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Record W4415382369 · doi:10.5430/wjel.v16n2p203

Implementation of Gamification as an Active Methodology to Enhance Motivation and Academic Performance in EFL Learners

2025· article· W4415382369 on OpenAlexvenueno aff
Carmen Benitez-Correa, Ana Quiñonez-Beltran, Elsa Morocho-Cuenca

Bibliographic record

VenueWorld Journal of English Language · 2025
Typearticle
Language
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
FundersUniversidad Técnica Particular de Loja
KeywordsMotivation to learnSample (material)Intrinsic motivationEnglish as a foreign languageForeign languagePublic universityEnglish languageQualitative researchQuantitative research

Abstract

fetched live from OpenAlex

Gamification is a dynamic instructional methodology that enhances motivation and learning by incorporating game elements, which actively engage students. In this context, the present research aimed to determine the impact of gamification on public school students’ motivation to learn English as a Foreign Language. The sample consisted of N=441 learners (251 female and 190 male) who attended onsite classes. Their ages ranged from 11 to 12 years old. A quasi-experimental design was used to carry out this quantitative and qualitative study, which lasted five months. The techniques used were survey, interview, pre-test and post-test. The findings revealed that gamification had a positive influence on students' motivation to learn this language because students became interested in learning it in a fun way. Furthermore, the use of gamification helped students to improve their performance, which favoured their English academic achievement. Further research might consider using this methodology to promote students´ motivation to develop English skills in other learning environments, such as virtual ones.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.040
GPT teacher head0.439
Teacher spread0.399 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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