Interwoven Factors that Influence EFL Learning at Tertiary Level in the Saudi Context: A Case Study
Bibliographic record
Abstract
It is undoubtedly true that some factors influence students' learning of the English language. This research examines factors that influence Saudi students' learning, studying in second language acquisition (ENG 466) course, at the English and Translation Department, Qasim University This qualitative study included 13 Saudi EFL learners using an open-ended three feedback questions to collect the data that explored linguistics, social, and psychological factors that influence their L2 learning. Using the content analysis procedures, the study found: some students considered they were average- or weak second language (L2) learners, more than half of the students believed that they were proficient L2 learners. Additionally, the results showed that all three (linguistic, social, and psychological) factors were significant in either encouraging or discouraging learners in the progression of their L2, for instance, a teacher may be a source of inspiration for students or a source of discouragement. The response also revealed that having a companion to practise the language who is interested in the subjects you are learning with, is among the key elements that define successful language learners. The research advises EFL teachers to exercise caution and address the various elements that influence their students' acquisition of a second language. The present paper's conclusion included a few comments, instructional implications, and suggestions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".