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Record W1836517004 · doi:10.24908/pceea.v0i0.4903

PEDAGOGICAL RESEARCHES CONDUCTED AT UNIVERSITÉ DE SHERBROOKE

2013· article· en· W1836517004 on OpenAlexvenueaboutno aff
G. Lachiver

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2013
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumPortfolioEngineering managementSoftware deploymentEngineering educationEngineeringActive learning (machine learning)Professional developmentKnowledge managementComputer scienceEngineering ethicsPedagogySoftware engineeringPsychologyArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

The USherbrooke Faculty of Engineering is recognized as a leader in innovation and research in engineering education. The Université de Sherbrooke was the second university in Canada to offer co-op programs for its students in 1966 and is now among the top 10 higher education institutions in North America for the significance of its co-op system. The faculty of engineering was the first in Canada to offer an undergraduate mechanical engineering program based on professional competencies with design as the ultimate competency integration activity - the backbone of the entire program. In 2001, we introduced completely redesigned electrical and computer engineering programs based on two complementary frameworks. The first one is a competency-based framework used to have a better alignment between teaching/learning activities, program objectives and competences development. The second one, called the learning framework, introduces a paradigm shift from passive to active learning methodologies with the deployment of problem and project based learning situations. Over the years, the faculty of engineering has developed many original approaches to both design of curriculum and faculty organisation. We also developed considerable expertise to improve teaching and learning especially integration of curriculum elements, the development of team skills and of a professional culture, the use of design projects extending over more than one year with links to industry, the use of portfolio to track competencies development, active learning environment such as problem and project-based learning and in utilising novel assessment techniques to improve learning. All these initiatives have been made possible by creating winning conditions to involve faculty members in pedagogical research activities and implementation methodologies. To do that we provided institutional financial support (faculty, university), encouragement, teaching load relief, recognition of faculty involvement in tenure and promotion, professional support for CEAB accreditation requirements, etc.

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)0.005
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.426

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0100.003
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.003

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 head0.014
GPT teacher head0.209
Teacher spread0.196 · 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
Published2013
Admission routes2
Has abstractyes

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