The Impact of Neuro-Linguistic Programming on English Language Teaching: Perceptions of NLP-Trained English Teachers
Bibliographic record
Abstract
This study aims at exploring the feasibility of Neuro-Linguistic Programming (NLP) in English Language Teaching (ELT) by analysing the perceptions of NLP-trained English teachers. The study applied a qualitative method based on interviews with 20 NLP-trained English teachers of high schools in India. To acquire an in-depth perspective of the extensive and comprehensive material available, the interview comprised open-ended detailed questions on the innovation, role, different techniques, and benefits and limitations of NLP, while also dwelling upon the different perceptions of NLP-trained language teachers, motivations of the learners towards NLP, and the positive changes brought about by NLP in the education sector. The researcher used a manual thematic analysis method to identify similarities in pattern while performing the analysis. Also, reliable studies from verifiable sources were selected for further analysis. Findings showed that NLP facilitated communication and encouraged learners of English. In addition, the finding strongly suggests NLP as an effective tool for developing teacher-student relationship and for promoting interactive learning environment.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".