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Record W1882055512 · doi:10.24908/pceea.v0i0.4686

Student perceptions and reported use of video recorded lectures in engineering courses

2012· article· en· W1882055512 on OpenAlexaffvenue
A. L. Steele, Cheryl Schramm

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicInnovations in Educational Methods
Canadian institutionsCarleton University
Fundersnot available
KeywordsAttendanceLikert scaleMedical educationPsychologyPerceptionCourse (navigation)Mathematics educationGraduate studentsMultimediaComputer scienceEngineeringMedicine

Abstract

fetched live from OpenAlex

Between 2008 and 2010 an introductory circuit analysis course for second year engineering students had its lectures recorded (2008 was audio only, other years were by video) and the recordings were made available to registered students as a supplemental resource. Attendance to lectures was still required. In 2011 an introductory programming course was recorded in a similar way. In each of these offerings the students were anonymously surveyed at the end of the course using an online survey tool with most questions using a five point Likert category scale. The survey looked at the perceived usefulness of the recordings, the approach to watching and the impact on attendance. The responses showed strong support to having video lecture capture and the reported use of the videos was to watch selected material. There was a difference between the courses on the impact on attendance, with the circuit analysis course indicated little impact on attendance, whereas the responses from the other course indicates more missed lectures due to the availability of recordings.

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)0.017
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 score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.328
Teacher spread0.302 · 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

Citations0
Published2012
Admission routes2
Has abstractyes

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