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Record W2994822816 · doi:10.5539/ijel.v10n1p183

The Effects of Using Games on EFL Students’ Speaking Performances

2019· article· en· W2994822816 on OpenAlexvenueno aff
Vũ Phi Hổ Phạm, Nguyen Minh Thien, Nguyen Thi My An, Ngoc Hoang Vy Nguyen

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicNationalism and Cultural Identity
Canadian institutionsnot available
Fundersnot available
KeywordsPresentation (obstetrics)PsychologyContext (archaeology)TourismMathematics educationControl (management)Process (computing)Data collectionMedical educationPedagogyComputer scienceSociologyPolitical science

Abstract

fetched live from OpenAlex

This study investigated the effects of employing games on students’ speaking performances in the classroom. 74 non-English major students, 36 students from the Tourism and Travel Management and 38 from the Office Management major from Tra Vinh University, participated in the study. The control group was trained with the methods of P-P-P (presentation, practice, and production) while the experimental group was trained with the same process but using selective games in the learning processes. Data collection was from the pre- vs. post-tests, questionnaire and interviews for analysis. The findings revealed that using games in the speaking classrooms, the students were motivated in the learning process and their speaking skills improve remarkably. The current study suggested teachers in the research context to apply gaming activities as an effective method to improve students’ participation in the learning processes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.345
Teacher spread0.330 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations14
Published2019
Admission routes1
Has abstractyes

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