Published: 2024-03-30
Utilization of Big Data in Educational Technology Research
DOI: 10.35870/ijecs.v4i1.2643
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Abstract
This study critically examines the implementation of big data analytics within the field of Educational Technology, with a specific focus on its application at the Faculty of Engineering, Langlangbuana University. The research is prompted by the unprecedented shift from traditional, in-person instruction to online learning environments, a transition significantly expedited by the COVID-19 pandemic. This paper explores how big data can be systematically utilized to develop advanced educational strategies that address the complexities of modern learning environments. Through an extensive review of relevant local and international literature, the study highlights the potential of big data to offer granular insights into student engagement, learning outcomes, and instructional effectiveness. The findings suggest that the strategic integration of big data in educational research not only facilitates personalized learning and improves pedagogical practices but also enhances institutional decision-making processes. This research underscores the critical role of big data in fostering an adaptive, evidence-based approach to contemporary educational challenges.
Keywords
Big Data ; Educational Technology ; Online Learning ; Learning Analytics ; Student Performance
Article Information
This article has been peer-reviewed and published in the International Journal Education and Computer Studies (IJECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 4 No. 1 (2024)
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Section: Articles
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Published: %750 %e, %2024
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License: CC BY 4.0
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Copyright: © 2024 Authors
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DOI: 10.35870/ijecs.v4i1.2643
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Awan Setiawan
Informatics Engineering Study Program, Universitas Langlangbuana, Bandung City, West Java Province, Indonesia
Aldi Muhammad
Informatics Engineering Study Program, Universitas Langlangbuana, Bandung City, West Java Province, Indonesia
Agung Purnama Sandi
Informatics Engineering Study Program, Universitas Langlangbuana, Bandung City, West Java Province, Indonesia
Nabila Rezky Pebrianti
Informatics Engineering Study Program, Universitas Langlangbuana, Bandung City, West Java Province, Indonesia
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Agustini, K. (2017). The adaptive eLearning system design: Student learning style trend analysis. In Proceedings of 2nd International Conference on Innovative Research Across Disciplines (ICIRAD) (pp. 50-54). https://doi.org/10.2991/icirad-17.2017.12.
-
Akrami, K. (2024). Investigating the integration of big data technologies in higher education settings. IJMST, 2(2), 1-12. https://doi.org/10.31004/ijmst.v2i2.296
-
Al-Rahmi, W., Yahaya, N., Aldraiweesh, A., Alturki, U., Alamri, M., Saud, M., & Alhamed, O. (2019). Big data adoption and knowledge management sharing: an empirical investigation on their adoption and sustainability as a purpose of education. IEEE Access, 7, 47245-47258. https://doi.org/10.1109/access.2019.2906668
-
Bai, H. (2024). Design and application of decision support system for educational management based on big data. JES, 20(6s), 1645-1655. https://doi.org/10.52783/jes.3084
-
Cusumano, M. (2013). MOOCs: Contexts and consequences. Communications of the ACM, 56(4), 31-33. https://doi.org/10.1145/2436256.2436266
-
Daniel, B. (2014). Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904-920. https://doi.org/10.1111/bjet.12230
-
Di, X., Zhu, D., & Wen-hai, X. (2021). The teaching pattern of law majors using artificial intelligence and deep neural network under educational psychology. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.711520
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