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Applying Complexity Frameworks to Self-Regulated Learning Research: From Theoretical Models to Practical Tools

Thu, April 24, 9:50 to 11:20am MDT (9:50 to 11:20am MDT), The Colorado Convention Center, Floor: Meeting Room Level, Room 103

Session Type: Symposium

Abstract

Self-regulated learning (SRL) is a dynamic and temporal process that improves performance and academic outcomes. However, given the dynamic and non-linear complexities of SRL, it is highly non-trivial challenge to capture and measure these processes. As this symposium will highlight, the combination of trace data and complex science frameworks addresses these challenges but still require more research as they remain highly exploratory at present. This symposium features two empirical papers examining SRL using trace data to measure learning engagement across single lessons and week-long courses. Additionally, we feature two conceptual papers that examine the theoretical implications for a complexity framework when measuring metacognition within teams and a new proposed analytical approach to examining the emergence of self-regulated learning processes.

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