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Record W1857645712 · doi:10.5539/elt.v8n10p152

The Impact of Mobile Learning on Listening Anxiety and Listening Comprehension

2015· article· en· W1857645712 on OpenAlexvenueno aff
Mehrak Rahimi, Elham Soleymani

Bibliographic record

VenueEnglish Language Teaching · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsActive listeningPsychologyListening comprehensionAnxietySignificant differenceControl (management)ComprehensionMathematics educationDevelopmental psychologyLinguisticsCommunicationComputer science

Abstract

fetched live from OpenAlex

This study aimed at investigating the impact of mobile learning on EFL learners’ listening anxiety and listening comprehension. Fifty students of two intermediate English courses were selected and sampled as the experimental (n=25) and control (n=25) groups. Students’ entry level of listening anxiety was assessed by foreign language listening anxiety questionnaire and their listening comprehension was assessed by Key English Test (KET) prior to the study. For one semester the experimental group did their listening activities by using podcasts listened to on their mobile phones and/or portable digital media players. Meanwhile the control group used their desktop computers to do their listening activities. The results of data analysis showed that listening anxiety of the experimental group reduced significantly after the experiment. Further, a significant difference between the experimental and control groups’ listening comprehension was found in favor of the experimental group at the end of the experiment.

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)0.003
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.327
Teacher spread0.314 · 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

Citations60
Published2015
Admission routes1
Has abstractyes

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