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Record W2891386691 · doi:10.5539/ijel.v8n6p281

The Effect of an Intensive English Program on the Vocabulary Size of Lebanese English Foreign Learners

2018· article· en· W2891386691 on OpenAlexvenueno aff
Reema Abouzeid

Bibliographic record

VenueInternational Journal of English Linguistics · 2018
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularySyllabusContext (archaeology)Foreign languageTest (biology)Mathematics educationPsychologyPedagogyLinguistics

Abstract

fetched live from OpenAlex

The research concerning intensive programs in general has yielded conflicting results and as such, whether or not such time-shortened courses are effective in achieving their learning objectives is a matter of controversy. This study hopes to contribute to the current body of research available and perhaps aid in clearing the uncertainty surrounding such compact programs in a foreign language context by evaluating the effectiveness of one IEP at an English-medium university in Lebanon through the construct of vocabulary size. The vocabulary size of 100 English as a Foreign Language (EFL) learners enrolled in an Intensive English Program (IEP) was measured pre and post instruction using the New Vocabulary Levels test (NVLT). Test results were examined and compared using a dependent samples t-test to determine the effectiveness of the IEP in improving students’ vocabulary size. Results revealed that across the NVLT’s 6 receptive vocabulary lists (1000, 2000, 3000, 4000, and 5000 most frequent words in addition to the Academic Word List), students showed a statistically significant improvement on the post test (p=0.000< 0.05), endorsing the effectiveness of IEP in a foreign language context in enhancing students’ receptive vocabulary size. This study concludes with practical implications for EFL IEP teachers and syllabus designers.

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)0.239
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.239
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.325
Teacher spread0.313 · 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 designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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