Influential Psycholinguistic Factors in the Development of Linguistic Competence in English as a Foreign Language
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it