The purpose of this article is to show the evolution and requirements of the educational system in the era of the fourth industrial revolution, to identify the main problems, to identify current areas for further research.

The fourth industrial revolution bases its development on smart technologies, artificial intelligence, big data, robotics, etc. In the new conditions, educational institutions are faced with the task of preparing successful graduates and new ways of learning. The relevance of the problems outlined in this article is determined by the main goal of higher education is to prepare qualified human resources for the country’s economy, create and maintain an extensive advanced knowledge base, and ensure the personal development of graduates of an educational institution. It is the quality of higher education that determines the quality of human resources in a country. To do this, students need to master a wide range of competencies in their chosen field of study, constantly expand the boundaries of knowledge in all disciplines, and develop professional skills in business, science and new technologies.

Methodology and research methods. During the study, an analysis of scientific publications for the period 2012–2022 (plus the beginning of 2023) was carried out, posted in the databases: Springer Link, IEEE Xplore, ACM, Science Direct, Google Scholar, as well as in the scientific electronic library eLIBRARY.ru.

In the course of the study, general scientific methods were used: an analytical review of the problem, methods of synthesis, induction, methods of comparative analysis, generalization and a systematic approach were applied to the use of intellectual analysis methods in e-education systems, scientific publications of the last 20 years were used.

Results and scientific novelty. Our research has identified the most common tasks used in EDM, as well as those that are the most promising in the future. The theoretical analysis of the main key trends of “Education 4.0” carried out in the paper made it possible to identify the main characteristics of education. It was shown that education should become more individualized and adapted to the abilities of the learner. As a result of the study, the most characteristic tasks of Data Mining in education were identified, ways of its improvement and quality improvement were shown.

Practical significance. Currently, educational institutions are striving to improve their learning and teaching by analyzing data collected during students’ studies, developing new databased system in the era of “Industry 4.0”. It is expected that the results mechanisms and improving interesting models that can help obtained can be used by specialists, managers and teachers to improve improve academic outcomes, stimulate student motivation and educational activities. avoid dropouts.

The results obtained can be used as information material in further research related to the study of the development of the education
system in the era of “Industry 4.0”. It is expected that the results
obtained can be used by specialists, managers and teachers to improve educational activities.

Purpose of research. Diagnostics of learning results presented in a competency-based format is currently a necessary part of the educational process of universities. The purpose of the research is to develop and implement a convenient and technological way to diagnose the results of mastering competencies by IT students in the information learning environment. One of the most popular distance learning systems in Russian universities, Moodle, supplemented with proprietary software and third-party resources, is used as the implementation environment. The article presents the results of modeling and implementation of the proposed method for diagnosing the results of mastering the competencies of IT students.

Methods and materials. The authors propose an infological model of the process of preparing a bank of diagnostic tasks in a competence format. The necessity of decomposition of competence indicators into disciplinary components, the achievement of which can be diagnosed by known means of pedagogical measurements, is substantiated. The method of implementing the decomposition of competencies in the Moodle system using the Competence Framework, and the technology of preparing diagnostic tasks based on the principle of uniform coverage of all competence indicators by tasks are presented. A step-by-step algorithm for adjusting the bank of tasks is proposed. The algorithm is based on the analysis of the statistical characteristics of the results of completing tasks by students. A method for automatically tracking the mastering of competencies by students based on the results of performing diagnostic tasks in the Moodle system is described.

Results. The article presents an example of the implementation of the proposed method for diagnosing the results of mastering the competencies in the process of training bachelors in the direction “09.03.04 Software engineering”. The following are presented: the result of decomposition of indicators of achievement of one of the decomposition of competencies into disciplinary components. The general professional competencies into disciplinary components, results of the implementation of the proposed method at the Vologda the process of preparing tasks for diagnostics for each disciplinary State University when teaching students of the direction “Software component, disciplinary and interdisciplinary results of diagnosing Engineering” are presented. The results of modeling and implethe mastering of competencies obtained in the Moodle system. mentation of the process of diagnostics of the results of mastering The results of the experiment showed that the implementation of competencies can be adapted to the educational process for various the proposed method in the information educational environment areas of training and forms of education. The continuation of this requires certain time costs at the preparatory stage, but they pay study has good prospects for improving the quality of diagnostic off by improving the quality of diagnostic procedures and the procedures of the competence format and the effectiveness of the convenience of their implementation. educational process as a whole.

Conclusion. In the course of the study, a technological method for diagnosing the results of mastering the competencies by IT
students in the information learning environment was developed,
implemented and tested in practice. The method is based on the
decomposition of competencies into disciplinary components. The
results of the implementation of the proposed method at the Vologda
State University when teaching students of the direction “Software
Engineering” are presented. The results of modeling and implementation of the process of diagnostics of the results of mastering competencies can be adapted to the educational process for various areas of training and forms of education. The continuation of this study has good prospects for improving the quality of diagnostic
procedures of the competence format and the effectiveness of the
educational process as a whole.

Purpose of research. The higher education system is undergoing changes under the influence of an increasing number of IT solutions used. Transformation changes take place at the organizational, technological, legal, and regulatory levels of management. Each of the directions affects the features of the functioning and development of the higher education system. In the 

process of their implementation, there are also deviations, risks that need to be, if not eliminated, then at least minimized. The article describes four main directions of development: technical, technological, instrumental, and educational. The types of risks associated with each of the described areas are also highlighted.

Materials and methods. A set of methods was used in the paper: bibliographic (selection of articles by keywords); bibliometric (quantitative characteristics by time parameters); content analysis (method of studying the content of articles); evaluation of keyword queries using Internet services.

Results. An analysis of queries by keywords showed that interest in the issues of digitalization and digital transformation of higher education arose later than in the system of general secondary education. There is a tendency to adapt successful models of digitalization of secondary education and business areas to the activities of the higher education system. Without considering the peculiarities of the functioning and development of the higher education system, we can get negative consequences expressed in different types of risks. The paper highlights financial, form-major, technological, operational, strategic, cognitive, and social risks.

Conclusion. One of the key problems highlighted in the process of analyzing developments in the field of digital transformation of the education system is the consideration of digitalization as means, and not as a catalyst for systemic changes in all areas of activity. Point solutions will not allow you to fully realize the potential of digital solutions. When considering the problems of digitalization and digital transformation, higher education systems are often guided by successful models in the field of secondary general education and / or business environment, which can contribute to the formation of negative consequences when adapting approaches without considering their own specifics.

Purpose of research. The higher education system is undergoing changes under the influence of an increasing number of IT solutions used. Transformation changes take place at the organizational, technological, legal, and regulatory levels of management. Each of the directions affects the features of the functioning and development of the higher education system. In the process of their implementation, there are also deviations, risks that need to be, if not eliminated, then at least minimized. The article describes four main directions of development: technical, technological, instrumental, and educational. The types of risks associated with each of the described areas are also highlighted.

Materials and methods. A set of methods was used in the paper: bibliographic (selection of articles by keywords); bibliometric (quantitative characteristics by time parameters); content analysis (method of studying the content of articles); evaluation of keyword queries using Internet services.

Results. An analysis of queries by keywords showed that interest in the issues of digitalization and digital transformation of higher education arose later than in the system of general secondary education. There is a tendency to adapt successful models of digitalization of secondary education and business areas to the activities of the higher education system. Without considering the peculiarities of the functioning and development of the higher education system, we can get negative consequences expressed in different types of risks. The paper highlights financial, form-major, technological, operational, strategic, cognitive, and social risks.

Conclusion. One of the key problems highlighted in the process of analyzing developments in the field of digital transformation of the education system is the consideration of digitalization as means, and not as a catalyst for systemic changes in all areas of activity. Point solutions will not allow you to fully realize the potential of digital solutions. When considering the problems of digitalization and digital transformation, higher education systems are often guided by successful models in the field of secondary general education and / or business environment, which can contribute to the formation of negative consequences when adapting approaches without considering their own specifics.

The research method consists in applying the state space method, widely used in the study of automatic dynamical systems, to describe the behavior of cognitive systems. It is assumed, that at the input of the cognitive system, there is a signal and interference described by Poisson point processes, modeling the amount of information, the amount of emotional stress, etc., corresponding to each event. The cognitive properties of the system in the paper are taken into account by two circumstances.

Firstly, events localized in time are characterized in the paper not only by the Poisson distribution of the times of their occurrence, but also by some random variables that characterize the importance (significance) events for the system. A typical example is the attribution of a certain amount of information to each event, if an information processing system is modeled. Another example is the emotional reaction of a person to the appearance of stress, described in a classic work on psychology. In this case, the point is the event that causes stress, and the effects of stress on the system are modeled by the relative magnitude of stress in accordance with the Holmes and Rahe scale. Secondly, the cognitive system processes, assimilates, adapts to the impact that each event has on it with its inherent speed. In this paper, this phenomenon is modeled as the passage of a point process through a dynamic system described by differential equations. Such processes are called filtered point processes.

Examples of impacts are given and, for simplicity, an assumption is made about the magnitude of the impact as the amount of information received by the system when an event occurs. Thus, the model of a cognitive system is a dynamic system described by a differential equation in the state space, at the input of which messages with a certain information load appear at random discrete moments of time.

As for any technical system, the cognitive system faces the task of evaluating the quality of its work. In this regard, the paper substantiates the use of a convenient quality index from an engineering point of view and an appropriate criterion in the form of a signal – interference ratio.

The new results are differential equations in the state space for the mathematical expectations of the signal and interference, as well as an algorithm for calculating the noise immunity of the cognitive system. As an example, a graph of the noise immunity of a particular cognitive system is calculated and presented, confirming an intuitive idea of its behavior.

In conclusion, it is noted that the main result of the paper is an algorithm for calculating the noise immunity of cognitive systems using differential equations that allow calculating the behavior of non-stationary cognitive systems under any point impacts described by a non-stationary function of the intensities of the appearance of points. The equations of behavior of the mathematical expectation of the processed information are reduced to a canonical form, which allows them to be applied to a variety of practical tasks, for example, to the description of hierarchical cognitive structures when the output of one level is the input of another.

Purpose of research. The purpose of the study is to evaluate certain machine learning models in data processing based on speed and efficiency related to the analysis of sentiment or consumer opinions in business intelligence. To highlight the existing developments, an overview of modern methods and models of sentiment analysis is given, demonstrating their advantages and disadvantages.

Materials and methods. In order to improve the semester analysis process, organized using existing methods and models, it is necessary to adjust it in accordance with the growing changes in information flows today. In this case, it is crucial for researchers to explore the possibilities of updating certain tools, either to combine them or to develop them to adapt them to modern tasks in order to provide a clearer understanding of the results of their treatment. We present a comparison of several deep learning models, including convolutional neural networks, recurrent neural networks, and long-term and shortterm bidirectional memory, evaluated using different approaches to word integration, including Bidirectional Encoder Representations from Transformers (BERT) and its variants, FastText and Word2Vec. Data augmentation was conducted using a simple data augmentation approach. This project uses natural language processing (NLP), deep learning, and models such as LSTM, CNN, SVM TF-IDF, Adaboost, Naive Bayes, and then combinations of models.

The results of the study allowed us to obtain and verify model results with user reviews and compare model accuracy to see which model had the highest accuracy results from the models and their combination of CNN with LSTM model, but SVM with TF-IDF vectoring was most effective for this unbalanced data set. In the constructed model, the result was the following indexes: ROC AUC - 0.82, precision - 0.92, F1 - 0.82, Precision - 0.82, and Recall - 0.82. More research and model implementation can be done to find a better model.

Conclusion. Natural language text analysis has advanced quite a bit in recent years, and it is possible that such problems will be completely solved in the near future. Several different models in ML and CNN with the LSTM model, but SVM with the TF-IDF vectorizer proved most effective for this unbalanced data set. In general, both deep classification algorithm. A combination of both approaches can also learning and feature-based selection methods can be used to solve be used to further improve the efficiency of the algorithm. some of the most pressing problems. Deep learning is useful when the most relevant features are not known in advance, while feature-based