Applying latent profile analysis in foreign language anxiety research: Uncovering hidden groups
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To gain a deeper understanding of the complexity of Foreign Language Anxiety (FLA), researchers have leveraged various quantitative and qualitative methods. Considering the quantitative methods, researchers have mostly relied on variable-centered approaches to examine the relationships between FLA and other variables. However, less attention has been given to person-centered approaches, which aim to identify subgroups of a population to better understand individual differences and heterogeneity. This study applies latent profile analysis (LPA), a robust person-centered method, to uncover FLA profiles and to examine the predictors and outcomes of FLA profiles. To this aim, we first reviewed person-centered methods, addressing best practices and methodological considerations for conducting LPA. For the empirical study, we gathered data from 384 tertiary-level EFL learners using a questionnaire, which measured their FLA, achievement goals, and willingness to communicate. The LPA results revealed five distinct latent profiles of FLA, characterized not only by the intensity of anxiety but also its manifestations and triggers. Each profile also showed meaningful differences in achievement goals and willingness to communicate. By applying LPA, we could gain a deeper understanding of how FLA is experienced across different learner subgroups. We believe person-centered approaches, such as LPA, provide additional value to investigate anxiety and other emotions in language education research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it