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USE OF PODCASTING FOR TEACHING ENGLISH ON THE EXAMPLE OF CBC EDMONTON RESOURCES

2020· article· en· W3118784303 on OpenAlexaboutno aff
Олена Бабенко

Bibliographic record

VenueМолодий вчений · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsInteractivityNoveltyActive listeningComputer scienceContext (archaeology)Process (computing)Reading (process)Mathematics educationMultimediaAdaptation (eye)CognitionPsychologyPedagogyLinguistics

Abstract

fetched live from OpenAlex

The paper is aimed at presenting theoretical and practical facets on the use of podcasting for boosting students’ listening, speaking and reading skills on the example of CBC Edmonton resources in ESL classes. The novelty of the study is in combining theoretical and empirical research through the use of authentic podcasts for teaching English. This paper summarizes the experience of Canadian colleagues in designing a number of various tasks based on audio podcasts. These teaching guides have clear measurable goals and include the following activity resources such as lesson plans, slide shows, ad-free audio for download, episode transcripts, activity sheets, bonus mini-episodes. The only national teacher’s concern is a problem of modification of these materials to make them more appropriate for a target group of ESL learners. Three components, in particular, linguistic, cognitive complexity and communicative peculiarities should be taken into consideration for further adaptation. The practical experience of university lecturers highlights that open educational resources differ from traditional teaching tools by their interactivity, novelty, optimality of their technical characteristics and socio-cultural context. We can emphasize that the implementation of podcasting as an aid into the educational process is necessary to improve the quality of the educational process in general.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.129
GPT teacher head0.242
Teacher spread0.112 · 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 designNot applicable
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

Citations4
Published2020
Admission routes1
Has abstractyes

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