The correlates of flow in the L2 classroom: Linking basic L2 task features to learner flow experiences
Bibliographic record
Abstract
Abstract Flow is an intrinsic motivational state associated with full task engagement, positive affect, and enhanced performance. While research has examined how different language tasks interact with flow experiences, no study has examined learner flow experiences in a wide range of tasks using an experience sampling method to determine how universal basic task features (e.g., modality, participant structure, information distribution, and targeted skills) interact with flow. The present study aims to respond to this gap in the research. Participants were 13 teachers and 327 students from 18 intact French L2 classes in a Canadian postsecondary school. Teachers selected and implemented an average of six tasks from their personal repertoires at random moments throughout the semester. Immediately following each task, learners anonymously completed a flow experience questionnaire (N = 1408;α = 0.91), and teachers a task description questionnaire containing 17 basic task features (N = 81). Statistical analyses show that 10 of the 17 variables significantly interacted with learners’ flow experiences. The results not only validate a frequently used flow measurement and establish norms for future research but also outline a framework language teachers can use to evaluate and modify practices to improve learners’ subjective classroom experience.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".