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Record W2981425888 · doi:10.5539/elt.v12n11p74

Using Cooperative Learning Strategies to Develop Rural Primary Students' English Oral Performance

2019· article· en· W2981425888 on OpenAlexvenueno aff
Lilian C. Nievecela, Diego Ortega

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPronunciationFluencyMathematics educationComprehensionTest (biology)Linguistics

Abstract

fetched live from OpenAlex

This small-scale quasi-experimental research study aimed at investigating the effectiveness of cooperative learning (CL) strategies in the achievement of students’ oral performance at the A1 Common European Framework of Reference for Languages (CEFR) level. The study participants were twenty-four seventh graders from a small rural primary school located on the southern part of Cuenca city. The quantitative part was based on a descriptive statistic study. It was collected through the students’ speaking pre and post-test. The results were processed and analyzed through SPSS version 25. To compare the mean scores of the students in the pre and post- test, a T test for one sample was used. In addition, the qualitative part based on phenomenological research was gathered through direct classroom observations and group discussion. Findings indicated that: firstly, the study participants reached their A1 oral performance level in the evaluation criteria of comprehension, interaction, fluency, pronunciation. Secondly, students had positive attitudes toward CL strategies. Thirdly, through CL strategies students became more motivated and less reluctant during oral participation. In light of the findings, CL should be adopted in primary English learning as it helps improve learners’ EFL peaking skill.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
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 head0.034
GPT teacher head0.387
Teacher spread0.353 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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