Chapter 21

Handbook of Learning Analytics
First Edition

Learning Analytics for Self-Regulated Learning

Philip H. Winne


The Winne-Hadwin (1998) model of self-regulated learning (SRL), elaborated by Winne’s (2011, in press) model of cognitive operations, provides a framework for conceptualizing key issues concerning kinds of data and analyses of data for generating learning analytics about SRL. Trace data are recommended as observable indicators that support valid inferences about a learner’s metacognitive monitoring and metacognitive control that constitute SRL. Characteristics of instrumentation for gathering ambient trace data via software learners can use to carry out everyday studying are described. Critical issues are discussed regarding what to trace about SRL, attributes of instrumentation for gathering ambient trace data, computational issues arising when analyzing trace and complementary data, the scheduling and delivery of learning analytics, and kinds of information to convey in learning analytics that support productive SRL.

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About this Chapter

Learning Analytics for Self-Regulated Learning

Book Title
Handbook of Learning Analytics

pp. 241-249




Society for Learning Analytics Research

Philip H. Winne

Author Affiliations
Faculty of Education, Simon Fraser University, Canada

Charles Lang1
George Siemens2
Alyssa Wise3
Dragan Gašević4

Editor Affiliations
1. Teachers College, Columbia University, USA
2. LINK Research Lab, University of Texas at Arlington, USA
3. Learning Analytics Research Network, New York University, USA
4. Schools of Education and Informatics, University of Edinburgh, UK

Founding Members
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