Targeting count and noncount nouns in English through textual enhancement and elaboration tasks: effects on L2 development and text comprehension
Bibliographic record
Abstract
This study investigated the effects of input enhancement in isolation and in combination with output elaboration tasks on the accuracy of count and noncount nouns and text comprehension in English as a Second Language. Participants were twenty-three Spanish adult ESL learners who were divided into one control and two intervention groups. The intervention lasted two weeks, and learners were required to read materials with input enhancement and to participate in classroom tasks that elicited output practice. Pre- and post-tests included a grammaticality judgment task, a written task and a decontextualized task. A note-taking activity along with questionnaire and interview materials provided qualitative support for the data analyzed quantitatively.Results from a two-factor ANOVA with repeated measures revealed the following: (a) no significant effect in regard to participants' mean scores on the grammaticality judgment tasks for Group, Time, or the Group x Time interaction; (b) participants' mean scores on the writing tasks showed a significant effect for Time, irrespective of group. Findings from the decontextualized task were not analyzed statistically, but they suggest beneficial effects on the performance of the intervention groups. Finally, findings from this study demonstrated improvement in text comprehension over time, irrespective of group (and no Group x Time interaction), which provided empirical support that this type of treatment is relatively unobtrusive to comprehension.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".