Fuzzy Clustering освітніх даних з автоматичним вибором параметрів обробки в системному аналізі якості освіти

Hu, Zhengbing та Derevyanchuk, Oleksandr та Serhiy, Balovsyak та Yuriy, Ushenko та Hanna, Kravchenko та Iryna, Sapsai (2025) Fuzzy Clustering освітніх даних з автоматичним вибором параметрів обробки в системному аналізі якості освіти I.J. Modern Education and Computer Science, 2025, 4, 101-111, 17 (4). с. 101-111. ISSN 2075-0161

Текст IJMECS-V17-N4-71.pdf Download (1MB)

Анотація

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.

Тип елементу :	Стаття
Ключові слова:	Education Technology; Clustering methods; Data Mining; Educational Data; Fuzzy Logic; K-Means; System Analysis; Quality Education
Типологія:	Статті у базах даних > Scopus > У виданнях Q2 Scopus
Підрозділи:	Інститут післядипломної освіти > Кафедра природничо-математичної освіти і технологій
Користувач, що депонує:	Ірина Вікторівна Сапсай
Дата внесення:	23 Жов 2025 06:19
Останні зміни:	23 Жов 2025 06:19
URI:	https://elibrary.kubg.edu.ua/id/eprint/53488

Actions (login required)

Перегляд елементу