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Record W2932504962 · doi:10.5539/elt.v12n5p42

Teaching Vocabulary Through Wiki to First Secondary Graders

2019· article· en· W2932504962 on OpenAlexvenueno aff
Khalid Yahya E. Al-Johali

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyMathematics educationPsychologyTest (biology)Teaching methodVocabulary learningVocabulary developmentPedagogyLinguistics

Abstract

fetched live from OpenAlex

Vocabulary is a fundamental component of any language. Learning vocabulary plays a crucial role in learning English language. Whereas wiki is a promising Web 2.0 technology that can be used innovatively in EFL instruction. This study aimed at examining the effectiveness of wiki-based instruction on vocabulary learning of first secondary graders. It was carried out in Sabya, Jazan, Saudi Arabia in 2018. It followed the quasi-experimental design of one experimental group. Fifty-seven Saudi teenage EFL students participated in a researcher-designed wiki-based vocabulary course in a random male school. The course consisted of twelve lessons to teach 80 words picked from the grade’s English Schoolbook (Mega Goal 2) to ensure their importance and benefit to students. Vocabulary pre-test and post-test along with a closed observation card were used to collect data. Descriptive statistics, paired samples and one-sample t-tests were used for analysis. It was found that wiki had slight positive effect on vocabulary learning. Results demonstrated that students achieved significantly better marks in their post-test but with a very low effect size (0.32). In addition, wiki was observed as usable, motivating, vocabulary enlarging assistant, and can be perceived positively by students. In the contrary, it was observed that the students’ collaborative work level was low. Accordingly, wikis can be a good vocabulary teaching tool if well-designed and well-implemented after training both teachers and 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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.304
Teacher spread0.295 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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