Artificial Intelligence and Personalized English Learning Plan: A Survey Study Based on Applied Undergraduate Programs
DOI:
https://doi.org/10.65170/jtr.v1i2.14Keywords:
Learning Motivation Theory, Cross-Line Campus, AI, Personalized English Learning PlanAbstract
Against the backdrop of the deep integration of artificial intelligence (AI) technology and the field of education, as well as the gradual popularization of cross-campus teaching models, personalized English learning has become a key approach to enhance students’ learning efficiency. Based on core learning motivation theories such as Self-determination theory and Achievement Motivation theory, this study, which takes 192 students from Geely University as the research sample, systematically analyzes the current status, motivation characteristics, and path dilemmas of college students using AI tools for English learning in the cross-campus environment. These findings show that the study in this paper can, to some extent, recommend the most effective AI-empowered personalized English learning plan that adapts to cross-campus teaching scenarios, and provide practical reference and decision-making base for English teaching reform in colleges and universities in China.
References
1. Bandura, A. (1997). Self-efficacy: The exercise of control. W H Freeman/Times Books/ Henry Holt & Co.
2. Cole, M., John-Steiner, V., Scribner, S., & Souberman, E. (Eds.). (1978). Mind in society: The develop-ment of higher psychological processes. L. S. Vygotsky. Harvard U Press.
3. Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research. Sage publications.
4. Davis, F.D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q., 13, 319-340. https://doi.org/10.2307/249008 DOI: https://doi.org/10.2307/249008
5. Deci, E. L., & Ryan, R. M. (2000). The “What” and “Why” of Goal Pursuits: Human Needs and the Self-Determination of Behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01 DOI: https://doi.org/10.1207/S15327965PLI1104_01
6. Elliot, A. J., & McGregor, H. A. (2001). A 2 X 2 achievement goal framework. Journal of personality and social psychology, 80(3), 501–519. https://doi.org/10.1037/0022-3514.80.3.501 DOI: https://doi.org/10.1037/0022-3514.80.3.501
7. Hattie, J. (2012). Visible Learning for Teachers: Maximizing Impact on Learning (1st ed.). Routledge. https://doi.org/10.4324/9780203181522 DOI: https://doi.org/10.4324/9780203181522
8. Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020, January 1). Vision, challenges, roles and re-search issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelli-gence. Elsevier B.V. https://doi.org/10.1016/j.caeai.2020.100001 DOI: https://doi.org/10.1016/j.caeai.2020.100001
9. Krippendorff, K. (2018). Content analysis: An introduction to its methodology. Sage publications. DOI: https://doi.org/10.4135/9781071878781
10. Luckin, R., & Holmes, W. (2016). Intelligence Unleashed: An argument for AI in Education.
11. McClelland, D. C. (1985). Human motivation. Glenview, IL: Scott, Foresman.
12. Oxford, R.L. (2016). Teaching and Researching Language Learning Strategies: Self-Regulation in Context, Second Edition (1st ed.). Routledge. https://doi.org/10.4324/9781315719146 DOI: https://doi.org/10.4324/9781315719146
13. Panadero, E., Klug, J., & Järvelä, S. (2016). Third wave of measurement in the self-regulated learning field: when measurement and intervention come hand in hand. Scandinavian Journal of Educa-tional Research, 60(6), 723–735. https://doi.org/10.1080/00313831.2015.1066436 DOI: https://doi.org/10.1080/00313831.2015.1066436
14. Pintrich, P. R. (2003). A Motivational Science Perspective on the Role of Student Motivation in Learn-ing and Teaching Contexts. Journal of Educational Psychology, 95(4), 667–686. https://doi.org/10.1037/0022-0663.95.4.667 DOI: https://doi.org/10.1037/0022-0663.95.4.667
15. Reeve, J. (2012). A Self-determination Theory Perspective on Student Engagement. In: Christenson, S., Reschly, A., Wylie, C. (eds) Handbook of Research on Student Engagement. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-2018-7_7 DOI: https://doi.org/10.1007/978-1-4614-2018-7_7
16. Ryan, R. M., & Deci, E. L. (2020). Intrinsic and Extrinsic Motivation from a Self-Determination Theo-ry Perspective: Definitions, Theory, Practices, and Future Directions. Contemporary Educational Psychology, 61, https://doi.org/10.1016/j.cedpsych.2020.101860 DOI: https://doi.org/10.1016/j.cedpsych.2020.101860
17. Zimmerman, B. J. (2002). Becoming a Self-Regulated Learner: An Overview. Theory Into Prac-tice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2 DOI: https://doi.org/10.1207/s15430421tip4102_2
18. Zimmerman, B. J., & Schunk, D. H. (Eds.). (2011). Handbook of self-regulation of learning and per-formance. Routledge/Taylor & Francis Group.
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