The Relationship Between Task Difficulty and Second Language Fluency in French: A Mixed Methods Approach
Bibliographic record
Abstract
While there exists a considerable body of literature on task‐based difficulty and second language (L2) fluency in English as a second language (ESL), there has been little investigation with French learners. This mixed methods study examines learner appraisals of task difficulty and their relationship to automated utterance fluency measures in French under three different task conditions. Participants were 40 adult learners of French at varying levels of proficiency studying in a university immersion context in Québec. Appraisal of task difficulty was assessed quantitatively by participants' self reports in response to a five‐item questionnaire and qualitatively by retrospective interviews. Utterance fluency was operationalized by four temporal variables and measured by Praat, a speech analysis software program. Across tasks, the quantitative results indicate that appraisals of lexical retrieval difficulty and fluency difficulty were most strongly related to perceived overall task difficulty. The qualitative analysis shows how L2 speakers evaluated the difficulty of each task as well as the features that either contributed to or limited their L2 fluency. Students' fluency in performing the three tasks was found to differ for articulation rate and average pause time, but not for pause frequency or phonation–time ratio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".