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Record W2165848477 · doi:10.5539/elt.v6n10p57

Investigating Learning English Strategies and English Needs of Undergraduate Students at the National University of Laos

2013· article· en· W2165848477 on OpenAlexvenueno aff
Thongma Souriyavongsa, Mohamad Jafre Zainol Abidin, Rany Sam, Ithayaraj Britto Aloysius

Bibliographic record

VenueEnglish Language Teaching · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsSyllabusPsychologyMathematics educationCurriculumNeeds analysisQuality (philosophy)VocabularyEnglish for specific purposesPedagogyMedical educationLinguistics

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.235
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2013
Admission routes1
Has abstractyes

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