Speaking Anxiety, English Proficiency, Affective and Social Language Learning Strategies of ESL Engineering Students in a State University in Northern Luzon, Philippines
Bibliographic record
Abstract
The primary aim of this paper is to examine the speaking anxiety, affective and social language learning strategies and English language proficiency among ESL Agricultural and Biosystems and Civil Engineering students of a state university in Northern Luzon, Philippines including the possible relationship among the aforementioned variables. The research adapted six (6) items on Affective Language Learning Strategies (ALLS) and six (6) items on Social Language Learning Strategies (SLLS) by Oxford (1990); the Foreign Language Communication Anxiety Scale designed by Horwitz et al. (1986) and the English Proficiency Test developed by Commission on Higher Education were utilized in this study. The findings disclose that the speaking anxiety level of the respondents is moderate. It was noted that they are uneasy every time teachers called them to recite in English class unprepared. To add more, their ALLS and SLLS are both somewhat true for them and the repondents’ English Proficiency Level is moderate. It also showed that small negative correlation exists between their English proficiency and speaking anxiety. However, a medium and small positive correlation established when their speaking anxiety and ALLS was correlated. And a small positive correlation was obtained in the correlation between the respondents’ speaking anxiety and SLLS. The research concludes with a list of recommendations on how to lessen speaking anxiety in the English language classroom to ESL learners.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".