‘Transported into like another space’: second language learners’ perspectives on their experience of flow
Bibliographic record
Abstract
This study explored the notion of flow, which refers to a person’s sense of being completely absorbed in a task, as it applies to second language (L2) learning and use. Previously, flow has been mainly examined through researcher-generated descriptions to which learners reacted using Likert-type scales. In this study, we examined flow through the perspectives of the individuals experiencing it, by asking them to describe any insights relevant to their experience. During four weeks, five undergraduate students taking L2 French or Spanish coursework reported their flow states in weekly diary entries and interviews. Students described a total of 15 flow states, reporting them most frequently in interaction inside and outside coursework (e.g. speaking to a friend/stranger, in a paired task) but also while taking an exam, studying grammatical forms, listening to a lecture, reading, and practicing a speech. Qualitative coding revealed seven dimensions of flow, where five previously reported dimensions (attention, interest, enjoyment, sense of accomplishment, skill–challenge balance) were supported and clarified through our analyses while the remaining two (oblivion, stress) were identified for the first time. Flow appeared to be linked to proceduralisation of L2 skills, as the element of automaticity was prominent in most descriptions of flow.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".