MétaCan
Menu
Back to cohort
Record W4308753368 · doi:10.5430/wjel.v12n8p402

Teachers’ and Students’ Perception on Acquiring English Vocabulary for Young Learners Using Mobile Apps in EFL Context

2022· article· en· W4308753368 on OpenAlexvenueno aff
Suhartawan Budianto, Nur Sayidah, Sucipto Sucipto, Amirul Mustofa

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
FundersDirecció General de Recerca, Generalitat de CatalunyaDirektorat Riset dan Pengabdian Masyarakat
KeywordsPerceptionRespondentContext (archaeology)PsychologyEnglish as a foreign languageMathematics educationVocabularyEnglish languageForeign languageVocabulary learningPedagogyLinguistics

Abstract

fetched live from OpenAlex

This study investigated the teachers’ and students’ perceptions on language acquisition for young learners using Mobile App in EFL context. Many ways have been found to assist young learners in acquiring English as a Foreign Language. This study applied survey where the teachers’ perceptions were obtained from the observation, while the students’ perceptions were taken from the observation questionnaire. The respondent and participants were taken from six elementary schools in which there were twelve English teachers and 569 students of grade 4th in Indonesia. The result showed that teachers thought most students were anxious and didn’t have self-esteem and a good motivation while learning English. The teachers’ perceptions don’t always match the students’ ones or vice-versa in learning English in EFL context. It is expected that the mismatches of teachers’ and students’ perception are not greater than the match 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)0.002
metaresearch head score (Gemma)0.001
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 score0.090
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.013
GPT teacher head0.288
Teacher spread0.275 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of English LanguageSame topicMobile Learning in EducationFrench-language works237,207