MétaCan
Menu
Back to cohort

Asynchronous Lectures, View-Speed Effect

2021· article· en· W4214875055 on OpenAlexaff
Ahmad Mohammadpanah, Nima Atabaki

Bibliographic record

VenueLiteracy Information and Computer Education Journal · 2021
Typearticle
Languageen
FieldComputer Science
TopicQuantum Computing Algorithms and Architecture
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAsynchronous communicationComputer scienceParallel computingMathematics educationMathematicsTelecommunications

Abstract

fetched live from OpenAlex

A study on the effect of speed-watching of recorded lectures in a second, third-, and fourth-year Mechanical Engineering undergraduate courses and one Graduate level course showed that the view speed (i.e. watching lectures with 1.5 or 2 times faster than normal speed) has no significant effect on the average grade performance. While the data might suggest that the supposed benefits of being able to speed up a lecture (preserving a lecture content while decreasing the amount of time spending on it) does not have any significant effect on a student's understanding of the Engineering topics content, there might be some disadvantages in speedwatching. Through, a post survey of the same sample groups, majority (82%) of the speed watcher reported that they feel a bit impatient when they couldn't "speed up" a live conversation. Some reported the feeling of frustration or a lack of attention when they have to attend a live lecture (real-time speed) or inperson lectures.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.001

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 head0.003
GPT teacher head0.241
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueLiteracy Information and Computer Education JournalSame topicQuantum Computing Algorithms and ArchitectureFrench-language works237,207