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Record W1976085316 · doi:10.4018/ijopcd.2014040105

Exploring Applications for Using Video Podcasts in Online Learning

2014· article· en· W1976085316 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Online Pedagogy and Course Design · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicInnovations in Educational Methods
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsFormative assessmentSummative assessmentComputer scienceMultimediaVariety (cybernetics)Online videoOnline learningVideoconferencingKey (lock)Plan (archaeology)Mathematics educationPsychology

Abstract

fetched live from OpenAlex

The purpose of this paper was to explore research-based applications for using video podcasts in an online learning environment. Five key video podcast uses were examined including administration, instruction, student assignments, feedback, and community. Administrative video podcasts provide course information on areas such as learning goals, lesson plan instructions, course policies, and homework or assignment expectations. Instruction-based video podcasts present short summaries or worked examples for teaching specific concepts. Student assignment video podcasts offer a creative way for students to demonstrate a variety of skills in a wide range of subject areas. Feedback-based video podcasts provide formative guidance to students about their progress or summative evaluation for assignments they complete. Finally, community-based video podcasts help build instructor-to-peer and peer-to-peer connections within an online learning course. Future exploration on the design of video podcasts, regardless of the application used, is discussed.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.792
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.292
GPT teacher head0.522
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