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Streaming Video and Podcasting Enhancements in the Post-secondary Classroom

2012· article· en· W2509548607 on OpenAlex
Linda Chmiliar

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 for Digital Society · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicInnovations in Educational Methods
Canadian institutionsAthabasca University
Fundersnot available
KeywordsMultimediaComputer science

Abstract

fetched live from OpenAlex

The use of online learning enhancements is becoming more popular in post-secondary instruction, and certainly distance education and lecture based institutions are exploring the use of elearning strategies. Unfortunately, there is very little available research in the literature that examines the use of streaming video and podcasting as enhancements to in class post-secondary instruction. This paper explores the use of short streaming video clips and audio podcasts with students enrolled in a lecture based pre-service teacher education class. Seven short video clips were developed that extended the classroom lecture material. The clips consisted of Power Point slides enhanced with voice, diagrams, and pictures. The clips were also made available to the students as audio only podcasts. Students voluntarily chose whether or not to view the video clips or listen to the audio only podcasts. A supporting study guide and workbook were also provided online. Student perspectives on the use of these online enhancements were gathered during the course feedback process. The benefits and pitfalls of using streaming video/podcasts as part of postsecondary instruction are 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.533

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 studies0.0010.000
Scholarly communication0.0010.002
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 head0.043
GPT teacher head0.414
Teacher spread0.371 · 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