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Record W2605144836 · doi:10.24908/pceea.v0i0.6508

TEN YEARS OF ENHANCING ENGINEERING EDUCATION WITH CASE STUDIES: INSIGHTS, LESSONS AND RESULTS FROM A DESIGN CHAIR

2017· article· en· W2605144836 on OpenAlexaffvenue
Steve Lambert, Cheryl Newton, David Effa

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2017
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsImplementationEngineering educationEngineering managementEngineeringGovernment (linguistics)Mathematics educationComputer scienceSoftware engineeringPsychology

Abstract

fetched live from OpenAlex

Improving student learning and increasing connections between theory and engineering practice captures the main goals of Waterloo Cases in Design Engineering (WCDE). WCDE is a group at the University of Waterloo (Waterloo) that was established in 2005 as a part of the NSERC Chairs in Design Engineering program. WCDE works with instructors, industry and students to bring real life complexities to the classroom by using authentic case studies. Over the last ten years, more than 175 case studies have been developed and implemented in more than 100 courses with over 125 instructors, across all engineering disciplines. WCDE has collaborated with more than 140 industry, government, non-profit, and academic case partners for the development and implementation of case material.Surveys are used to gauge students’ receptivity to case implementations and for continuous improvement. Student feedback from WCDE case implementations are presented and discussed. The benefits and challenges of case study teaching are discussed, along with reflections on the next steps towards extensive use of engineering cases in education.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.248
Teacher spread0.230 · 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.

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

Citations4
Published2017
Admission routes2
Has abstractyes

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