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Informative tools for characterizing individual differences in learning: latent class, latent profile, and latent transition analysis

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journal contribution
posted on 31.03.2021, 10:28 by Marian Hickendorff, Peter A. Edelsbrunner, Jake McMullen, Michael Schneider, Kelly TreziseKelly Trezise
This article gives an introduction to latent class, latent profile, and latent transition models for researchers interested in investigating individual differences in learning and development. The models allow analyzing how the observed heterogeneity in a group (e.g., individual differences in conceptual knowledge) can be traced back to underlying homogeneous subgroups (e.g., learners differing systematically in their developmental phases). The estimated parameters include a characteristic response pattern for each subgroup, and, in the case of longitudinal data, the probabilities of transitioning from one subgroup to another over time. This article describes the steps involved in using the models, gives practical examples, and discusses limitations and extensions. Overall, the models help to characterize heterogeneous learner populations, multidimensional learning outcomes, non-linear learning pathways, and changing relations between learning processes. The application of these models can therefore make a substantial contribution to our understanding of learning and individual differences.

History

School

  • Science

Department

  • Mathematics Education Centre

Published in

Learning and Individual Differences

Volume

66

Pages

4 - 15

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Learning and Individual Differences and the definitive published version is available at https://doi.org/10.1016/j.lindif.2017.11.001.

Acceptance date

02/11/2017

Publication date

2017-11-09

Copyright date

2017

ISSN

1041-6080

eISSN

1873-3425

Language

en

Depositor

Dr Kelly Trezise. Deposit date: 28 March 2021