Interdisciplinary research has demonstrated that learning and problem solving with advanced learning technologies (ALTs) such as intelligent tutoring systems, simulations, hypermedia, serious games, and virtual reality can promote and foster the development of 21st century skills (e.g., collaboration, problem solving, self-regulated learning) by measuring and using the interactions between cognitive, affective, metacognitive, motivational, and social (CAMMS) processes. Interdisciplinary researchers focused on self-regulated learning (SRL) have developed several theoretical models which model students' CAMMS processes and their learning behaviors. However, when empirically testing these models, researchers face complicated methodological decisions around modeling, measuring, processing, and analyzing student data. Many of these questions come from examining the interactions of the various processes in relation to overall learning instead of the isolated examination of each process independent of one another. This is especially true when looking across CAMMS (e.g., metacognitive regulation and motivational engagement) and not just within a single CAMMS process (e.g., metacognitive monitoring and control). For instance, metacognition and engagement are often discussed informally in conjunction with one another, however, many models of SRL provide a cursory mention of this relationship at best, if at all. Therefore, comprehensive models of both metacognition and engagement are needed to define future work within this field. Critically, this modeling needs to be specific about the component operationalizations and interactions, the dynamics of the components, and the conditions by which metacognition and engagement may interact. This may be accomplished by utilizing a combination of online dynamic multimodal data captured during learning, reasoning, and problem solving (revealing the what, when, and for how long), and traditional offline self-reports (revealing the why) as we measure, model, and (in the future) simulate learners and their metacognitive and engagement processes.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2898 |
| Date | 01 January 2023 |
| Creators | Wiedbusch, Megan |
| 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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