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Record W2118433811 · doi:10.5539/ass.v9n9p224

Enhancing ESL Learners Speaking Skills through Asynchronous Online Discussion Forum

2013· article· en· W2118433811 on OpenAlexvenueno aff
Nadzrah Abu Bakar, Hafizah Latiff, Afendi Hamat

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
FundersUniversiti Kebangsaan Malaysia
KeywordsAsynchronous communicationTask (project management)Online discussionPsychologyDiscussion boardForeign languageLanguage proficiencyMathematics educationComputer sciencePedagogyMedical educationMultimediaWorld Wide Web

Abstract

fetched live from OpenAlex

Communicating orally in a second or foreign language such as English can be a difficult task especially for the low proficiency learners as they may lack the confidence and feel shy and apprehensive when interacting verbally in the target language. This may hold them back from expressing themselves vocally though they are fully aware that in order to be fluent in the target language, they need to practise speaking not only in the classroom but also outside the classroom. This paper discusses the use of an asynchronous online discussion forum (AODF) as a communication tool to assist the low proficiency ESL learners to build their confidence and practise using the target language orally. As an online discussion forum, the Multimedia Enhance Discussion Forum (MEDiF) was specifically designed to be used by a selected group of low proficiency ESL learners at tertiary level for one academic semester. This online forum allows the learners to audio and video-record their discussions, listen to the recorded discussions and respond to their friends’ ideas and opinions. Interviews and observations of the discussions on the MEDiF were conducted to gather data. The findings generally indicate positive responses from the ESL learners although there were also some obstacles faced by them while utilizing the MEDiF.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.322
Teacher spread0.311 · 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.

Study designOther design
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

Citations34
Published2013
Admission routes1
Has abstractyes

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