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Record W2626467555 · doi:10.5539/elt.v10n7p247

Benefits and Difficulties of Learning in Group Work in EFL Classes in Saudi Arabia

2017· article· en· W2626467555 on OpenAlexvenueno aff
Nurah Alfares

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPerceptionMathematics educationQualitative researchForeign languageQualitative propertyCognitionSample (material)English as a foreign languagePedagogyMedical education

Abstract

fetched live from OpenAlex

This study investigates learners’ perceptions of the benefits and the difficulties of group work (GW) in EFL (English as a foreign language) classes. The purpose of this study is to explore the possible effects of GW, in order to better understand learners’ attitudes towards GW, and to inform language teachers of students’ views of using GW. A mixed-methods approach (quantitative and qualitative methods) was used to collect the required data for the study. Questionnaires were collected from 188 students in five private language institutions. These institutions specialise in teaching EFL to adult students studying in intermediate and secondary schools. From this sample, 20 students were interviewed in more detail in follow-up telephone interviews. The questionnaire examined learners’ general perceptions, and the telephone interviews further explored the questionnaire findings. The findings revealed that many language learners consider the advantages of GW to be mainly related to (1) cognitive aspects, i.e. benefits that help learners in the learning process; and (2) emotional aspects, which are benefits that enhance motivation for learners. However, some learners identified difficulties, mostly related to learners’ behaviours, which can result in uncooperative work in groups. These findings revealed that Saudi learners regard GW as effective in learning, but that some students’ negative behaviours may prevent them from obtaining the benefits of GW.

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)0.002
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 score0.185
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
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 head0.018
GPT teacher head0.247
Teacher spread0.228 · 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

Citations57
Published2017
Admission routes1
Has abstractyes

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