Abstract

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.

Notes

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Graduation Date

2023

Semester

Spring

Advisor

Azevedo, Roger

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Department

School of Modeling, Simulation, and Training

Degree Program

Modeling & Simulation

Format

application/pdf

Identifier

CFE0009511; DP0027515

URL

https://purls.library.ucf.edu/go/DP0027515

Language

English

Release Date

May 2023

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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