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Record W2276155829 · doi:10.5539/ells.v6n1p47

Vocabulary Learning Strategies Employed by Undergraduate EFL Jordanian Students

2016· article· en· W2276155829 on OpenAlexvenueno aff
Reem Ibrahim Rabadi

Bibliographic record

VenueEnglish Language and Literature Studies · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyMetacognitionLanguage learning strategiesMathematics educationVocabulary learningPsychologyCurriculumCognitionComputer sciencePedagogyLinguistics

Abstract

fetched live from OpenAlex

<p>The present study investigates the various vocabulary learning strategies (VLSs) used by undergraduate Jordanian students majoring English Language and Literature in Jordanian universities. The five categories of the vocabulary learning strategies (Memory, Determination, Social, Cognitive, and Metacognitive) were used in this study following Schmitt’s taxonomy. For this purpose, a questionnaire containing forty items selected from Schmitt’s (1997) Vocabulary Learning Strategies Questionnaire (VLSQ) was administered to a pool of 110 Jordanian students majoring in English Language and Literature from eight Jordanian universities. This testing instrument was used to reveal the types of vocabulary learning strategies used by the participants, to discover the most and least frequently used VLS employed by them, and to know the main patterns of variation of the participants’ choice of VLSs if they are high, medium, or low VLS users. The descriptive analysis of the study showed that Jordanian EFL learners were “medium” strategy users overall. With regard to strategy categories, the results revealed that Memory strategies were the most frequently employed by them and Metacognitive strategies were the least frequently used strategies among them. Although the participants were medium strategy users, the results of the VLSQ revealed that some individual strategies were employed at a low level. This result leads to adopt the learners’ individual vocabulary learning strategy as an important variable in future research. The findings of this study will be advantageous to language instructors to improve effective vocabulary teaching techniques and curriculum designers to provide learners with preferable vocabulary learning strategies.</p>

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)0.000
metaresearch head score (Gemma)0.000
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 score0.410
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.266
Teacher spread0.257 · 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

Citations34
Published2016
Admission routes1
Has abstractyes

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