51. Examining trace data to explore self-regulated learning
- Author
-
Philip H. Winne, Allyson F. Hadwin, Dianne Jamieson-Noel, Jillianne Code, and John C. Nesbit
- Subjects
education ,Metacognition ,Contrast (statistics) ,computer.software_genre ,Education ,Metacognitive Monitoring ,Mathematics education ,Data mining ,Construct (philosophy) ,Adaptation (computer science) ,Psychology ,Self-regulated learning ,computer ,TRACE (psycholinguistics) - Abstract
This exploratory case study examined in depth the studying activities of eight students across two studying episodes, and compared traces of actual studying activities to self-reports of self-regulated learning. Students participated in a 2-hour activity using our gStudy software to complete a course assignment. We used log file data to construct profiles of self-regulated learning activity in four ways: (a) frequency of studying events, (b) patterns of studying activity, (c) timing and sequencing of events, and (d) content analyses of students’ notes and summaries. Findings indicate that students’ self-reports may not calibrate to actual studying activity. Analyses of log file traces of studying activities provide important information for defining strategies and sequences of fine-grained studying actions. We contrast these analytic methods and illustrate how trace-based profiles of students’ self-regulated studying inform models of metacognitive monitoring, evaluation, and self-regulated adaptation.
- Published
- 2007