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Data Mining and Self-Regulated Learning: Aligning Constructs With Measurement
Mon, April 20, 4:05 to 5:35pm, Virtual RoomSession Type: Symposium
Abstract
The question of how students strategically regulate their learning has been a source of academic intrigue for decades. Although self-regulated learning (SRL) is conceptualized as a dynamic interaction between individual and contextual processes unfolding over the course of learning or problem-solving activities, SRL research relies mainly on self-report data. As a result, there are increasing calls to leverage data mining techniques, including sequential and differential pattern mining and machine learning algorithms, to measure SRL and predict achievement. This symposium explores the utility of data mining techniques for tracing SRL processes in individual or team settings, with implications for researchers and organizational stakeholders.
Sub Unit
Chair
Papers
Socially Shared Regulation and Relational Reasoning in Engineering Design Teams: A Microgenetic Approach - Sophie Jablansky, University of Maryland - College Park; Patricia A. Alexander, University of Maryland - College Park; Linda Schmidt
The Relative Importance of Self-Regulated Learning, Emotions, and Cognitive Load in Clinical Reasoning - Susanne P. Lajoie, McGill University; Shan Li, McGill University; Juan Zheng, McGill University; Tianshu Li, McGill University; Alejandra Ruiz-Segura, McGill University; Kirsten Nynych, McGill University
Leveraging Campus Data, Learning Theory, and Educational Data Mining to Predict Achievement Before Students Begin to Fail - Matthew L. Bernacki, University of North Carolina Chapel Hill; Christopher J. Urban, University of North Carolina - Chapel Hill; Robert D Plumley, University of North Carolina - Chapel Hill; Lan Luo, University of North Carolina - Chapel Hill; Kathleen M. Gates, University of North Carolina - Chapel Hill; Abigail Panter, University of North Carolina - Chapel Hill; Jeff A. Greene, University of North Carolina - Chapel Hill
Two Methods of Extracting Campus Data, Interpreting Learning Management System Events That Reflect Learning, and Predicting Achievement - Wonjoon Hong, University of Nevada - Las Vegas; Matthew L. Bernacki, University of North Carolina Chapel Hill