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Record W3127432443 · doi:10.1080/01587919.2020.1869521

Can lecture capture contribute to the development of a community of inquiry in online learning?

2021· article· en· W3127432443 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

VenueDistance Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsMastercard Foundation
Fundersnot available
KeywordsCommunity of inquiryDistance educationOnline communityScrutinyPerceptionOnline learningPsychologyNaturalistic observationOnline videoValue (mathematics)PedagogyMathematics educationCognitionMultimediaComputer scienceSocial psychologyWorld Wide Web

Abstract

fetched live from OpenAlex

This paper presents findings from a study into the value of lecture captures for online postgraduate courses. There has been little scrutiny of the role of on-campus lecture capture in online courses. We addressed this gap by exploring online distance learning students’ perceptions of lecture captures through the lens of the community of inquiry framework. We found that students were enthusiastic about campus lecture captures due to their naturalistic lecturing style and the opportunity to learn vicariously. However, students also expressed preferences for video material that was produced specifically for online audiences. Overall, we found that lecture captures do not contribute to the creation of a community of inquiry as there is no substantial increase in cognitive, social, or teaching presence for online students. We suggest greater consideration of the role of vicarious learning in online education and the tendency to perceive campus-based education as more authentic.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.027
GPT teacher head0.353
Teacher spread0.326 · 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