Affective factors influencing fluent performance: French learners’ appraisals of second language speech tasks
Bibliographic record
Abstract
The present mixed-methods study examined the role of learner appraisals of speech tasks in second language (L2) French fluency. Forty adult learners in a Canadian immersion program participated in the study that compared four sources of data: (1) objectively measured utterance fluency in participants’ performances of three narrative tasks differing in their conceptualization and formulation demands, (2) a questionnaire on their interest, task-related anxiety, task motivation, and perceived success in task-completion, (3) an interview in which they elaborated on their perceptions of the tasks, and (4) subjective ratings of their performances by three native speakers. Findings showed the cognitive demands of tasks were associated with learners’ affective responses to tasks as well as objective and subjective measures of fluency. Furthermore, task-related anxiety and perceived success in task completion were the most important affective factors associated with fluent task performance, whereas interest and task motivation were correlated with native speakers’ fluency ratings. These results are discussed in terms of how task design and implementation can contribute to enhanced task motivation and performance in the classroom.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".