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International Journal of Modern Computer Science and IT Innovations

Open Access Peer Review International
Open Access

Data Science Approaches in The Education System and Their Pedagogical Significance

DOI https://doi.org/10.55640/ijmcsit-v03i05-06
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Eshmurodova Malikabonu 1 , Odiljonov Ikromjon 1 , Husanova Marjona 1 , Mukhriddin Mukhiddinov 1 ,
4 Master student of Nordic International University, Uzbekistan
4 Master student of Nordic International University, Uzbekistan
4 Master student of Nordic International University, Uzbekistan
4 Associate Professor of Nordic International University, Uzbekistan

Abstract

The rapid development of digital technologies has transformed educational systems worldwide, generating large volumes of educational data through learning management systems, online platforms, and digital assessment tools. Data Science has emerged as a powerful approach for extracting meaningful insights from these data and improving educational effectiveness. This study examines the pedagogical significance of Data Science approaches in modern education, focusing on Learning Analytics, Educational Data Mining, Machine Learning, and Artificial Intelligence technologies. The research employed a systematic literature review and comparative analysis of recent studies related to data-driven educational practices. The findings indicate that Data Science technologies support personalized learning, improve academic performance prediction, facilitate adaptive learning environments, and enhance educational decision-making. Furthermore, these approaches contribute to identifying learning difficulties at early stages and optimizing instructional strategies. However, challenges related to data privacy, algorithmic bias, and technological infrastructure remain significant concerns. The study concludes that Data Science-based educational approaches represent an essential component of future digital pedagogy and educational innovation.

Keywords

Data Science, Learning Analytics, Educational Data Mining

References

Alpaydin, E. (2020). Introduction to Machine Learning (4th ed.). MIT Press.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Research and Practice in Technology Enhanced Learning, 15(1), 1–29.
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355.
Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.
UNESCO. (2021). AI and Education: Guidance for Policy-makers. UNESCO Publishing.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(39), 1–27.

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