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Record W4200500304 · doi:10.5539/elt.v15n1p154

Radio Drama Competition as an Effective Tool to Boost the Motivation and Self-Confidence of Primary and Secondary School English Learners in Hong Kong

2021· article· en· W4200500304 on OpenAlexvenueno aff
Kevin Kai-Wing Chan, William Ko Wai Tang

Bibliographic record

VenueEnglish Language Teaching · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicSubtitles and Audiovisual Media
Canadian institutionsnot available
FundersStanding Committee on Language Education and Research
KeywordsPsychologyDramaCompetition (biology)Self-confidenceLanguage artsPedagogyTeamworkLanguage acquisitionThe artsMathematics educationSocial psychologyVisual artsManagement

Abstract

fetched live from OpenAlex

In this report, we investigate the use of a radio drama competition to boost motivation, self-confidence, and cooperation in language learning for primary and secondary school students in Hong Kong. The results suggest the radio drama competition had a positive impact on increasing motivation, collaboration, and confidence in language learning.  For the study, we used online surveys and interviews with students and teachers who participated in the radio drama competition to examine their perceptions of the competition. We have included the surveys and interview results of two competitions in consecutive years, and both years’ results indicate students had positive views about their experience. Both students and teachers believed the competition enhanced collaboration and teamwork, confidence, and communication skills most.  This paper contributes to the literature by shedding light on the pedagogical implications of English teachers incorporating more radio drama and language arts into their classrooms to improve students’ language learning. Well-selected language arts materials could increase students’ language learning process as well as their motivation and self-confidence to learn the target language.

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)0.002
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 score0.069
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.223
Teacher spread0.216 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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