Investigating Learning English Strategies and English Needs of Undergraduate Students at the National University of Laos
Bibliographic record
Abstract
This paper aims to investigate learning English strategies and the requirement of English needs of the undergraduate students at the National University of Laos (NUOL). The study employed a survey design which involved in administering questionnaires of rating scales, and adapting the items from (Barakat, 2010; Chengbin, 2008; Kathleen A, 2010; Patama, 2001; Richards, 2001), to measure learning English strategies and the needs of English skills from 160 Lao undergraduate students of NUOL. The findings of this study revealed that speaking skill was the most important skills that students needed to improve in their undergraduate program. All participants reported a medium frequency use of strategy on learning English. The most frequently used strategies involved in using vocabulary books and electronic dictionaries to remember new English words. Based on the research findings, the researchers provided some recommendations for course developers to be reconsidered and redesigned the curriculum and syllabus including the instructional materials, learning behaviours and learning strategies of the English courses in all faculties in order to enhance the quality of learning and teaching activities as well as to meet the learners’ needs and social demands for their prospective careers and country’s development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".