“You can’t start a fire without a spark”. Enjoyment, anxiety, and the emergence of flow in foreign language classrooms
Bibliographic record
Abstract
Abstract The present study adopted a mixed-methods approach using a convergent parallel design to focus on the role that positive and negative emotions have in the Foreign Language (FL) classroom on the ontogenesis of positive flow. Participants were 1,044 FL learners from around the world. They provided quantitative and qualitative data on FL enjoyment (FLE), classroom anxiety (FLCA) and experience of flow via an on-line questionnaire (Dewaele, Jean-Marc & Peter D. MacIntyre. 2014. The two faces of Janus? Anxiety and enjoyment in the foreign language classroom. Studies in Second Language Learning and Teaching 4. 237–274). FLE was a significantly stronger predictor of frequency of flow experience than FLCA. Further statistical analyses revealed that flow experiences are typically self-centred, infrequent and short-lived at the start of the FL learning journey and when the perceived social standing in the group is low. They become an increasingly shared experience, more frequent, stronger and more sustained as learners reach a more advanced level in their FL. What starts as an occasional individual spark can turn into a true fire that extends to other group members. The findings are illustrated by participants’ reports on enjoyable episodes in the FL classroom in which some reported complete involvement in an individual or collective task, merging of action and awareness, joyful bonding with classmates, intense focus and joy, loss of self-consciousness, sense of time and place.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".