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Record W4385665740 · doi:10.5539/elt.v16n9p13

Exploring the Impact of Interlinear vs. L1 Marginal Glosses on Iraqi EFL Learner’s L2 Vocabulary Learning

2023· article· en· W4385665740 on OpenAlexvenueno aff
Esmaeil Bagheridoust, Hiba Adnan Jawad

Bibliographic record

VenueEnglish Language Teaching · 2023
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyVocabulary learningGrammarPsychologyLinguisticsTest (biology)Vocabulary developmentMathematics educationPhilosophy

Abstract

fetched live from OpenAlex

This study investigates the impact of interlinear vs. L1 marginal glosses on the Iraqi Learning of vocabulary in EFL instructional setting. Along with all the efforts made during past 3 decades to improve the teaching of vocabulary via various strategies and techniques countywide, the classical grammar-translation method and its offspring are still widely used in most schools in Iraq. By analyzing the elicited data out of the management, treatment, and assessment of 92 pre-university students studying in two high schools in Iraq, the inquirer observed a reliable improvement of vocabulary learning in participants. To examine the effectiveness of the two glossing strategies, i.e., the implantation of L1 marginal and interlinear glosses, the results of pre and post-test of three groups were compared using One-Way ANOVA. We found reliable difference between the two groups in post-test vocabulary learning. Consequently, L1 marginal and Interlinear glosses both have come to make the learners understand L2 texts well, in comparison with the other group. Ultimately, Marginal glossing is more effective than the interlinear (text) glossing in the improvement of vocabulary development of Iraqi EFL learners.

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)0.002
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 score0.117
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 integrity0.0000.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.048
GPT teacher head0.347
Teacher spread0.298 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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