MétaCan
Menu
Back to cohort
Record W4412870732 · doi:10.24908/pceea.2025.19698

In-Person Versus Virtual Case-Based Learning for Advanced Course in Engineering Technology Education

2025· article· en· W4412870732 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCourse (navigation)Virtual learning environmentMathematics educationComputer scienceEngineeringEngineering managementEngineering ethicsMedical educationPsychologyMultimediaMedicine

Abstract

fetched live from OpenAlex

Case-based learning (CBL) is an active learning instructional technique used to extend a more engaging learning experience to students. The purpose of this pedagogical study is to analyze the individual opinions of students participating in CBL. Students enrolled in the undergraduate course Advanced Biotechnology in the academic years 2021 and 2022 were asked to compare their opinions on an in-person CBL versus a virtual CBL environment. While most of the students favoured an in-person CBL setting, it was still found that CBL, regardless of whether it was offered in an in-person or a virtual platform, proved to improve essential learning and cognitive skills. These are comprised of critical thinking, problem solving, teamwork, communication, real-life technical skills, course performance, self-confidence, concept understanding and application, deeper understanding, and an overall positive learning experience.

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)0.005
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 score0.753
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.006
GPT teacher head0.268
Teacher spread0.262 · 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