Investigating the effect of task modality on the written and oral production of young EFL learners
Bibliographic record
Abstract
Abstract Research on task-based interaction examines learner-initiated attention to formal aspects of language (i.e., language-related episodes or LREs) and how task modality (oral or written) impacts on their incidence, nature (meaning- or form- focused), and resolution. In the light of studies attesting a relationship between LREs and L2 development (LaPierre 1994. Language output in a cooperative learning setting: Determining its effects on second language learning (Unpublished master’s thesis). University of Toronto, Toronto, Ontario, Canada; Swain and Lapkin 1998. Interaction and second language learning: Two adolescent French immersion students working together. Modern Language Journal 82(3). 320–337; Williams 2001. The effectiveness of spontaneous attention to form. System 29. 325–340), it has been claimed that LREs represent second language learning in progress (Gass and Mackey 2007. Input, interaction and output in second language acquisition. In Bill VanPatten & Jessica Williams (eds.), Theories in second language acquisition: An introduction , 175–199. Mahwah, NJ: Lawrence Erlbaum). This study compared the incidence, nature, resolution and reflection of LREs produced by 59 child learners of L3 English aged 10–12, where students in the written and oral tasks were instructed to focus on accuracy and were given the chance to edit their final production. The study also examined the correspondence between the resolution of each LRE and its occurrence on the written and oral tasks. The main results showed that while the incidence of form- and meaning-focused LREs was indeed higher in the written task, it was also the case that in the written task more non-target-like resolved LREs were reflected in the written final output than in the oral final output. This finding leads us to caution researchers and teachers in promoting the use of written collaborative tasks over oral tasks until we are in a better position to understand the effect that the reflection of non-target-like LREs in the final output may have for second-language (L2) development.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".