Exploring the Impact of Generative Artificial Intelligence Scaffolding on University Students’ Collaborative Knowledge Building
Journal: Journal of Higher Education Research DOI: 10.32629/jher.v5i6.3391
Abstract
Collaborative learning is a significant form of learning in the era of intelligence. Generative artificial intelligence (GAI) holds advantages in providing personalized support for collaborative learning. However, current understanding of how GAI influences university students' collaborative knowledge building is limited. This study aims to address this research gap by empirically examining fthe role of GAI scaffolding in university students' collaborative knowledge building. Fifty-four undergraduates were recruited and divided into 18 groups for the experiment, with 9 groups engaging in collaborative learning supported by GAI scaffolding and the other 9 groups in a conventional technology-supported environment. All participants completed the same collaborative learning task. The results indicate that GAI scaffolding can offer cognitive and metacognitive support, effectively enhancing learners' collaborative knowledge building competences. The primary contribution of this exploratory study lies in providing support for the design and implementation of GAI scaffolding in collaborative learning.
Keywords
generative artificial intelligence; scaffolding; collaborative learning; collaborative knowledge building
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[2] Zheng, L., Kinshuk, Fan, Y., & Long, M. (2023). The impacts of the comprehensive learning analytics approach on learning performance in online collaborative learning. Education and Information Technologies, 28(12), 16863-16886.
[3] Zheng, L., Zhong, L., Niu, J., Long, M., & Zhao, J. (2021). Effects of personalized intervention on collaborative knowledge building, group performance, socially shared metacognitive regulation, and cognitive load in computer-supported collaborative learning. Educational Technology & Society, 24(3), 174-193.
[4] Mei, J., Chen, W., Li, B., Li, S., & Zhang, J. (2023). Visualization of computersupported collaborative learning models in the context of multimodal data analysis. 3c Empresa: investigación y pensamiento crítico, 12(1), 87-109.
[5] Kollar, I., Wecker, C., & Fischer, F. (2018). Scaffolding and scripting (computer-supported) collaborative learning. In International handbook of the learning sciences (pp. 340-350). Routledge.
[6] Dillenbourg P , Jermann P . Designing integrative scripts[M]. Springer US, 2007.
[7] Kim D , Lim C . Promoting socially shared metacognitive regulation in collaborative project-based learning: a framework for the design of structured guidance[J]. Teaching in Higher Education, 2017:1-18.
[8] Molenaar I, Roda C, Boxtel C, et al. Dynamic Scaffolding of Socially Regulated Learning in a Computer-based Learning Environment[J]. Computers & Education, 2012, 59: 515–523.
[9] O’Donnell, A. M., & Hmelo-Silver, C. E. (2013). Introduction: What is collaborative learning?: An overview. The international handbook of collaborative learning, 1-15.
[10] Baker, M. J. (2015). Collaboration in collaborative learning. Interaction studies, 16(3), 451-473.
[11] Wang, M. J. (2010). Online collaboration and offline interaction between students using asynchronous tools in blended learning. Australasian Journal of Educational Technology, 26(6).
[12] Redeș, A. (2016). Collaborative learning and teaching in practice. Educația Plus, 16(2), 334-345.
[13] Lan, Y. F., Tsai, P. W., Yang, S. H., & Hung, C. L. (2012). Comparing the social knowledge construction behavioral patterns of problem-based online asynchronous discussion in e/m-learning environments. Computers & Education, 59(4), 1122-1135.
[14] Gu, X., Shao, Y., Guo, X., & Lim, C. P. (2015). Designing a role structure to engage students in computer-supported collaborative learning. The Internet and Higher Education, 24, 13-20.
[15] Le, H., Janssen, J., & Wubbels, T. (2018). Collaborative learning practices: teacher and student perceived obstacles to effective student collaboration. Cambridge Journal of education, 48(1), 103-122.
[16] Newman, S., & Latifi, A. (2021). Vygotsky, education, and teacher education. Journal of Education for Teaching, 47(1), 4-17.
[17] Mermelshtine, R. (2017). Parent–child learning interactions: A review of the literature on scaffolding. British Journal of Educational Psychology, 87(2), 241-254.
[18] Smagorinsky, P. (2018). Deconflating the ZPD and instructional scaffolding: Retranslating and reconceiving the zone of proximal development as the zone of next development. Learning, culture and social interaction, 16, 70-75.
[19] Ahmadi Safa, M., & Motaghi, F. (2024). Cognitive vs. metacognitive scaffolding strategies and EFL learners’ listening comprehension development. Language Teaching Research, 28(3), 987-1010.
[20] Razaghi, M., Bagheri, M. S., & Yamini, M. (2019). The Impact of Cognitive Scaffolding on Iranian EFL Learners' Speaking Skill. International Journal of Instruction, 12(4), 95-112.
[21] Valencia-Vallejo, N., López-Vargas, O., & Sanabria-Rodríguez, L. (2019). Effect of a Metacognitive Scaffolding on Self-Efficacy, Metacognition, and Achievement in E-Learning Environments. Knowledge Management & E-Learning, 11(1), 1-19.
[22] Hod Y, Katz S, Eagan B. Refining qualitative ethnographies using Epistemic Network Analysis: A study of socioemotional learning dimensions in a Humanistic Knowledge Building Community[J]. Computers & Education, 2020, 156: 103943.
[23] Shaffer D. Operationalizing identity: studying changing selves in experimental learning environments[J]. The Journal of Experimental Education, 2021, 89: 1-8.
[24] SHAFFER D W, COLLIER W, RUIS A R. A tutorial on epistemic network analysis: analyzing the structure of connections in cognitive, social, and interaction data[J]. Journal of learning analytics, 2016, 3（3）:9-45
[25] Swiecki Z, Ruis A, Farrell C, et al. Assessing Individual Contributions to Collaborative Problem Solving: A Network Analysis Approach[J]. Computers in Human Behavior, 2019, 104.
[26] Draper, D. C. (2015). Collaborative instructional strategies to enhance knowledge convergence. American Journal of Distance Education, 29(2), 109-125.
[27] Park, H., Ko, H., Lee, Y. T. T., Feng, S., Witherell, P., & Cho, H. (2023). Collaborative knowledge management to identify data analytics opportunities in additive manufacturing. Journal of Intelligent Manufacturing, 1-24.
[28] Yücel, Ü. A., & Usluel, Y. K. (2016). Knowledge building and the quantity, content and quality of the interaction and participation of students in an online collaborative learning environment. Computers & Education, 97, 31-48.
[29] Lin C-L, Hou H-T, Tsai C-C. Analyzing the Social Knowledge Construction and Online Searching Behavior of High School Learners During a Collaborative Problem Solving Learning Activity: a Multi-Dimensional Behavioral Pattern Analysis[J]. The Asia-Pacific Education Researcher, 2016, 25(5): 1-14.
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