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Record W2803598416 · doi:10.5539/jel.v7n4p163

The Effect of Group Work on English Vocabulary Learning

2018· article· en· W2803598416 on OpenAlexvenueno aff
Su-Fei Lin

Bibliographic record

VenueJournal of Education and Learning · 2018
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyPsychologyReading (process)Vocabulary developmentMathematics educationIntervention (counseling)Vocabulary learningExtensive readingTeaching methodLinguistics

Abstract

fetched live from OpenAlex

This study investigated the effectiveness of group work (GW) in EFL vocabulary learning by second year, non-English major, university students in Taiwan, in comparison with working individually (IW). The students (N=44) worked in mixed ability groups of 3-4 or in IW to complete vocabulary exercises following reading activities. The classroom intervention followed a repeated measures design with alternating sessions (one week IW, one week GW) for 12 weeks. In order to measure students’ word knowledge gains, the modified vocabulary knowledge scale was used in pre-, post- and delayed-post tests, and the scores from the tests were analyzed with paired t tests. Qualitative information about vocabulary discovery and retention was further obtained from interviews with 24 students conducted after the classroom intervention. Results showed that students’ overall improvement in vocabulary knowledge with group work was significantly higher than that with individual work on immediate post-tests, though both treatments had a beneficial effect. Later retention of word knowledge after GW was only 2% higher than that with IW. Interpretations and implications of these findings are discussed.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.304
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 designOther design
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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