The article is devoted to the creation of an effective solution for user segmentation. The article presents an analysis of existing user segmentation services, an analysis of approaches to user segmentation (ABCDx segmentation, demographic segmentation, segmentation based on a user journey map), an analysis of clustering algorithms (K-means, Mini-Batch K-means, DBSCAN, Agglomerative Clustering, Spectral Clustering). The study of these areas is aimed at creating a “flexible” segmentation solution that adapts to each user sample. Dispersion analysis (ANOVA test), analysis of clustering metrics is also used to assess the quality of user segmentation. With the help of these areas, an effective solution for user segmentation has been developed using advanced analytics and machine learning technology.

The questions of usage of the learners’ data in the solutions for generating student academic groups, electives and project teams are considered. The applications of Machine Learning clustering algorithms for these tasks are illustrated. The opportunity of usage of social network data is shown.

This study focuses on the application of machine learning methods to improve the quality of test items. The research includes a review of the subject area and the implementation of two enhancement methods: similar question retrieval and distractor quality assessment. The first method involves testing five transformer-based models for generating text embeddings and six clustering algorithms. The second method uses the same transformer models in combination with three classification algorithms. Experimental results demonstrated the high effectiveness of the proposed approaches in solving both tasks.

A study of two graphs of scientific cooperation based on co-authorship and citation according to the all-Russian mathematical portal was conducted Math-Net.Ru. A citation-based scientific collaboration graph is a directed graph without loops and multiple edges, whose vertices are the authors of publications, and arcs connect them when there is at least one publication of the first author that cites the publication of the second author. A co-authorship graph is an undirected graph in which the vertices are the authors, and the edges record the co-authorship of two authors in at least one article. The customary study of the main characteristics of both graphs is carried out: diameter and average distance, connectivity components and clustering. In both graphs, we observe a similar connectivity structure – the presence of a giant component and a large number of small components. The similarity and difference of scientific cooperation through co-authorship and citation is noted.