Abstract
Several studies have attempted to capture and analyze the intersect of self-regulated learning (SRL) behaviors and agency (i.e., control over one’s own actions) during game-based learning. However, limited studies have attempted to theoretically ground or analytically evaluate these constructs in appropriate theoretical assumptions that can discuss and aptly analyze SRL. As such, this paper argues that complex systems theory, which refers to SRL as a system that is self-organizing, interaction dependent, and emergent, should be integrated into theoretical models of SRL and be analyzed using nonlinear dynamical systems theory techniques to fully capture how learners’ SRL behaviors can be captured and scaffolded during game-based learning. This paper guides future discussions and empirical research to understand how to better scaffold learners’ SRL behaviors using restricted agency during game-based learning by: (1) understanding scaffolding SRL during game-based learning; (2) reviewing studies that review the intersection of SRL, agency, and game-based learning; (3) discussing the limitations within the field; (4) defining and defend SRL according to complex systems theory; and (5) discussing the open challenges in theoretically, methodologically, and analytically applying complex systems theory to SRL.
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Acknowledgements
The research reported in the paper has been funded by the National Science Foundation (DRL #1661202 and DUE # 1761178). The authors would like to thank her dissertation advisor and committee members, and members of UCF’s SMART Lab and NCSU’s IntelliMedia Group for their numerous contributions. The authors would also like to thank Drs. Winne and Biswas for their mentoring of Ms. Dever through the AIEd Doctoral Consortium process.
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Dever, D.A., Azevedo, R. (2022). Scaffolding Self-regulated Learning in Game-Based Learning Environments Based on Complex Systems Theory. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_7
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