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Record W4365451746 · doi:10.5430/wjel.v13n5p354

Mobile Technology-Mediated Language Learning: A Quantitative Study to Unravel Language Learners’ Achievement and Autonomy

2023· article· en· W4365451746 on OpenAlexvenueno aff
Komang Puteri Yadnya Diari, Suwarna Suwarna, I Made Suweta

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
Fundersnot available
KeywordsPaceLearner autonomyAutonomyMathematics educationLanguage acquisitionComputer sciencePsychologyLanguage educationComprehension approachPolitical science

Abstract

fetched live from OpenAlex

This study investigates the impact of mobile technology-mediated language learning (MTMLL) on the learners’ Balinese language learning achievement and autonomy. A quantitative approach research with quasi-experimental and survey designs was conducted. The data were collected through the tests and MTMLL questionnaire administration to fifty-eight primary school students in Bali, Indonesia. The data were analyzed statistically using SPSS 23 version. The results indicate that MTMLL has a positive impact on learners' achievement and autonomy. Participants reported that learners using MTMLL showed higher levels of achievement compared to those who did not use this technology. Additionally, MTMLL provided them with more control over their learning and allowed them to study at their own pace and convenience. The findings of this study contribute to the literature on MTMLL and local language learning by highlighting the importance of technology in promoting learners’ achievement and autonomy. The study suggests that MTMLL can be an effective tool for language learners to enhance their learning experience and achieve better outcomes.

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)0.002
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 score0.031
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.291
Teacher spread0.281 · 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
Published2023
Admission routes1
Has abstractyes

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