A new paradigm for the learning of a second or foreign language: the neurolinguistic approach
Bibliographic record
Abstract
This article considers the contribution of research in neuroscience to resolving the question of how to develop communication skills in a second language in an institutional setting. The purpose of the article is to demonstrate how the findings of cognitive neuroscience can assist educators to understand the complexity of learning and, as a result, to develop more effective instructional practices. The article begins with a brief description of the two options for the learning of French as a second language currently offered in the Canadian school system and the deficiencies inherent in these programs for a country attempting to foster English-French bilingualism in its anglophone citizens. Secondly, the paradigm underlying the core French option, based on cognitive psychology, is examined and its limitations are discussed. The remainder of the article presents the Neurolinguistic Approach (NLA) as developed by the authors, explaining its bases in cognitive neuroscience, the ensuing five major principles of the approach, with the pedagogical consequences that each one entails. Reference is then made to two classroom applications of the NLA: intensive French implemented widely in Canada and another adaptation implanted in China. After comparing the approach briefly with French immersion, limitations of the NLA are presented, and the article concludes with some directions for future research. The positive results of the practical applications of the NLA indicate the important contribution research in cognitive neuroscience can make to improving learning in a classroom situation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.002 | 0.030 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".