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.
| Mualliflar | Hamiyev, Akrom, Khusanov, Kamoliddin |
|---|---|
| Jurnal | Techscience.uz - техника фанлари долзарб масалалри |
| Nashr sanasi | 2025-12-27 |
| Jild | 3 |
| Son | 12 |
| Betlar | 54-62 |
| Til | O‘zbek |
| DOI | 10.47390/ts-v3i12y2025n07 |
DOI: 10.47390/ts-v3i12y2025n07 · Maqolaning asl sahifasi
educational data mining (EDM), K-means clustering, student segmentation, personalized learning, learning analytics, machine learning, behavioral analytics., ta'lim sohasida ma'lumotlarni qazib olish (EDM), K-means klasterlash, talabalar segmentatsiyasi, shaxsiylashtirilgan ta'lim, o‘quv tahlili, mashinali o‘rganish, xulq-atvor tahlili.
The article developed and compared MIMO models of classical, azeotropic, and extractive rectification. Key differences in dynamics, thermodynamics, and controllability are shown. The need to apply predictive control for…
This paper addresses the development of an integrated intelligent control system based on artificial intelligence for optimizing temperature, pressure, and combustion processes in gas-fired industrial furnaces. The main…
This paper presents an adaptive ensemble framework for real-time anomaly detection in large-scale data streams, addressing the challenges of concept drift, high-velocity data processing, and computational efficiency in…
Artificial Intelligence (AI) is an interdisciplinary field focused on developing methods and technologies that replicate human intellectual behavior. Choosing the appropriate programming language is crucial for…
This article is devoted to the study of the corrosion process of metals in various aggressive environments and the identification of effective protection strategies. The problem is associated with the reduction of the…
This article discusses methods for intelligent modeling of industrial technological systems and the optimization of real-time control strategies. Within the framework of the Industry 4.0 concept, the theoretical and…
The article examines an approach based on the construction of intelligent virtual analyzers, which calculate the values of difficult-to-measure quality indicators based on the current readings of technological parameter…
Drought is one of the most serious natural phenomena negatively impacting arid regions, including Karakalpakstan in the Aral Sea basin. This study used MODIS remote sensing data (NDVI and EVI) to analyze the…
This article examines the key factors determining the quality of deodorized vegetable oils: the physicochemical characteristics of the raw materials, process parameters, equipment design features, and the level of…
The task of the proposed development is to analyze and synthesize an optimal control system for the process of refining vegetable oils and improve the quality of the final product, taking into account oil and alkali…
Techscience.uz - техника фанлари долзарб масалалри — barcha maqolalar