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Article · Scientific Journal of Astana IT University · September 30, 2025

APPLYING DATA ANALYTICS AND BI SYSTEMS TO BUILD A STUDENT DIGITAL PROFILE: THE CASE OF ASTANA IT UNIVERSITY

Gulzhan Soltan, Zharkyn Bekkaliyev, Alen Issayev, Nazerke Akzhol, Zhuldyz Basheyeva

Alen Issayev
Open via DOIPDF

Abstract

Modern challenges of the digital transformation of education require the development of new approaches to assessing academic success and monitoring students' educational trajectories. This study presents a functional model of the data analytics system and visualization of the digital profile of a graduate of Astana IT University (AITU), based on the Integrated IGPA (Integrated Grade Point Average) indicator, which combines the academic, research, and social achievements of students. The aim of the work is to create a system of analytics, visualization, and interpretation of data reflecting the comprehensive development of students and their readiness for professional activity. The theoretical part examines modern approaches to educational analytics in higher education. A critical analysis of scientific sources, including research on learning analytics, educational data mining, and the formation of digital profiles of students, was carried out. The emphasis on technical aspects and insufficient connection with educational practice reveals the main limitations of the existing models. The empirical part uses anonymized data from AITU students for 2022–2024, covering the indicators of Grade Point Average (GPA), Indicators of Research-Oriented Study (iROS), and Social Competition Indicators (SSCI). Dashboards built with the help of Power BI made it possible to visualize and interpret educational trajectories. The use of machine learning algorithms (K-means clustering, PCA analysis) ensured the typologization of student profiles. Using Python and the scikit-learn, seaborn, and pandas’ libraries allowed us to deeply explore the relationships between IGPA components. The results of the study demonstrate the possibilities of personalized academic support, strategic management of educational processes, and increased transparency of student achievement. The developed model can serve as a basis for making managerial decisions and improving the quality of educational programs in the context of digital transformation. The proposed approach can be scaled and adapted to other educational institutions, regardless of their size and specialization. Flexibility in integrating additional indicators reflecting the unique goals and values of a particular educational environment facilitates the model's versatility.

Big Data and Business IntelligenceOnline Learning and AnalyticsEngineering Education and TechnologyLearning analyticsAnalyticsTransparency (behavior)

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