Data Mining Methods for Assessing Self-Regulated Learning

Gautam Biswas, Ryan Baker, Luc Paquette

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter discusses the recent advancements and potential for advancement in leveraging data mining methods to study SRL behaviors and processes. It reviews how educational data mining (EDM), conducted on fine-grained data from learner interactions, can produce an understanding of SRL and the phenomena which compose it. Within the broad range of data mining methods used in educational domains, five stand out in their use to detect and study SRL: feature engineering, prediction modeling, sequence mining, cluster analysis, and correlation mining. The chapter defines each of these and their use in research projects. To capture the SRL behaviors, researchers require techniques for detecting key aspects of cognition, metacognition, affect, and motivation in the context of the learning task and the environment. Such analyses often rely upon identifying and assessing learners' cognitive skill proficiency, interpreting their action sequences in terms of learning strategies, detecting relevant aspects of their affect and engagement, and evaluating the students' success in accomplishing their current tasks.
Original languageEnglish (US)
Title of host publicationHandbook of Self-Regulation of Learning and Performance
EditorsDale H Schunk, Jeffrey A Greene
PublisherRoutledge
Pages388-403
Edition2
ISBN (Electronic)9781315697048
ISBN (Print)9781138903180, 9781138903197
DOIs
StatePublished - Sep 7 2017

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