THE ROLE OF BIG DATA AND LEARNING ANALYTICS IN THE QUALITY ASSURANCE PROCESS OF HIGHER EDUCATION
DOI:
https://doi.org/10.46991/educ-21st-century.v7.i1.087Keywords:
big data, learning analytics, quality assurance, higher education, student engagement, academic performance, data privacy, ethical considerations, mixed methods, educational technologyAbstract
This research explores how big data and learning analytics can strengthen quality assurance processes in higher education institutions (HEIs). Employing a mixed-methods design, the study gathered data from 600 students and 200 lecturers across six diverse universities, spanning urban and rural contexts. Quantitative analysis, including regression models, showed that engagement with learning management systems (LMS) accounted for 45% of the variation in student grades, underscoring a significant link between technology use and academic outcomes. Qualitative findings from interviews revealed challenges such as inconsistent LMS reliability and ethical issues, notably data privacy concerns, which hinder widespread adoption. The study concludes that learning analytics offer substantial benefits for monitoring and improving educational quality, but their success depends on robust technological infrastructure, staff training, and ethical frameworks. It recommends strategic investments in underserved regions and the establishment of clear data policies to maximize the potential of these tools while addressing equity and privacy.
References
1. Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 267–270). Association for Computing Machinery. https://doi.org/10.1145/2330601.2330666
2. Baig, M. I., Shuib, L., & Yadegaridehkordi, E. (2021). Big data in education: Systematic literature review. Journal of Enterprise Information Management, 34(4), 1074–1105. https://doi.org/10.1108/JEIM-06-2020-0235
3. Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice (pp. 61–75). Springer. https://doi.org/10.1007/978-1-4614-3305-7_4
4. Daniel, B. (2015). 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
5. Drachsler, H., & Greller, W. (2016). Privacy and analytics: It’s a DELICATE issue. A checklist for trusted learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 89–98). Association for Computing Machinery. https://doi.org/10.1145/2883851.2883893
6. Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an 'early warning system' for educators: A proof of concept. Computers & Education, 54(2), 588–599. https://doi.org/10.1016/j.compedu.2009.09.008
7. Gašević, D., Dawson, S., & Siemens, G. (2016). Let’s not forget: Learning analytics are about learning. TechTrends, 60(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x
8. Harvey, L., & Williams, J. (2010). Fifteen years of quality in higher education. Quality in Higher Education, 16(1), 3–36. https://doi.org/10.1080/13538321003679457
9. Jokhan, A., Sharma, B., & Singh, S. (2020). Early warning system as a predictor for student performance in higher education blended courses. Studies in Higher Education, 45(9), 1900–1911. https://doi.org/10.1080/03075079.2019.1604648
10. Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439–1459. https://doi.org/10.1177/0002764213479367
11. Prinsloo, P., & Slade, S. (2017). An elephant in the learning analytics room: The obligation to act. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 46–55). Association for Computing Machinery. https://doi.org/10.1145/3027385.3027406
12. Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
13. Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366
14. Subotzky, G., & Prinsloo, P. (2011). Turning the tide: A socio-critical model and framework for improving student success in open distance learning at the University of South Africa. Distance Education, 32(2), 177–193. https://doi.org/10.1080/01587919.2011.584846
15. Tsai, Y.-S., Poquet, O., Gašević, D., Dawson, S., & Pardo, A. (2018). Complexity leadership in learning analytics: Drivers, challenges, and opportunities. British Journal of Educational Technology, 50(6), 2839–2854. https://doi.org/10.1111/bjet.12846
16. Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Samvel Asatryan , Liza Hakobyan, Nune Adamyan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
