Systemic Reform Driven by Educational Big Data: A Mixed Study of Multimodal Learning Analytics and Evidence-Based Decision Making

Shuo Sun

doi:10.71222/yh31y875

Authors

Shuo Sun Graduate School of Information and Communication, Ajou University, Suwon, Korea Author

DOI:

https://doi.org/10.71222/yh31y875

Keywords:

educational big data, learning analytics, systemic transformation, data-driven education, educational equity

Abstract

With the rapid advancement of educational big data and learning analytics technologies, data-driven systemic transformation in education has become increasingly viable and impactful. As digital learning environments proliferate globally, the sheer volume of data generated offers unprecedented opportunities to understand and optimize the learning process. This study explores how multimodal learning analytics (MMLA) can support evidence-based decision making in educational transformation efforts. Using a comprehensive mixed-methods approach, we systematically analyze quantitative data extracted from digital learning platforms alongside in-depth qualitative insights gathered from educators, administrators, and policymakers. The integration of these diverse data sources allows for a holistic understanding of the complex educational ecosystem. The findings highlight how MMLA enables more accurate learner profiling, facilitates real-time feedback mechanisms, and promotes highly personalized instruction tailored to individual student needs. Furthermore, the empirical evidence generated through this approach plays a crucial role in informing strategic policy adjustments and driving systemic improvements across educational institutions. By bridging the gap between raw data and actionable pedagogical strategies, this research provides a robust, practical framework for integrating data-intensive tools into institutional decision-making processes. Ultimately, the proposed framework seeks to significantly enhance teaching effectiveness, boost learner engagement, and foster greater educational equity by ensuring that interventions are precisely targeted and empirically supported. This study underscores the transformative potential of combining advanced analytics with pedagogical expertise to shape the future of modern education.

References

1. S. M. Asadzadeh and A. Azadeh, "An integrated systemic model for optimization of condition-based maintenance with human error," Reliability Engineering & System Safety, vol. 124, pp. 117-131, 2014.

2. E. S. Parris, M. J. Carey, and H. Lloyd-Thomas, "Feature fusion for music detection," in Eurospeech, Sep. 1999, pp. 2191-2194.

3. G. E. Marcus and M. B. MacKuen, "Anxiety, enthusiasm, and the vote: The emotional underpinnings of learning and involvement during presidential campaigns," American Political Science Review, vol. 87, no. 3, pp. 672-685, 1993.

4. D. Sargiotis, "Establishing a Data Governance Culture: Change Management and Leadership," in Data Governance: A Guide, Cham: Springer Nature Switzerland, 2024, pp. 391-418.

5. C. Navarretta and P. Paggio, "Classification of feedback expressions in multimodal data," in Proceedings of the ACL 2010 Conference Short Papers, Jul. 2010, pp. 318-324.

6. E. E. Jang, D. E. McDougall, D. Pollon, M. Herbert, and P. Russell, "Integrative mixed methods data analytic strategies in research on school success in challenging circumstances," Journal of Mixed Methods Research, vol. 2, no. 3, pp. 221-247, 2008.

7. A. Walker, M. Recker, M. B. Robertshaw, J. Osen, H. Leary, L. Ye, and L. Sellers, "Integrating technology and problem-based learning: A mixed methods study of two teacher professional development designs," Interdisciplinary Journal of Problem-Based Learning, vol. 5, no. 2, p. 7, 2011.

8. E. Morvant, A. Habrard, and S. Ayache, "Majority vote of diverse classifiers for late fusion," in *Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)*, Berlin, Heidelberg: Springer Berlin Heidelberg, Aug. 2014, pp. 153-162.

9. V. Venkatesh, S. A. Brown, and H. Bala, "Bridging the qualitative–quantitative divide: Guidelines for conducting mixed methods research in information systems," MIS Quarterly, vol. 37, no. 1, pp. 21-54, 2013.

10. K. L. M. Ang, F. L. Ge, and K. P. Seng, "Big educational data & analytics: Survey, architecture and challenges," IEEE Access, vol. 8, pp. 116392-116414, 2020.

11. S. Ray and M. Saeed, "Applications of educational data mining and learning analytics tools in handling big data in higher education," in Applications of Big Data Analytics: Trends, Issues, and Challenges, Cham: Springer International Publishing, 2018, pp. 135-160.

12. P. Long and G. Siemens, "Penetrating the fog: Analytics in learning and education," EDUCAUSE Review (Online), 2011.