An Analysis of Academic Master's Students' Types of Learning Engagement and Its Relationship with Mental Health from a Big Data Perspective

DOI: https://doi.org/10.62517/jhet.202515454

Author(s)

Mei Ma1,2, Yuan Wang1,2,*, Ru Wang3

Affiliation(s)

1College of Educational Science, Xinjiang Normal University, Urumqi, China 2Center for Xinjiang Higher Education Development Studies, Xinjiang Normal University, Urumugi, China 3Chongqing College of Foreign Languages and Foreign Affairs, Chongqing, China *Corresponding Author

Abstract

This study aims to explore the types and characteristics of academic master's students' learning engagement from a big data perspective, integrating mental health insights to provide a comprehensive understanding of students' learning states. As graduate education expands, leveraging big data to capture students' diverse characteristics has become crucial for enhancing educational quality. The research sampled 3,112 academic master's students, employing learning engagement theory and second-order cluster analysis to examine behavioral, cognitive, and emotional engagement, alongside mental health indicators such as anxiety, self-esteem, and resilience. Behavioral engagement was measured by learning time, academic discussion participation, and resource usage; cognitive engagement by thinking patterns and learning strategies; and emotional engagement by learning attitudes and academic enthusiasm. The analysis revealed five distinct engagement types: comprehensive, emotion-driven, moderate, passive avoidance, and limited participation. Comprehensive engagement students excelled across all dimensions with strong mental health; emotion-driven students exhibited high emotional investment driving their engagement; moderate engagement students showed balanced performance; passive avoidance students scored low on all engagement metrics with high anxiety and unmet psychological needs; and limited participation students demonstrated weak cognitive and emotional engagement with partial involvement in learning activities. In conclusion, academic master's students exhibit significant variations in learning engagement types, some of which are closely tied to mental health challenges. Universities should recognize these differences, prioritize mental health support, and implement tailored interventions to foster positive changes and improve graduate education quality.

Keywords

Academic Master's Students; Learning Engagement; Cluster Analysis; Mental Health; Big Data

References

[1]Yu, Y., & Lin, G. (2022). Digital technology empowers the high-quality development of graduate education: What is possible and what can be done. Chinese Higher Education Research, 11, 53-60. [2]Wang, Z. J., Zhang, W., & Qiao, W. F. (2024). AI empowers graduate education management: GROM theory construction. Degree and Graduate Education, 12, 1-8. [3]Wang, Z. J. (n.d.). Theory and methods of degree and graduate education evaluation. Beijing: Beijing Institute of Technology Press, 90. [4]Jiang, Y. (2013). Reflection on the intrinsic motivational mechanism of full-time master's students' academic research. Southwest University. [5]Zhu, X. Y. (2012). Cultivating the research capabilities of outstanding academic master's students for innovation. Graduate Education Research, 04, 50-53. [6]Niu, J. J., & Zhou, W. H. (2021). Who is more willing to pursue a PhD: An analysis of factors influencing the PhD intentions of academic master's students. Chinese Higher Education Research, 04, 82-88. [7]Su, C. Y. (2021). An empirical study on the research engagement of academic master's students. Shenyang Normal University. [8]Ni, J., Bai, Y., Chen, W. Y., et al. (2021). Study on the types and characteristics of learning engagement among undergraduates in independent medical colleges. Chinese Health Industry Management, 38(08), 615-619. [9]Wang, S. (2019). A study on the types of learning engagement behaviors among Chinese research university undergraduates. Jiangsu Higher Education, 04, 32-41. [10]Wang, Y. S. (2020). A study on the learning engagement of general education courses from a typological perspective: An empirical analysis based on a "Double First-Class" university. Journal of the National Academy of Educational Administration, 03, 78-85. [11]Fu, C. Z., Liu, Y. L., Qu, Q. F., et al. (2017). Typological analysis of university students' learning states and the role of quality assurance. Fudan Education Forum, 15(05), 42-49. [12]Li, Y. Y. (2013). A study on the classification of university students from the perspective of their engagement. Huazhong University of Science and Technology. [13]Wei, S. G., Du, X., & Chen, M. (2020). A typological analysis of undergraduate experiences and learning outcomes in research universities: A case study of H University. Chinese Higher Education Research, 01, 49-56. [14]Fredricks, A. J. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109. [15]Cai, J. (2018). The impact of the guiding relationship on doctoral students' research engagement. Nanjing University. [16]Xu, C. H. (2020). Model and intervention strategies for the factors influencing learning engagement in MOOC-based blended learning. Shaanxi Normal University. [17]Liu, H., Lu, H., Ruan, J. H., et al. (2015). Precision marketing segmentation model based on "user portraits". Silk, 52(12), 37-42+47. [18]Krause, K. L. (2005). Understanding and promoting student engagement in university learning communities. Paper presented as keynote address: Engaged, Inert or Otherwise Occupied, 21-22. [19]Rogers, C. R., et al. (2004). Client-Centered Therapy (Chinese translation). Beijing: Renmin University Press. [20]Bao, W. (2010). The unfinished transformation: Talent cultivation and student development in the popularization stage of higher education in the capital. Peking University Education Review, 8(01), 27-44+189. [21]Zhang, C., & Cui, Y. Y. (2014). Research on graduate students' mental health issues and countermeasures. Chinese Medical Herald, 11(03), 141-143.