Development of Language Proficiency and Pragmatic Competence in an Immersive Language Program
Bibliographic record
Abstract
Since pragmatic competence and grammatical competence are two distinct aspects of communicative competence (Bachman, 1990), a high level of grammatical competence may not lead to a high level of pragmatic competence, rather it can be best developed through immersion in the target language. In this respect, this paper addresses three research questions within the context of an immersive language program in an EFL setting: 1) Does instruction in an immersive language program have a significant effect on language learners’ general language proficiency? 2) Is there any significant relationship between language learners’ general language proficiency and their pragmatic competence? 3) Is there any significant relationship between language learners’ level of language contact and their pragmatic competence?In the experiment, Japanese first-year college students (n=18) were assessed through TOEFL PBT at the start of a one-year language immersion program. The subjects thereupon participated in an intensive language program. At the end of the academic year, all subjects took another TOEFL PBT along with a pragmatic competence test (Bardovi-Harlig, 2009) and a language contact survey. The statistical findings of this study demonstrated a significant positive effect for immersive language program on general language proficiency. However, the findings found no significant association between general language proficiency and pragmatic competence and only a weak correlation between language contact and pragmatic competency. This suggests that developing general linguistic proficiency and immersive language contact with a target language do not automatically ensure the acquisition of pragmatic competence.
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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 | 0.002 |
| 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 | 0.001 | 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".