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Modeling Change in Learning Strategies throughout Higher Education: A Multi-Indicator Latent Growth Perspective

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  • Liesje Coertjens
  • Vincent Donche
  • Sven De Maeyer
  • Gert Vanthournout
  • Peter Van Petegem

Abstract

The change in learning strategies during higher education is an important topic of research in the Student Approaches to Learning field. Although the studies on this topic are increasingly longitudinal, analyses have continued to rely primarily on traditional statistical methods. The present research is innovative in the way it uses a multi-indicator latent growth analysis in order to more accurately estimate the general and differential development in learning strategy scales. Moreover, the predictive strength of the latent growth models are estimated. The sample consists of one cohort of Flemish University College students, 245 of whom participated in the three measurement waves by filling out the processing and regulation strategies scales of the Inventory of Learning Styles – Short Versions. Independent-samples t-tests revealed that the longitudinal group is a non-random subset of students starting University College. For each scale, a multi-indicator latent growth model is estimated using Mplus 6.1. Results suggest that, on average, during higher education, students persisting in their studies in a non-delayed manner seem to shift towards high-quality learning and away from undirected and surface-oriented learning. Moreover, students from the longitudinal group are found to vary in their initial levels, while, unexpectedly, not in their change over time. Although the growth models fit the data well, significant residual variances in the latent factors remain.

Suggested Citation

  • Liesje Coertjens & Vincent Donche & Sven De Maeyer & Gert Vanthournout & Peter Van Petegem, 2013. "Modeling Change in Learning Strategies throughout Higher Education: A Multi-Indicator Latent Growth Perspective," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-12, July.
  • Handle: RePEc:plo:pone00:0067854
    DOI: 10.1371/journal.pone.0067854
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    Cited by:

    1. Dirk Tempelaar & Bart Rienties & Quan Nguyen, 2020. "Subjective data, objective data and the role of bias in predictive modelling: Lessons from a dispositional learning analytics application," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-29, June.

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