Neurolinguistic Programming and Regular Verbs Past Tense Pronunciation Teaching
Bibliographic record
Abstract
A troublesome pronunciation issue for Spanish EFL learners is the past -ed ending of regular verbs. Neuro linguistic Programming (NLP) is a perspective integrating neurology, language and programming which are key for processing information and for responding to learners’ styles with the potential to help EFL teachers address this pronunciation issue. This paper reports a study conducted in two subsequent terms with 43 students at a university language institute: two groups taught using standard pronunciation techniques and two using NLP techniques preceded by oral tasks in which they were encouraged to pronounce regular verbs in the past. Data collected included students self-recorded pronunciation tasks, a survey to elicit students’ motivation and satisfaction and a teacher’s log with insights about students’ attitude and response to the strategy. Analysis of the data showed that after the first implementation, the NLP group improved their pronunciation a 30%, the standard one improved a 10%. During the second implementation, the NLP group showed an improvement of 23.7% pronunciation accuracy in task 1 and a 24.6% in task two compared to the standard group. The findings suggest that teacher’s use of NLP techniques into their classroom instruction have a positive impact on students’ pronunciation of the past ending of regular verbs.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".