Changes in Language Learners’ Affect: A Complex Dynamic Systems Theory Perspective

Abstract This study investigated changes in motivation, self‐efficacy beliefs, and a range of emotions, including enjoyment, hope, pride, curiosity, anxiety, boredom, apathy, confusion, and shame, from a complex dynamic systems theory (CDST) perspective over a 2‐year period in the Hungarian English as a Foreign Language (EFL) context. Using the same questionnaire, we collected data four times throughout 4 semesters from 101 participants studying English in two Hungarian high schools. For data analysis, we used latent growth curve modeling (LGCM) to detect the group‐level changes in learners’ motivation, self‐efficacy, and emotions. We also employed dynamic cluster analysis to identify trends in learners’ trajectories regarding these variables. In our panel data, linear models described the data well concerning the ought‐to second language (L2) self, language learning experience, boredom, apathy, and confusion, and for enjoyment, curiosity, anxiety, and shame, nonlinear models had the best fit. We could also identify trajectories depicting attractor states and learner paths that featured influences of perturbations.

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Publication Details

Journal
Language Learning
Published
2024-10-22
DOI
https://doi.org/10.1111/lang.12686
Citations
5
Primary Topic
Cognitive Science and Mapping
Type
article
Field-Weighted Citation Impact
1.74

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article

Changes in Language Learners’ Affect: A Complex Dynamic Systems Theory Perspective

Ágnes Albert, Katalin Piniel
5 citations
Language Learning
Cognitive Science and Mapping
1.74
article

Changes in Language Learners’ Affect: A Complex Dynamic Systems Theory Perspective

Ágnes Albert, Katalin Piniel
article en
5 citations

Abstract

Abstract This study investigated changes in motivation, self‐efficacy beliefs, and a range of emotions, including enjoyment, hope, pride, curiosity, anxiety, boredom, apathy, confusion, and shame, from a complex dynamic systems theory (CDST) perspective over a 2‐year period in the Hungarian English as a Foreign Language (EFL) context. Using the same questionnaire, we collected data four times throughout 4 semesters from 101 participants studying English in two Hungarian high schools. For data analysis, we used latent growth curve modeling (LGCM) to detect the group‐level changes in learners’ motivation, self‐efficacy, and emotions. We also employed dynamic cluster analysis to identify trends in learners’ trajectories regarding these variables. In our panel data, linear models described the data well concerning the ought‐to second language (L2) self, language learning experience, boredom, apathy, and confusion, and for enjoyment, curiosity, anxiety, and shame, nonlinear models had the best fit. We could also identify trajectories depicting attractor states and learner paths that featured influences of perturbations.

Language LearningVol. 75(3)
Eötvös Loránd University (HU)
University of Oxford, Magyar Tudományos Akadémia, Eötvös Loránd Tudományegyetem, Nemzeti Kutatási Fejlesztési és Innovációs Hivatal
Quality Education
Openalex Percentile: Top 13%
Cognitive Science and Mapping
1.74
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Changes in Language Learners’ Affect: A Complex Dynamic Systems Theory Perspective — Ágnes Albert, Katalin Piniel · Language Learning (2024) | TGRS Research Map | TGRS