Headings of the journal
"Educational Resources and Technologies"

Annotation: The article addresses the problem of objective competency assessment for information security specialists. The relevance is driven by a shortage of qualified personnel and the necessity to transition from subjective to formalized methods amidst digital transformation. The aim is the development and experimental testing of a methodology based on the application of mathematical methods—specifically, the Analytic Hierarchy Process and fuzzy set theory. The scientific novelty lies in the synthesis of a specialized process-result competency model with quantitative multi-criteria evaluation methods, which minimizes subjectivity and yields an integral numerical indicator of proficiency level. The methodological foundation comprised theoretical analysis, expert survey, and a pedagogical experiment involving the assessment of a group of specialists. The results demonstrate that the proposed mathematical toolkit provides not only comparative ranking of specialists but also precise diagnostics of individual deficits in their professional profile. This establishes a basis for constructing personalized develop- ment pathways. The practical significance of the research lies in the potential for implementing the methodology into processes of certification, career planning, and the forming of individual professional development programs within state and commercial structures.

Annotation: The article analyzes the opportunities and risks of using artificial intelligence (AI) technologies in higher educa- tion. The relevance of the article stems from the difference in cognitive and technological positions regarding AI between teachers and students. For instance, students master digital tools faster, while teachers are more cautious and critical. The results of a sociological survey of students and teachers at the Ural State University of Economics served as the empirical basis of the study. A comparative analysis of the perception and practice of using AI was conducted between two key groups in the educational process: students and teachers. The study identified differences in the purposes for using AI and the readiness to integrate it into the educational process. The study reveals a societal demand for the institutionalization of AI competencies in higher education. The ar- ticle substantiates the need to modernize educational programs, taking into account the growing role of artificial intelligence, and emphasizes the importance of critically assessing the advantages and risks of neural networks for students’ effective knowledge acquisition. The results obtained expand the empirical base of research on the digital transformation of education and can be used in the development of university educational policies.

Annotation: The article empirically assesses the effectiveness of three models of higher education digitalization: the basic online learning system (Learning Management System, LMS); blended learning with self-Regulated learning mechanisms (SRL); the flipped classroom with analytics tools. A quasi-experimental study was performed, and a parameterized evaluation of the effectiveness of these learning models was determined. The initial diagnostic and academic performance indicators, as well as the digital readiness index, were used as the initial covariates. The final variable is the final control score, whereas additional indicators are engagement and dropout risk. The statistical analysis included the ANCOVA (Analysis of covariance) method with covariate control, logistic regression of dropout risk, and mediation analysis of the indirect effect via SRL with bootstrap estimation of confidence intervals. The research findings have yielded an integrative model wherein the pedagogical design of digital educational environments exerts an indirect influence on academic outcomes by developing self-regulated learning processes. The model assumes that: basic LMS support provides minimal support for self-regulation; blended learning with targeted self-regulatory learning mechanisms enhances goal setting, monitoring, and stra- tegic participation in learning; flipped classroom with analytics tools generates the most intense feedback cycles and adaptive regulation. Formally, the model is expressed by the following link: pedagogical design of digital learning – self-regulated learning – academic performance. These pedagogical configurations will allow us to consistently strengthen the impact on the development of self-regulated learning, which, in turn, will contribute to the growth of academic performance.

Annotation: The article presents a model of using didactic games in the process of teaching chemistry to medical students in order to improve the effectiveness of mastering this discipline by future physicians. The author notes and justifies the relevance of using didactic games in higher education. In order to expose the pedagogical potential of didactic games, a systematization of didactic games has been developed for the entire period of mastering chemistry disciplines by medical students. The systematization is based on the dominant didactic goal and the nature of students’ activities. Particular emphasis is placed on the methodological principles of developing and implementing didactic games: consideration of the specifics of higher education, systematic application, prac- tice-oriented approach, and the organic inclusion of chemical experiments. The author notes that the results of applying the model will be most successful if didactic games are used systematically in the educational process and have appropriate methodological content: practice-oriented tasks and the use of various types of chemical experiments. The validity of the proposed model is demonstrated.

Annotation: This article analyzes the relationship between publication activity of applicants to doctoral programs and the likelihood of their successful on-time thesis defence. The issue of empirically verifying publication background as a predictor of successful thesis defense is underrepresented in the Russian scientific literature, making this research topic relevant. This empirical study was conducted using data from the doctoral program at the Mos- cow Technical University of Communications and Informatics (MTUCI) and the Russian Science Citation Index (RSCI), a national bibliographic database. Bibliometric indicators of 121 postgraduate students were collected based on the initial data on those enrolled in postgraduate studies in 2017–2020 and those who graduated in 2021–2024. The analysis of the generated dataset confirms the correlation between the bibliometric indicators of doctoral program applicants and the probability of their successful on-time thesis defence. During the study, a methodology was developed for the integrated assessment of postgraduate students’ publication background. The research addresses the important and methodologically significant issue of assessing predictors of academic success in the training of highly qualified personnel.