Analysis of the Effects of the "Pedagogy - Space - Technology" Framework on University Student’s Learning Efficiency
Open accessAbstract
In the background of the global COVID-19 pandemic, the popularization of intelligent online teaching platforms dominated by advanced teaching technologies is accelerated. Pedagogy-Space-Technology (PST) framework learning space provides students high-quality teaching resources, the overall learning efficiency of students is dissatisfying because students lack intrinsic motivation to learn. Based on existing “intelligent teaching platforms of the PST framework” and combining with the principle of improving internal drive force of students through external guiding strategies, an experiment was designed in this study. The course Technical Analysis of the Financial Markets launched as an open online optional course for Chinese six undergraduate universities in 2021, and was chosen as the research example. Experimental data in 16 weeks were collected to analyze the effects of the PST framework learning space on overall intrinsic learning ability, staged academic performances, and online learning behaviors and improvement degree of intrinsic independent learning ability of students. The students’ independent learning ability is generally improved, but their attitude improvement is not obvious. The academic performances of students are generally improved, especially for students with low intrinsic independent learning ability. The online learning behaviors of students with low intrinsic independent learning ability are not dissatisfying, but their independent learning ability is improved significantly. In this study, the relationship between the PST framework learning space and learning efficiency of students was disclosed from the perspective of the internal-driving and external-guiding teaching model. The obtained conclusion provides a way for university teachers to provide students with emotional support, design multiple teaching technological activity combined strategies, offer external guidance with timely and high-efficiency feedbacks, and improve learning efficiency.
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The breadth and depth of SDT integration with emerging educational technologies is limited and needs further expansion.
“the breadth and depth of its integration with emerging educational technologies (such as AI-based personalized learning tools, gamification, and digital platforms) need to be further expanded, and their role in supporting or hindering independent learning needs to be explored more [8].”
From Mapping the Research Landscape: A Comprehensive Bibliometric Analysis of Self-Determination Theory in Higher Education (2024) · cites this paper as [8]
The State of the art section discusses internal motivation and technology-supported learning in the PST framework, while the Conclusion proposes refining strategies to motivate students. Neither section addresses self-determination theory (SDT) or assesses the breadth and depth of its integration with emerging educational technologies. Searches for SDT, gamification, and that proposed research gap found no supporting passage in our copy. (LLM, conf=0.75)
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Lin, C., Zhang, Y., Wang, L., & Wu, J. (2024) · Proceedings of the 2024 2nd International Conference on Information Education and Artificial Intelligence · doi:10.1145/3724504.3724576
- Authors
- Bi, J., & Chen, J.
- Affiliations
- Zhejiang Wanli University (CN)
- Venue
- International Journal of Emerging Technologies in Learning (iJET)
- Published
- 2022-08-17
- Volume
- 17
- Issue
- 15
- Pages
- 219-232
- ISSN
- 1863-0383 (ISSN-L)
- OpenAlex
- W4292117687
- Cited by
- 4
- References
- 0
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 1
- FWCI
- 2.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- International Journal of Emerging Technologies in Learning (iJET) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Science and Technology Program of Zhejiang Province
- Topics
- Social Sciences › Education › Online and Blended Learning
- Physical Sciences › Computer Science › Information Systems › Educational Technology and E-Learning
- Social Sciences › Education › Technology-Enhanced Education Studies
- Keywords
- learning efficiency, intrinsic motivation, independent learning skills, online learning behavior, academic performance, COVID-19 remote learning
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