Recognizing and measuring self-regulated learning in a mobile learning environment

Li SHA, Chee Kit LOOI, Wenli CHEN, Peter SEOW, Lung-Hsiang WONG

Research output: Contribution to journalArticlespeer-review

107 Citations (Scopus)

Abstract

With the realization that more research is needed to explore external factors (e.g.; pedagogy, parental involvement in the context of K-12 learning) and internal factors (e.g.; prior knowledge, motivation) underlying student-centered mobile learning, the present study conceptually and empirically explores how the theories and methodologies of self-regulated learning (SRL) can help us analyze and understand the processes of mobile learning. The empirical data collected from two elementary science classes in Singapore indicates that the analytical SRL model of mobile learning proposed in this study can illuminate the relationships between three aspects of mobile learning: students' self-reports of psychological processes, patterns of online learning behavior in the mobile learning environment (MLE), and learning achievement. Statistical analyses produce three main findings. First, student motivation in this case can account for whether and to what degree the students can actively engage in mobile learning activities metacognitively, motivationally, and behaviorally. Second, the effect of students' self-reported motivation on their learning achievement is mediated by their behavioral engagement in a pre-designed activity in the MLE. Third, students' perception of parental autonomy support is not only associated with their motivation in school learning, but also associated with their actual behaviors in self-regulating their learning. Copyright © 2011 Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)718-728
JournalComputers in Human Behavior
Volume28
Issue number2
Early online dateDec 2011
DOIs
Publication statusPublished - Mar 2012

Citation

Sha, L., Looi, C.-K., Chen, W., Seow, P., & Wong, L.-H. (2012). Recognizing and measuring self-regulated learning in a mobile learning environment. Computers in Human Behavior, 28(2), 718-728. doi: 10.1016/j.chb.2011.11.019

Keywords

  • Mobile learning
  • Self-regulated learning
  • Motivation and metacognition
  • Elementary science learning

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