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Record W2871241220 · doi:10.5539/elt.v11n8p65

Speaking Activities to Foster Students’ Oral Performance at a Public School

2018· article· en· W2871241220 on OpenAlexvenueno aff
Bocanegra Bonilla Carolina, Ramírez Valencia Astrid

Bibliographic record

VenueEnglish Language Teaching · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyClass (philosophy)Set (abstract data type)Mathematics educationLanguage proficiencyFirst languageAction researchAction (physics)PedagogyLinguisticsComputer science

Abstract

fetched live from OpenAlex

By employing an action research framework, this study was designed to explore the effectiveness of a set of speaking activities centered on a children’s cartoon to improve oral performance in the EFL classroom. The participants were fourth graders of a public school in Colombia, who had low oral performance in spoken English language. The findings suggest that the proposed speaking activities were comprehensible for the students, and appropriate to the students’ proficiency level. Although to maximize learners’ opportunities to speak in the classroom, teachers should design activities that provide chances to talk in English. Thus students will be engaged to participate in the classroom. Also, most of the students were comfortable speaking during the activities since they were familiar with watching cartoons, thus being at ease with certain topics can help students exchange ideas in class. The most important recommendation is to avoid translating instruction into students’ mother tongue for the purpose to familiarize students with the target language.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient 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 score0.645
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.028
GPT teacher head0.283
Teacher spread0.254 · 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
Published2018
Admission routes1
Has abstractyes

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