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51.024 - Educational Data Mining: Affordances and Constraints in the Analysis of Behavior and Performance in STEM
Sun, April 7, 3:40 to 5:10pm, Sheraton Centre Toronto Hotel, Floor: Mezzanine, Pine EastSession Type: Symposium
Abstract
As the ability to collect repositories of data grows, so does the need to manage and interpret these oceans of data. Although there has been a surge in computer-based techniques for uncovering patterns within repositories, educational researchers have been slower to embrace them. This symposium showcases four investigations demonstrating the potential of data mining techniques including relationship mining, prediction models, and social network analysis to generate insights about learning and performance. These presentations focus on data-mining in relation to self-regulation, problem-solving, and reasoning in fields of biology, calculus, engineering, and medicine. The goals of the presenters and discussant are not only to demonstrate the potential of data-mining in education research, but also the challenges and issues it presents.
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Chair
Papers
Theory-Driven Data Mining to Understand Self-Regulated Learning Processing in a Higher Education Biology Course - Jeff A. Greene, University of North Carolina - Chapel Hill; Christopher J. Urban, University of North Carolina - Chapel Hill; Robert D Plumley, University of North Carolina - Chapel Hill; Matthew L. Bernacki, University of North Carolina Chapel Hill; Kathleen M. Gates, University of North Carolina - Chapel Hill; Kelly Hogan, University of North Carolina - Chapel Hill; Cynthia Demetriou, University of North Carolina- Chapel Hill; A. T. Panter, University of North Carolina - Chapel Hill
Examining the Power of Multiple Data Sources in Predicting Academic Achievement in Undergraduate STEM Courses - Wonjoon Hong, University of Nevada - Las Vegas; Matthew L. Bernacki, University of North Carolina Chapel Hill
Finding Patterns Within Patterning: Data Mining of Relational Reasoning Utterances in Engineering Design - Sophie Jablansky, University of Maryland - College Park; Patricia A. Alexander, University of Maryland - College Park; Linda Schmidt
Learning Analytics as a Tool for Understanding Clinical Reasoning - Susanne P. Lajoie, McGill University; Eric G. Poitras, University of Utah