The use of input‐based tasks to initiate adult learners to the expression of temporality in French literacy and language instruction
Bibliographic record
Abstract
Abstract Input‐based tasks have shown promising outcomes for teaching concrete notions (i.e., vocabulary items and number marking) to beginner students in varied educational contexts, including with second language (L2) adults with little to no schooling experience. However, neither the teaching of more abstract notions nor the learning outcomes generated by such an intervention with this specific population of learners has yet been documented. We thus conducted a study targeting the expression of temporality in an intact L2 French language and literacy classroom ( N = 18), using input‐based tasks that required participants to process three‐frame picture stories representing the same action unfolding in time. The students who volunteered ( n = 6) were tested before and after the intervention on a receptive and a productive task. Results show not only that participants performed better after the intervention on both measures but also that their production of temporality moved from a picture‐by‐picture description to a more coherent description expressed through different means (lexical, morphological, etc.). The results of this study can benefit both researchers and practitioners focused on adult language and literacy learners, for whom evidence‐based research is scarce, but also to L2 researchers, who are encouraged to expand research beyond the traditional Western, educated, industrialized, rich, and democratic (WEIRD) populations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".