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Record W2886039228 · doi:10.24908/pceea.v0i0.7425

Assessing the Efficacy of Online Lecture Modules in a Core Mechanical Engineering Undergraduate Course

2017· article· en· W2886039228 on OpenAlexafffundvenue
Mohammed S. Taboun, Robert W. Brennan

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of Calgary
FundersSuncor Energy IncorporatedUniversity of Calgary
KeywordsSession (web analytics)Class (philosophy)Course (navigation)Flipped classroomContent deliveryOnline courseBlended learningMultimediaComputer scienceOnline learningCore (optical fiber)Mathematics educationEducational technologyWorld Wide WebPsychologyEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In recent years, there has been a growing interest in flipped delivery of undergraduate courses. There has also been an interest in blending online learning with traditional, in-class learning. In this paper, the efficacy of a blended online course is assessed based on the second-year mechanical engineering course “Computing Tools for Engineering Design” for the Fall 2016 semester. This is an extension of a Fall 2015 study in the same course where traditional lectures were used. This study examines how the online modules are used by the students, as well as students’ opinions on the video effectiveness. The results of the study painted a picture of a typical flipped delivery student: one who streams the content on a personal device/computer before the in-class session, and tends to stop/rewind the content rather than playing it continuously. Student impressions of the mode of delivery were generally positive, indicating that a combination of online lectures and in-class practice sessions support learning.

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 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)0.003
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.033
GPT teacher head0.359
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

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
Published2017
Admission routes3
Has abstractyes

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