Second language listening comprehension: The role of anxiety and enjoyment in listening metacognitive awareness
Bibliographic record
Abstract
Emotion in second language acquisition (SLA) has recently received greater attention because it is largely implicated in daily conversations, which may affect second or foreign language (L2) use including listening comprehension. Most research into emotion and L2 listening comprehension is focused exclusively on anxiety, with an attempt to reduce its negative effects on individuals’ listening performance. With the arrival of positive psychology in SLA, researchers began to take a holistic view of a wider range of emotions including enjoyment that language learners experience during their L2 communication. The current study explored the relationships among listening anxiety, enjoyment, listening comprehension performance, and listening metacognitive awareness among a group of 410 international students in a Canadian university. Correlational analyses showed that listening anxiety was negatively correlated with enjoyment. However, these two variables shared only 18% of their variance, indicating that listening anxiety and enjoyment are related but independent emotions. This study suggests that anxiety and enjoyment in L2 listening are not the opposite ends of the same emotional continuum, but each serves a different purpose. L2 learners should work to find intriguing and enjoyable experiences in language learning, rather than focusing merely on reducing anxiety.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".