Trace-based micro-analytic measurement of self-regulated learning processes

Melody Siadaty, Dragan Gasevic, Marek Hatala

Research output: Contribution to journalArticlepeer-review

Abstract

To keep pace with today’s rapidly growing knowledge-driven society, productive self-regulation of one’s learning processes are essential. We introduce and discuss a trace-based measurement protocol to measure the effects of scaffolding interventions on self-regulated learning (SRL) processes. It guides tracing of learners’ actions in a learning environment on the fly and translates these data into indicators of engagement in SRL processes that reflect learners’ use of scaffolding interventions and contingencies between those events. Graphs of users’ learning actions in a learning environment are produced. Our trace-based protocol offers a new methodological approach to investigating SRL and new ways to examine factors that affect learners’ use of self-regulatory processes in technology-enhanced learning environments. Our application of the protocol was described in a study about Learn-B, a learning environment for SRL in the workplace. The findings of the work presented in this paper indicate that future research can gain substantially by using learning analytics based on users’ trace data and merging them with other quantitative and qualitative techniques for researching SRL beliefs and processes.
Original languageEnglish
Pages (from-to)183-214
Number of pages38
JournalJournal of Learning Analytics
Volume3
Issue number1
Early online date2016
DOIs
Publication statusE-pub ahead of print - 2016

Keywords

  • self‐regulated learning
  • micro‐level process
  • trace‐based methodologies
  • learning analytics
  • graph theory
  • learning technology

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