Clustering of educational data was performed in the space of two parameters using the K-Means method. Students who are characterized by grades in certain types of activities were used as objects of clustering. Software for fuzzy data clustering is implemented in the Python language in the Google Colab cloud service. The obtained clusters are described by fuzzy Gaussian membership functions, which allowed to reliably determine the membership of each object to a certain cluster, even if the clusters do not have clear boundaries. Due to clustering, the most important characteristics of the educational process for a certain task are obtained, that is, this is how Data Manning tasks are solved. Fuzzy membership functions implemented using the scikit-fuzzy library. The developed program can also be used for educational purposes, as it allows a better understanding of the principles of cluster analysis and fuzzy logic. The correctness of the work of the developed program was confirmed during the processing of test educational data. The determination of the number of clusters was performed by software, taking into account the intra-cluster and inter-cluster distances, as well as the shape of the clusters. Automated selection of the number of clusters and cluster boundaries allows to reduce data processing time. The developed clustering tools are designed to increase the efficiency of system analysis of quality education.

In the work, an analysis of modern methods of Educational Data Mining (EDM) was carried out, on the basis of which a set of methods of EDM was developed for the training of vocational education teachers. The basic methods of EDM are considered, namely Prediction, Clustering, Relationship Mining, Distillation of Data for Human Judgment, Discovery with Models. The possibilities of using artificial neural networks, in particular, networks of Long-Short-Term Memory (LSTM), to predict the results of the educational process are described. The main methods of clustering and segmentation of educational data are considered. The basic methods of EDM are complemented by specialized methods of digital image pre-processing and methods of artificial intelligence, taking into account the peculiarities of the training of future specialists in engineering and pedagogical specialties. As specialized methods of digital image pre-processing, methods of filtering, contrast enhancement and contour selection are used. As specialized methods of artificial intelligence, methods of image segmentation, object detection on images, object detection using fuzzy logic were used. Methods of object detection on images using convolutional neural networks and using the Viola-Jones method are described. To process data with a certain degree of uncertainty, it is proposed to apply the methods of EDM and Fuzzy Logic in a integral manner. Ways of integrating Fuzzy Logic with methods of data clustering, image segmentation and object detection on images are considered. The possibilities of applying the developed complex of specialized methods of EDM in the educational process, in particular, when performing STEM (Science, Technology, Engineering and Mathematics) projects, are described.