The Impact of Teachers’ Aural Input Enhancement vs. Textual Enhancement in Learners’ Awareness of Ungrammatical Forms
Bibliographic record
Abstract
Explicit teaching of grammar for the first time became prevalent in Grammar Translation Method. This method was mainly used for teaching the classical languages of Greek and Latin. Attention plays a fundamental role in all areas of L2 learning. This research focused on raising learners’ awareness through input enhancement. It attempted to compare the effects of visual enhancement and aural enhancement on the learning of new grammar forms. The research question was whether there was any statistically significant difference between visual and aural enhancement on the learning of new grammar points. To answer this research question, the researcher selected sixty learners from a language institute. Having been homogenized, each intact group which included thirty learners received the treatment. One group was taught through visual enhancement and another through aural input enhancement. The data collected through tests was analyzed through an independent t-test. The result indicated that visual enhancement was more useful than an aural enhancement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".