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Record W2532692116 · doi:10.5539/elt.v9n11p67

Teaching and Learning Vocabulary through Reading as a Social Practice in Saudi Universities

2016· article· en· W2532692116 on OpenAlexvenueno aff
Sultan Altalhab

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyPsychologyReading (process)Competence (human resources)Mathematics educationPedagogyTeaching methodAutonomyContext (archaeology)Qualitative researchVocabulary developmentSocial psychologyLinguisticsSociology

Abstract

fetched live from OpenAlex

<p>The study explores the social practice of vocabulary learning by examining vocabulary teaching techniques employed by teachers, the vocabulary learning strategies (VLSs) identified by students as most useful and the ones they felt most competent in using when reading and teachers’ and students’ attitudes towards learning vocabulary through reading. While most vocabulary research is quantitative, this study used a mixed methods approach of quantitative and qualitative data collected from a range of sources. One hundred and fifty students majoring in English from four different universities completed a semi-structured questionnaire and twenty-two of them were interviewed. In addition, nine teachers of vocabulary and reading subjects were interviewed and their classes observed. A systematic analysis for the prescribed textbooks was also conducted. The findings revealed that both teachers and students were negotiating their autonomy on an ongoing basis, which means that the social context of learning has a powerful influence on what students learn. The study concludes that vocabulary learning is a social practice influenced by a range of factors, such as teaching techniques, VLSs, the prescribed textbook, participants’ beliefs and attitudes, learners’ interests, cultural values and learners’ level of competence in English.</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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.311
Teacher spread0.302 · 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.

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

Citations4
Published2016
Admission routes1
Has abstractyes

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