The Motion of Emotion: Idiodynamic Case Studies of Learners' Foreign Language Anxiety
Bibliographic record
Abstract
ABSTRACT Language learning is an emotionally and psychologically dynamic process that is influenced by a myriad of ever‐changing variables and emotional “vibes” that produce moment‐by‐moment fluctuations in learners' adaptation. This individual‐level study triangulates physiological, idiodynamic, interview, and self‐report survey data of three high and three low anxiety language learners to examine their language anxiety, its triggers, and the interpretations of rapidly changing affective reactions over a short period of time. Participants were videorecorded giving a presentation, while wearing heart monitors, in their Spanish as a Foreign Language class. Using the idiodynamic method, participants self‐rated their moment‐by‐moment anxiety 42 times over three and a half minutes and later explained their reactions in an interview. The strong relationship observed among the various converging data sources demonstrates the strength of considering language learners on an individual level using triangulated quantitative and qualitative approaches. The study generated pedagogical implications for dealing with both positive and negative emotions, facilitating the reinterpretation of physiological cues, planning “escape routes” that allow participants to remain active in communication exchanges, and invoking the positive power of preparation, planning, and rehearsal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".