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Record W2955833368 · doi:10.21315/eimj2019.11.2.5

Podcasting 101: Top Tips on Educational Podcasting

2019· article· en· W2955833368 on OpenAlexaff
Alireza Jalali, Safaa El Bialy

Bibliographic record

VenueEducation in Medicine Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRSSMultimediaComputer scienceCurriculumThe InternetWeb syndicationWorld Wide WebSummative assessmentPublicationMedical educationMedicinePsychologyFormative assessmentMathematics educationPedagogy

Abstract

fetched live from OpenAlex

Medical education has seen significant progress and innovation over the last decade. Today's students utilise a variety of contemporary devices that have replaced the good old pencil and pen. During preclinical years, the students carry around tablet-PCs instead of notebooks to access the web-based curriculum. In their clinical years, smartphones have largely replaced reference books. As much as the teaching methods have revolutionised in medical education, one reality remains constant: during their first two years of study, medical students need to absorb a tremendous volume of information. Students are further challenged by a lack of study time prior to writing summative examinations. Podcasting is a method for distributing multimedia audio and video files over the Internet using the Really Simple Syndication (RSS) format; these can be played back on mobile devices and personal computers. RSS is a web feed format used to publish frequently updated content on the web. In implementing an educational podcasting project, the investigator recommends following the five steps of the Instructional Design Process: Define, Design, Develop, Delivery, and Demonstrate. The following tips are intended to help the reader with design, production and publication of a successful educational podcast.

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)0.003
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.100
GPT teacher head0.467
Teacher spread0.367 · 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 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

Citations10
Published2019
Admission routes1
Has abstractyes

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