MétaCan
Menu
Back to cohort
Record W2128096435 · doi:10.5539/elt.v7n4p11

The Effects of Multimedia Task-Based Language Teaching on EFL Learners’ Oral L2 Production

2014· article· en· W2128096435 on OpenAlexvenueno aff
Madhubala Bava Harji, Mehrnaz Gheitanchian, Krishnaveni Letchumanan

Bibliographic record

VenueEnglish Language Teaching · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsFluencyTask (project management)PsychologyContext (archaeology)Test (biology)Task analysisMathematics educationTeaching methodComputer-Assisted InstructionLanguage acquisitionComputer scienceMultimedia

Abstract

fetched live from OpenAlex

This study examined the effects of tasks, with varying levels of complexity, i.e. simple, + complex and ++ complex tasks on EFL learners’ oral production in a multimedia task-based language teaching environment. 57 EFL adult learners carried out a total of 12 tasks, in sets of four tasks within three different themes and different levels of complexity. During the 16 weeks of the study, the students performed three oral test tasks that were assessed in terms of accuracy, fluency and complexity, using eight different measures. This study found that scaffolding learners in performing tasks with increasing levels of complexity in a multimedia task-based language teaching/learning context, results in improved second language oral production, particularly in terms of accuracy, fluency and complexity.

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)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.007
GPT teacher head0.237
Teacher spread0.230 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Language TeachingSame topicEFL/ESL Teaching and LearningFrench-language works237,207