This article presents elements of the linguistic knowledge graph “Turklang”, developed at the Institute of Applied Semiotics of the Academy of Sciences of Tatarstan and used as a basis for creating a number of linguistic resources and tools: the portal “Turkic Morpheme”, the electronic corpus of the Tatar language “Tugan Tel”, morphoanalyzer. Creating an educational environment requires subject-oriented knowledge graphs, for which methods of general and open graphs are not suitable. This paper describes linguistic knowledge graphs, which reflect, on the one hand, potential capabilities of Turkic languages, and on the other hand, examples of actual use in texts. Peculiarity of these knowledge graphs is that they contain linguistic units of different linguistic levels, and concepts corresponding to meanings of these linguistic units, which are built into the thesaurus of concepts. Structure of this knowledge graph allows to formulate the content of a training course, build an individual educational trajectory, as well as create tests and tools of automated answer grading as part of knowledge control when teaching Turkic languages. This makes it possible to subsequently develop, based on these graphs, training programs taking into account the structural and functional features of the Turkic languages, and also contributes to the implementation of individual goals of students.

This article presents an educational blockchain simulator intended for training students and beginning blockchain developers. The simulator was created to provide users with an intuitive and accessible tool for learning the basic concepts and mechanisms of blockchain functioning. The article discusses the main aspects of the design and architecture of the simulator, and also provides a demonstration of the application. In addition, the possibilities for further development of the simulator and its potential as a tool for teaching and research in the field of blockchain technologies are discussed. The resulting simulator contributes to the field of education and science, helping to increase the level of competence of specialists and the development of innovative solutions in the field of blockchain.

Methods for assessing student answers using language models are currently being studied by various specialists. The results of automated assessment depend on the subject area and characteristics of the academic discipline. This paper analyzes the students’ answers received during the course «Computer Graphics and Design». It is proposed to determine the cosine similarity of document vectors obtained using language models and refine the estimates by checking keywords. The results obtained can be used for preliminary assessment of students' answers and are the basis for further research.

The problem of predicting early dropout of students of Russian universities is urgent and therefore requires the development of new innovative approaches to solve it. To solve this problem, it is possible to develop predictive systems based on the use of student data, available in the information systems of universities. This paper investigates machine learning models for predicting early student dropout trained on the basis of student characteristics and performance data. The main scientific novelty of the work lies in the use of explainable AI methods to interpret and explain the performance of the trained machine learning models. The Explainable AI methods allow us to understand which of the input features (student characteristics) have the greatest influence on the results of the machine learning models. (student characteristics) have the greatest influence on the prediction results of trained models, and can also help to understand why the models make certain decisions. The findings expand the understanding of the influence of various factors on early dropout of students.

The paper is devoted to the study of the cognitive function associated with the generation of elliptical sentences in the Russian language. The study is conducted by testing this cognitive ability using a computer system specially developed by the authors for this purpose. Testing of this cognitive ability is proposed and implemented for the first time. The system is an extension of Moodle and is openly hosted in the github repository. Elliptical constructions are limited to verbal and nominal ellipses, which are theoretically possible to be completely reconstructed based on the context of the sentence. The study is conducted with the participation of SPbPU students as respondents. The texts of planimetric tasks are chosen as the subject area. As a result of the analysis of testing data, the following results are obtained: the influence of the respondent’s knowledge of the subject area (planimetry) on the test results is established; a tendency towards self-study of respondents was discovered, which is manifested in a reduction in time and an increase in scores as they pass tests; it is shown that respondents are poorly motivated if they do not see feedback on the answer to the completed task. The paper discusses the problems of further development of the testing system and its use in adapting questionnaires (tasks) to assess the knowledge of SPbPU students in the field of automation of bug detection in programs, as well as for diagnosing the functional state of operator specialists and express diagnosis of dementia. It also seems promising to use the system to improve the processes of syntactic parsing of elliptic sentences and automate the restoration of ellipses in the subject area of planimetry.

This paper is devoted to the design and development of a system for automating numerical similarity assessment of Android applications. The task of application similarity evaluation is reduced to the similarity evaluation of sets of control flow graphs constructed based on code from classes.dex files of applications. The similarity value was calculated based on the similarity matrix. The algorithms of graph editing and Levenshtein distance were used to compare control flow graphs. Application similarity criteria were formulated and their representation forms were investigated. Types of Android application models and methods of their construction are presented. A prototype of the system for automating the numerical evaluation of Android-applications similarity is developed. Optimization of the software solution is performed with the help of parallel programming tools. Experiments are carried out and the conclusion is made about the ability of the developed system to detect similarities between Android applications.

Nowadays, many different tools can be used to classify images, each of which is aimed at solving a certain range of tasks. This article provides a brief overview of libraries and technologies for image classification. The architecture of a simple convolutional neural network for image classification is built. Image recognition experiments have been conducted with popular neural networks such as VGG 16 and ResNet 50. Both neural networks have shown good results. However, ResNet 50 overfitted due to the fact that the dataset contained the same type of images for training, since this neural network has more layers that allow reading the attributes of objects in the images. A comparative analysis of image recognition specially prepared for this experiment was carried out with the trained models.

In the paper, we develop some approaches to teaching multidimensional geometry in elective courses the aim of which is to help students to develop multidimensional geometric intuition. Special attention is given to the use of transformation groups in the study of geometry of regular polyhedrons.