Influential Psycholinguistic Factors in the Development of Linguistic Competence in English as a Foreign Language
Bibliographic record
Abstract
This study investigated the influence of cognitive and metacognitive psycholinguistic factors on English linguistic competence among university students enrolled in a Language Center at a national university in Peru. The sample consisted of 153 students selected through convenience non-probabilistic sampling from a pre-intermediate level. A virtual form instrument was designed for data collection, which was validated through factorial analysis, showing a good model fit with two factors and good internal consistency. This study employed multiple linear regression (MLR) as the statistical method to investigate the relationship between psychosocial factors and linguistic competence. The results showed high statistical significance in the global model test, suggesting that the analyzed factors explain 21.8% of the variability in linguistic competence. The analysis of effect sizes, with ε² values of 0.161 and 0.023 for cognitive and metacognitive factors, respectively, supports the stronger influence of cognitive factors on linguistic competence. This study highlights the importance of considering psycholinguistic factors in developing linguistic competence and provides a basis for further research.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".