The Causal Model and Development Strategies of Personalized Education in Shanxi University, China
คำสำคัญ:
Personalized Education, Students' Learning Ability Factors, Universities' Instructional Management Factors, Educational Management Strategiesบทคัดย่อ
This dissertation aims to analyze a causal model of the influencing factors of Personalized Education in universities in Shanxi Province, China, and based on the results of this analysis, to discuss and propose strategies for the development of personalized education for contemporary university students. The research adopts a mixed-method approach, combining both quantitative and qualitative research methodologies. Data were collected using surveys and interviews, with a sample of 368 university students from various higher education institutions in Shanxi Province. The study examines four key factors—students, teachers, the university education strategies, and family education strategies—and explores their influence on personalized education.
The research findings indicate that students' individual learning abilities and needs are crucial determinants in the success of personalized education. Furthermore, the teaching practices and pedagogical competencies of instructors play a significant role in the development of personalized education. The structure and effectiveness of the educational management system within universities have a substantial impact on the level of personalized education offered. Additionally, family education is found to be an important factor influencing students' personalized learning experiences.
Based on the above findings, the dissertation identifies the current challenges and status of personalized education in Shanxi Province's universities. It then proposes specific strategies aimed at improving and promoting personalized education in higher education institutions, with a focus on the four influencing factors—students, teachers, the education management system, and family education.
เอกสารอ้างอิง
Bulger, M. (2016). Personalized learning: The conversations we’re not having. Data & Society Working Paper, 1–29.
Dede, C. (2006). A seismic shift in epistemology. EDUCAUSE Review, 41(3), 80–81.
Guo, S., & Li, Q. (2018). Institutional barriers to personalized learning in Chinese higher education. Higher Education Research & Development, 37(5), 963–977. https://doi.org/10.1080/07294360.2018.1456519
Holmes, K., & Prieto-Rodriguez, E. (2018). Student and staff perceptions of a learning management system for blended learning in higher education. Australasian Journal of Educational Technology, 34(3), 72–84. https://doi.org/10.14742/ajet.2993
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Boston, MA: Center for Curriculum Redesign.
Hu, Y., & Qian, M. (2016). Family involvement and its influence on student achievement in Chinese higher education. Journal of Educational Research, 109(3), 231–240. https://doi.org/10.1080/00220671.2014.992157
Kolmos, A., & Holgaard, J. E. (2020). Impact of problem-based learning in engineering education: A review of the research. European Journal of Engineering Education, 45(5), 779–795. https://doi.org/10.1080/03043797.2019.1628944
Li, X., & Chen, Y. (2019). Adaptive learning technologies and equity in Chinese higher education. Educational Technology Research and Development, 67(4), 913–930. https://doi.org/10.1007/s11423-019-09671-3
Liu, H., & Xu, J. (2021). Teacher professional development for personalized learning in Chinese universities. Asia Pacific Education Review, 22(4), 623–636. https://doi.org/10.1007/s12564-021-09692-8
Liu, J., & Zhao, Y. (2019). Barriers to innovation in Chinese higher education: The case of personalized learning. Frontiers of Education in China, 14(2), 168–185. https://doi.org/10.1007/s11516-019-0008-4
Means, B., Padilla, C., DeBarger, A., & Bakia, M. (2013). Implementing data-informed decision making in schools: Teacher access, supports and use. U.S. Department of Education.
OECD. (2019). OECD future of education and skills 2030: OECD learning compass 2030. OECD Publishing.
Pane, J. F., Steiner, E. D., Baird, M. D., & Hamilton, L. S. (2015). Continued progress: Promising evidence on personalized learning. Educational Evaluation and Policy Analysis, 37(4), 437–458. https://doi.org/10.3102/0162373714558867
Pane, J. F., Steiner, E. D., Baird, M. D., & Hamilton, L. S. (2017). Informing progress: Insights on personalized learning implementation and effects. RAND Corporation.
Walkington, C., & Bernacki, M. L. (2020). Personalized learning in mathematics. Journal of Research on Mathematics Education, 51(4), 401–416. https://doi.org/10.5951/jresematheduc-2020-0015
Wang, J., & Mao, S. (2021). Family involvement and student learning outcomes in China: A critical review and future directions. Asia Pacific Education Review, 22(3), 437–449. https://doi.org/10.1007/s12564-021-09698-2
Zhao, Y. (2015). Who’s afraid of the big bad dragon? Why China has the best (and worst) education system in the world. Jossey-Bass.
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