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Record W2987095563 · doi:10.5539/elt.v12n12p46

Multimedia Glosses for Enhancing EFL Students’ Vocabulary Acquisition and Retention

2019· article· en· W2987095563 on OpenAlexvenueno aff
Samah Zakareya Ahmad

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyPsychologyReading (process)Significant differenceTest (biology)Vocabulary developmentVocabulary learningMathematics educationMultimediaTeaching methodLinguisticsComputer scienceMedicine

Abstract

fetched live from OpenAlex

The present study attempted to investigate the effect of multimedia glosses on EFL students’ vocabulary acquisition and retention. Forty-five EFL students were divided into two groups: control (n=22) and experimental (n=23). A vocabulary test was administered to both groups in order to ensure that they were equivalent. Then, all participants attended 12 weekly reading sessions where participants of the experimental group practiced reading computerized texts that included multimedia glosses while participants of the control group practiced reading the same texts but without any glosses. Immediately after the treatment was over, the vocabulary test was administered to both groups in order to evaluate the differences between the two groups in vocabulary acquisition. Moreover, the same test was administered to both groups two weeks after the administration of the posttest with the purpose of evaluating the differences between the two groups in vocabulary retention. The statistical analysis revealed a significant difference between the two groups in both the immediate and the delayed administrations of the vocabulary test. Therefore, it was concluded that multimedia glosses enhanced both vocabulary acquisition and retention among EFL students.

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)0.000
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 score0.107
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.308
Teacher spread0.299 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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