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Record W2598999122 · doi:10.5539/elt.v10n4p92

The Effect of Aural and Visual Storytelling on Vocabulary Retention of Iranian EFL Learners

2017· article· en· W2598999122 on OpenAlexvenueno aff
Maryam AminAfshar, Ahmad Mojavezi

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyPsychologyCronbach's alphaTest (biology)StorytellingMathematics educationVocabulary developmentVocabulary learningTeaching methodLinguisticsNarrativeDevelopmental psychologyPsychometrics

Abstract

fetched live from OpenAlex

EFL learners at all ages and proficiency levels are usually confronted with various problems in vocabulary learning and retention. This study sought to introduce strategies for improvement of vocabulary learning and retention. Therefore, the effects of using aural/visual storytelling on Iranian EFL learners’ vocabulary learning and retention were investigated. To do so, 50 intermediate female EFL learners were randomly assigned to two groups. After the administration of teacher made English Vocabulary Test as the pre-test, aural storytelling method was used for the control group, and visual storytelling method was used for the experimental group. After three months of instruction, the aforementioned teacher made English Vocabulary Test, as the post-test, was given to the students of both groups to assess their improvements. Two weeks after post-test, they were given a delayed post-test to measure their retention of English vocabulary knowledge. The reliability of the English Vocabulary Test using Cronbach's Alpha was estimated equal to 0.80. Finally, Using ANCOVA, the results revealed that, the experimental group’s participants outperformed those of control group in both learning and retention of English vocabulary. So, it can be noted that the training program according to visual could have impressive impact on the learning and retention of vocabulary knowledge.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.991

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 communication0.0000.000
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 head0.020
GPT teacher head0.371
Teacher spread0.351 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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