Monitoring and depth of strategy use in computer-based learning environments for science and history
Datos Bibliográficos
| ID | 5263441 |
|---|---|
| Autores | Victor M Deekens (0000-0003-1889-8499, University of North Carolina at Chapel Hill North Carolina USA), Jeffrey A Greene (0000-0003-4145-1847, University of North Carolina at Chapel Hill North Carolina USA, autor de correspondencia), Nikki G Lobczowski (0000-0002-9018-2957, University of North Carolina at Chapel Hill North Carolina USA) |
| Año | 2018 |
| Volumen | 88 |
| Número | 1 |
| Páginas | 63-79 |
| Fecha de publicación | 2018-03-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | British Journal of Educational Psychology (JOURNAL) |
| Identificadores de la revista | ISSN: 0007-0998 • E-ISSN: 2044-8279 |
| Editorial | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/bjep.12174 |
| PMID | 28801957 |
| OpenAlex | W2744789409 |
| Idioma | EN |
| Citas recibidas | 17 |
| Referencias citadas | 45 |
BackgroundSelf-regulated learning (SRL) models position metacognitive monitoring as central to SRL processing and predictive of student learning outcomes (Winne & Hadwin, 2008; Zimmerman, 2000). A body of research evidence also indicates that depth of strategy use, ranging from surface to deep processing, is predictive of learning performance.AimsIn this study, we investigated the relationships among the frequency of metacognitive monitoring and the utilization of deep and surface-level strategies, and the connections between these SRL processes and learning outcomes across two academic domains, science and history.SampleThis was a secondary data analysis of two studies. The first study sample was 170 undergraduate students from a University in the south-eastern United States. The second study sample consisted of 40 US high school students in the same area.MethodsWe collected think-aloud protocol SRL and knowledge measure data and conducted both structural equation modelling and path analysis to investigate our research questions.ResultsFindings showed across both studies and two distinct academic domains, students who enacted more frequent monitoring also enacted more frequent deep strategies resulting in better performance on academic evaluations.ConclusionsThese findings suggest the importance of measuring not only what depth of strategies learners use, but also the degree to which they monitor their learning. Attention to both is needed in research and practice
Cognition · Cognitive science · Human–computer interaction · Machine learning · Mathematics education · Metacognition · Path analysis (statistics) · Protocol analysis · Sample (material) · Self-regulated learning · Think aloud protocol · Computer Science · Educational and Psychological Assessments · Innovative Teaching and Learning Methods · Psychology · Visual and Cognitive Learning Processes
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| Obras citantes distintas | 17 |
|---|---|
| Citas por año | 2,13 |
| Intervalo de citas | 2018 - 2026 (9) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 17 |