Game-based learning environments (GBLEs) can offer students with engaging interactive instructional materials while also providing a research platform to investigate the dynamics and intricacies of effective self-regulated learning (SRL). Past research has indicated learners are often unable to monitor and regulate their cognitive and metacognitive processes within GBLEs accurately and effectively on their own due mostly to the open-ended nature of these environments. The future design and development of GBLEs and embedded scaffolds, therefore, require a better understanding of the discrepancies between the affordances of GBLEs and the required use of SRL. Specifically, how to incorporate interdisciplinary theories and concepts outside of traditional educational, learning, and psychological sciences literature, how to utilize process data to measure SRL processes during interactions with instructional materials accounting for the dynamics of leaners' SRL, and how to improve SRL-driven scaffolds to be individualized and adaptive based on the level of agency GBLEs provide. Across four studies, this dissertation investigates learners' SRL while they learn about microbiology using CRYSTAL ISLAND, a GBLE, building upon each other by enhancing the type of data collected, analytical methodologies used, and applied theoretical models and theories. Specifically, this dissertation utilizes a combination of traditional statistical approaches (i.e., linear regression models), non-linear statistical approaches (i.e., growth modeling), and non-linear dynamical theory (NDST) approaches (aRQA) with process trace data to contribute to the field's current understanding of the dynamics and complexities of SRL. Furthermore, this dissertation examines how limited agency can act as an implicit scaffold during game-based learning to promote the use of SRL processes and increase learning outcomes.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2548 |
| Date | 01 January 2023 |
| Creators | Dever, Daryn |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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