APPLYING DATA ANALYTICS AND BI SYSTEMS TO BUILD A STUDENT DIGITAL PROFILE: THE CASE OF ASTANA IT UNIVERSITY
DOI:
https://doi.org/10.37943/23JOZI8138Keywords:
digital graduate profile, integral GPA, educational analytics, data visualization, digital transformation of education, automation of data analysis, interactive dashboardsAbstract
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.
References
Salas Pilco, S. Z., Xiao, K., & Hu, X. (2022). Artificial intelligence and learning analytics in teacher education: A systematic review. Education Sciences, 12(8), 569. https://doi.org/10.3390/educsci12080569
Wu, C., & Carroll, J. M. (2024). Self-presentation and social networking online: The professional identity of PhD students in HCI. The Internet and Higher Education, 62, 100951. https://doi.org/10.1016/j.iheduc.2024.100951
Stojanov, A., & Daniel, B. K. (2024). A decade of research into the application of big data and analytics in higher education: A systematic review of the literature. Education and Information Technologies, 29, 5807–5831. https://doi.org 10.1007/s10639-023-12033-8
Zhang, M. (2024). Integrating deep learning into educational big data analytics for enhanced intelligent learning platforms. Information Technology and Control, 53(4). https://doi.org/10.5755/j01.itc.53.4.36968
Romero, C., & Ventura, S. (2024). Educational data mining and learning analytics: An updated survey. arXiv preprint. https://doi.org/10.48550/arXiv.2402.07956
Stojanov, A., & Daniel, B. K. (2024). A decade of research into application of big data and analytics in higher education: A systematic review. Education and Information Technologies, 29, 5807–5831. https://doi.org/10.1007/s10639-023-12033-8
Ersozlu, Z., Taheri, S., & Koch, I. (2024). A review of machine learning methods used for educational data. Education and Information Technologies, 29, 22125–22145. https://doi.org/10.1007/s10639-024-12704-0
Batuchina, A., & Melnikova, J. (2023). Application of learning analytics in European general education schools: Theoretical review. Quaderni di Comunità, 2, 119–131. https://doi.org/10.61007/QdC.2023.2.119
Wang, C., Chen, J., Xie, Z., & Zou, J. (2024). Research on education big data for students academic performance analysis based on machine learning. arXiv preprint. https://doi.org/10.48550/arXiv.2407.16907
Ed Daoudi, R., Azhari, M., Ettaki, B., & Zerouaoui, J. (2024). Academic performance prediction in virtual environments using big data and machine learning. Journal of Electrical Systems, 20(3).
https://www.scimagojr.com/journalsearch.php?q=21100386444&tip=sid
Gómez Pulido, J. A., Park, Y., Soto, R., & Lanza Gutiérrez, J. M. (2023). Data analytics and machine learning in education. Applied Sciences, 13(3), 1418. https://doi.org/10.3390/app13031418
Zhang, J., Liu, H., & Wu, P. (2025). Machine learning based big data analytics for education in curriculum reforms. Applied Mathematics and Nonlinear Sciences, 10(1). https://doi.org/10.2478/amns-2025-0135
Sorour, A., & Atkins, A. S. (2024). Big data challenge for monitoring quality in higher education institutions using business intelligence dashboards. Journal of Electronic Science and Technology, 22(1), 100233. https://doi.org/10.1016/j.jnlest.2024.100233
Luo, X. (2024). Learning analytics based on big data: Student behavior prediction and personalized educational strategy formulation. Applied and Computational Engineering, 116, 7–13. https://doi.org/10.54254/2755-2721/116/20251732
Khan, F., Al Shammari, M., & Alghamdi, A. (2022). Artificial intelligence and big data: The advent of new pedagogy in the adaptive e learning system in the higher educational institutions of Saudi Arabia. Education Research International, 2022, 1263555. https://doi.org/10.1155/2022/1263555
Shaulska, L., Yurchishena, L., & Popovskyi, Y. (2021). Using MS Power BI tools in the university management system to deepen the value proposition. Proceedings of ACIT, 294–298. https://doi.org/10.1109/ACIT52158.2021.9548447
Gonçalves, C., Gonçalves, M. A., & Campante, M. (2023). Developing integrated performance dashboards visualisations using Power BI as a platform. Information, 14, 614. https://doi.org/10.3390/info14110614
Sequeira, R., Reis, A., Alves, P., & Branco, F. (2024). Roadmap for implementing business intelligence systems in higher education institutions: Systematic literature review. Information, 15(4), 208. https://doi.org/10.3390/info15040208
Azevedo, A., Azevedo, J. M., & Hayakawa, M. (2021). Designing and implementing a dashboard with key performance indicators for a higher education institution. Proceedings of CSEDU, 165–172. https://doi.org/10.5220/0010539501650172
Badhe, R., & Khond, R. (2024). Student performance monitoring system using Power BI. ResearchGate. https://doi.org/10.13140/RG.2.2.32532.10884
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