear combination of three loss functions: cross-entropy, self-supervised contrastive loss and supervised contrastive loss in a multi-tasking manner. We introduce radial and cosine margin into the formulation of self-supervised and supervised contrastive loss to pull feature embeddings of same class closer together compared to feature embeddings of different classes. Weights learned over stage-1 training are subsequently fine-tuned over cross-entropy multi-tasking loss in stage-2. These stage-2 weights are transfer learned and are further fine-tuned in stage-3 training. For stage-3 training, we propose the usage of spatial context aware multi-tasking combined loss function. This loss function leverages on the fine-grained spatial features obtained from HiResCAM. These are processed in parallel with features obtained from XGrad-CAM (sensitivity and conservation axioms satisfying features) to further supervise the learning of hierarchical yoga pose classifier. We exemplify our methodology on the publicly available Yoga-82 large-scale dataset. We report peak Top-1 YPC accuracy of 95.89% over 6 pose classes (Yoga-6), 93.85% over 20 pose classes (Yoga-20) and 90.0% over 82 pose classes (Yoga-82). Our proposed method achieves 6.1% improvement over Top-1 classification accuracy in Yoga-6 hierarchy, 9.3% improvement in Yoga-20 hierarchy and 10.9% improvement in Yoga-82 hierarchy in comparison with state-of-the-art (SOTA) methodology. We achieve the current best Top-1 classification accuracies in all the three YPC hierarchies.

Association Between Total Genotype Score and Muscle Injuries in Top-Level Football Players: a Pilot Study

BackgroundRecently, genetic predisposition to injury has become a popular area of research and the association between a few single nucleotide polymorphisms (SNPs) and the susceptibility to develop musculoskeletal injuries has been shown. This pilot study aimed to investigate the combined effect of common gene polymorphisms previously associated with muscle injuries in Italian soccer players.ResultsA total of 64 Italian male top football players (age 23.1 ± 5.5 years; stature 180.2 ± 7.4 cm; weight 73.0 ± 7.9 kg) were genotyped for four gene polymorphisms [ACE I/D (rs4341), ACTN3 c.1729C > T (rs1815739), COL5A1 C > T (rs2722) and MCT1 c.1470A > T (rs1049434)].Muscle injuries were gathered for 10 years (2009–2019). Buccal swabs were used to obtain genomic DNA, and the PCR method was used to genotype the samples. The combined influence of the four polymorphisms studied was calculated using a total genotype score (TGS: from 0 to 100 arbitrary units; a.u.). A genotype score (GS) of 2 was assigned to the “protective” genotype for injuries, a GS of 1 was assigned to the heterozygous genotype while a GS of 0 was assigned to the “worst” genotype.The distribution of genotype frequencies in the ACE I/D (rs4341), ACTN3 c.1729C > T (rs1815739) and MCT1 c.1470A > T (rs1049434) polymorphisms was different between non-injured and injured football players (p = 0.001; p = 0.016 and p = 0.005, respectively). The incidence of muscle injuries was significantly different among the ACE I/D (rs4341), ACTN3 c.1729C > T (rs1815739) and COL5A1 C > T (rs2722) genotype groups, showing a lower incidence of injuries in the “protective” genotype than “worse” genotype (ACE, p < 0.001; ACTN3, p = 0.005) or intermediate genotype (COL5A1, p = 0.029).The mean TGS in non-injured football players (63.7 ± 13.0 a.u.) was different from that of injured football players (42.5 ± 12.5 a.u., p < 0.001). There was a TGS cut-off point (56.2 a.u.) to discriminate non-injured from injured football players. Players with a TGS beyond this cut-off had an odds ratio of 3.5 (95%CI 1.8–6.8; p < 0.001) to suffer an injury when compared with players with lower TGS.ConclusionsThese preliminary data suggest that carrying a high number of "protective" gene variants could influence an individual's susceptibility to developing muscle injuries in football. Adapting the training load parameters to the athletes’ genetic profile represents today the new frontier of the methodology of training.

Sensor fusion-based virtual reality for enhanced physical training

PurposeThis study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.Design/methodology/approachThe research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.FindingsSensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.Originality/valueThe integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

Подготовка будущих переводчиков к передаче слов и словосочетаний, репрезентирующих кинесические движения персонажа в художественном произведении

The study aims to justify the need to train future translators to render words and phrases representing characters’ kinesic movements in fiction from one language to another, as well as to propose a corresponding training methodology. The article specifies what a kineme is, indicates its role in fiction and substantiates the importance of its correct translation. An algorithm for translating such words and phrases is presented for the first time. The scientific novelty of the study lies in describing a methodology for training students how to translate words and phrases representing characters’ kinesic movements in fiction. As a result, the article lists the difficulties faced by translators when rendering kinemes from one language to another, presents the algorithm for translating such words and phrases and provides examples of tasks for its successful integration into the educational process, describes the results of testing the developed methodology.

Evaluating a research methodology workshop among postgraduate students using Kirkpatrick's model.

In this era of evidence-based medicine, only systematic research can help in providing judicious and precise healthcare to individual patients based on updated knowledge and skills. However, many medical professionals do not feel competent and confident enough to conduct research. One of the reasons could be the lack of a research-based curriculum in undergraduate courses. The National Medical Council has also stressed the need for formal training in research methodology for healthcare professionals. The research methodology workshops help to familiarize the participants with basic, clinical, and translational research required to impart optimum patient care. The objective of our study was to evaluate a research methodology workshop conducted for postgraduate students by assessing the participant's knowledge, feedback, and expected impact using Kirkpatrick's evaluation model. A quasi-experimental, single-group study was conducted among 132 first-year postgraduate students. The four levels of Kirkpatrick's model were applied for evaluation. Feedback forms, scores of the pretest and posttest, quality of the research proposal drafted by the postgraduates for their thesis, and finally successful submission of the research proposal were the components used to evaluate the four levels of outcome of Kirkpatrick's model. Data collected were compiled and tabulated into MS Excel. Proportions were calculated for categorical variables and mean and standard deviation (SD) for scores. A comparison of means between pre- and postworkshop scores was made with paired t-test. A value of P < 0.05 was considered statistically significant. Statistical analysis was done using IBM SPSS Statistics version 20.0 software. Out of 132 participants, 29% (38) were males and 71% (94) were females. The mean ± SD pretest and posttest scores at a 95% confidence interval were 10.55 ± 2.537 and 12.43 ± 2.484, respectively. The difference was found to be statistically significant by paired sample t-test (P < 0.001). Participant feedback is vital for improving research methodology workshops. The workshop met the overall requirements of the participants. There was a significant improvement in the knowledge of participants after the workshop completion.

Study of the level of training future school physics teachers in methods of physics experiments

Introduction. Experiments are fundamental to the physics teacher’s profession, as well as to other natural science subjects. Knowledge of issues which arise in training students in educational physics experiment improves mastery of the subject. This fact accounts for the relevance of the study and the need to objectively monitor the quality of methodological and subject training of future physics teachers. The article, based on theoretical and experimental research, aims to analyze the level of students’ training, to find out and present objective and subjective reasons that influence the quality of future physics teachers’ mastery of the methods of school experiments, and, based on conclusions and consequences, to make recommendations for improving the educational process at higher education institutions. Methodology and methods. The study was conducted at Vyatka State University (Russia) among 78 students of pedagogical education course (with two training profiles: physics and computer science). The methodology included the organization of exploratory research through the constructed norms, knowledge and skills for the purpose of conducting internal monitoring of student training in educational physics experiment. Results. The study in various aspects revealed a low level of students’ acquisition of the didactic and methodological potential of experiments, and of experiments as a subject activity and as a means of teaching (methodological component). Only 30.8% of test takers were able to answer half the questions sufficiently correctly, and only 2.6% answered all the questions correctly. Only 30.8% gave a complete answer to one of the main research questions: “How are students’ activities organized during this demonstration experiment?” Conclusion. The study achieved its goal: the methodology for internal monitoring of the training level of future physics teachers in methods for organizing experiments was theoretically justified and tested. An in-depth study of the diverse didactic possibilities of educational experiments of forming the physics thinking and the scientific worldview of students is needed.

SIGNIFICANCE OF THE STUDY AND RESEARCH QUESTIONS

This thesis evaluates the effectiveness of individualized physical training programs tailored for high-level athletes, aiming to bridge the gap in sports science literature regarding personalized training methodologies. The primary objective is to ascertain whether customized training regimens yield superior performance outcomes compared to generalized training approaches. Employing a mixed-methods research design, the study integrates quantitative data analysis from performance metrics with qualitative feedback from athletes and coaches across various disciplines.

DIRECTING THE SYSTEM OF PRACTICAL TRAINING IN GENERAL EARTH KNOWLEDGE TO PROFESSIONAL ACTIVITY IN PEDAGOGICAL HIGHER EDUCATION COUNTRIES

In this article, the development and implementation of pedagogical conditions, content, form, methods and tools of preparing future geography teachers in higher educational institutions of pedagogy for professional activities by equipping them with the methodology of organizing practical training in General Earth Science. implementation issues are covered. Also, the problems that await the solution of scientific importance in the formation of methodological training of future geography teachers are highlighted based on the author's observation, study and analysis during the research.

ANALYSIS OF THE PROFESSIONAL COMPETENCIES OF PHYSICAL EDUCATION TEACHERS IN THE CONTEXT OF THE UPDATED CONTENT OF EDUCATION

Purpose. The article is devoted to the problem of professional competence of physical education teachers in the process of implementing the updated content of the subject "Physical culture" in secondary schools. The purpose of the study was to analyze the professional competencies of physical education teachers in the context of the requirements of the updated content of general education. Methodology. A systematic and activity-based approach is fundamental in the study. Research methods: analysis, diagnostic methods, conversation. Results. The results of diagnostics of the formation of professional competencies of physical education teachers within the framework of advanced training courses at the levels of formation (optimal, acceptable, critical, unacceptable) in the field of subject, psychological, pedagogical and methodological training are presented and interpreted. Professional deficits of normative and legal competence among teachers have been identified, the levels of professional skills for the implementation of the educational process on the subject of "Physical culture" in the context of the requirements of the updated content of general education have been determined. Practical implications. The results of the study can be applied in the field of additional professional education of teachers, as well as in the educational process of university training.