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Record W2599269676 · doi:10.24908/pceea.v0i0.6474

WHY DOES IT MATTER? EXPLAINING THE IMPORTANCE OF COMPLEMENTARY STUDIES TO FIRST-YEAR ENGINEERING STUDENTS

2017· article· en· W2599269676 on OpenAlexafffundvenueabout
John Donald, Sofie Lachapelle, Thomas Sasso, Kyle Augusto, M. Gloria González‐Morales

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2017
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Guelph
FundersUniversity of Guelph
KeywordsCurriculumSoft skillsAccreditationPsychologyEngineering educationMedical educationMathematics educationPedagogyEngineeringEngineering managementMedicineSocial psychology

Abstract

fetched live from OpenAlex

While complementary studies are an accreditation requirement and feature prominently in the Canadian engineering curriculum, focus-group conversations with upper-year engineering students have indicated that a lack of awareness of, and appreciation for, soft skills development often prevents students from benefiting from complementary studies to the fullest. Given this reported difficulty to grasp the importance of complementary studies, a study was undertaken at the University of Guelph using a quasi-experimental design to explore the possibility that triggering self-assessment and awareness about career development early in the engineering curriculum promotes greater engagement with complementary studies and soft-skill development. First-year engineering students took part in a learner-centered activity focused on the importance of complementary studies for the development of soft skills. Through active learning exercises and case studies of successful engineering graduates, who described the skills and knowledge required to perform their daily work, the session was designed to encourage students to develop greater self-awareness and intentionality about complementary studies and their associated graduate attributes. The outcomes of this activity and issues on how to embed it in the Engineering first-year curriculum will be discussed

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.154
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.011
GPT teacher head0.252
Teacher spread0.242 · 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

Citations3
Published2017
Admission routes4
Has abstractyes

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