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Self-regulation during e-learning: using behavioural evidence from navigation log files.

The current paper examined the relationship between perceived characteristics of the learning environment in an e-module in relation to test performance among a group of e-learners. Using structural equation modelling, the relationship between these variables is further explored in terms of the prop...

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Published in:Journal of Computer Assisted Learning Vol. 30; no. 3; pp. 272 - 285
Main Authors: Jeske, D., Backhaus, J., Stamov Roßnagel, C.
Format: research tables/charts Journal Article
Published: Wiley-Blackwell Jun2014
Online Access:View this record in EBSCOhost
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      dt: Jun2014
      vid: 30
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1111/jcal.12045
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        atl: Self-regulation during e-learning: using behavioural evidence from navigation log files.
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        au:
          Jeske, D.
          Backhaus, J.
          Stamov Roßnagel, C.
        affil: Jacobs Center on Lifelong Learning and Institutional Development, Jacobs University Bremen
      sug:
        subj:
          Self Regulation
          Computer-Assisted Instruction
          Learning Environment
          Learning
          Human
          Male
          Female
          Structural Equation Modeling
          Computerized Educational Testing
          World Wide Web
          Computer Literacy
          Motivation
          Adult
          Middle Age
          Aged
          Data Analysis Software
          Descriptive Statistics
          Chi Square Test
          Confidence Intervals
          Funding Source
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Aged: 65+ years
          Male
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      ab: The current paper examined the relationship between perceived characteristics of the learning environment in an e-module in relation to test performance among a group of e-learners. Using structural equation modelling, the relationship between these variables is further explored in terms of the proposed double mediation as outlined by Ning and Downing. These authors initially proposed that motivation and self-regulation strategies are mediators between the perception of the learning environment and performance. In our replication and extension study, we substituted self-reported self-regulation with behavioural indicators of self-regulation using navigation log files and focused on test-taking rather than general motivation. We proposed that navigational patterns captured using log files can also help deduce self-regulation in e-modules and provide information in the absence of self-reports. Path analyses provide partial support for our navigational hypotheses and the model. Implications of our results for the use of e-module data and conclusions based on navigation are discussed.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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