ANALYSIS OF TECHNICAL WASTE IN LOW-VOLTAGE OVERHEAD ELECTRICAL NETWORKS CABLES
DOI:
https://doi.org/10.47390/ts-v3i12y2025N16Keywords:
educational data mining (EDM), K-means clustering, student segmentation, personalized learning, learning analytics, machine learning, behavioral analytics.Abstract
This article uses clustering analysis, an important area of data mining in the field of education (Educational Data Mining - EDM). The article proposes a methodology for analyzing data on student academic performance and behavior using the K-means clustering algorithm. Within the framework of the study, students were divided into different groups (clusters) according to their similar characteristics. Each cluster was characterized by its own characteristics, including academic achievement, activity on online platforms, and the intensity of use of educational resources. The results show that the K-means algorithm allows dividing students into specific segments.
The results of the study are of practical importance for the administration of educational institutions in making data-based decisions and personalizing the learning process.
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