Interaction, modality, and word engagement as factors in lexical learning in a Chinese context
Bibliographic record
Abstract
This study investigates the roles of collaborative output, the modality of output, and word engagement in vocabulary learning and retention by Chinese-speaking undergraduate EFL learners. The two treatment groups reconstructed a passage that they had read in one of two ways: (1) dyadic oral interaction while producing a written report (Written Output); (2) dyadic oral interaction followed by an oral report (Oral Output). A control group completed a reading comprehension task (Reading) based on the same passage. Four posttests revealed that Oral Output led to significantly better productive and receptive lexical learning than Reading all the way to the last posttest. Written Output led to significantly better productive and receptive lexical learning than Reading on posttest 2, but not on posttests 3 and 4. However, the difference in lexical learning between the Written and Oral Output conditions did not achieve significance. Interaction analysis found that the Oral and Written Output groups differed in the types of word processing they favoured as well as in the frequency of their word engagement. The article discusses the reasons why collaborative output facilitates lexical learning; considers the association between the Output performers’ word engagement and lexical retention; and suggests what might have contributed to the better success of the Oral Output group in their lexical retention.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".