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Record W2600259897 · doi:10.24908/pceea.v0i0.6523

REDESIGNING THE UBC FIRST YEAR INTRODUCTION TO ENGINEERING: SUCCESSES AND CHALLENGES

2017· article· en· W2600259897 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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2017
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of British Columbia
FundersUniversity of British ColumbiaDalhousie University
KeywordsCurriculumGrading (engineering)ConstructiveSustainabilityEngineering educationEngineering ethicsComputer scienceEngineeringEngineering managementPedagogyProcess (computing)SociologyCivil engineering

Abstract

fetched live from OpenAlex

A new first year introduction to engineering experience was developed at the University of British Columbia. This paper provides an overview of the two new courses and the lessons learned both in developing and delivering the courses. Several key problematic areas in the previous curriculum were addressed, namely, to improve student connection with the engineering profession, increase design and practical engineering experiences, more effectively integrate sustainability into the curriculum, and better emphasize the human and social connection to engineering.The courses operate in a flexible learning framework with a sequence of online, lecture, and studio components arranged in a whole-part-whole format delivered to a class of 850 students. Elements of numerous effective course design, teaching and learning practices, including integrated course design, constructive alignment, components of Team-Based Learning, classroom assessment techniques, peer evaluation, and peer grading were incorporated into these courses. Student feedbackthrough surveys has shown that the new format has been highly successful in addressing most of the key high-level goals, such as establishing a student connection to the engineering profession, helping students understand what engineers do and how they do it, and providing an introduction and appreciation for design, sustainability, decision-making, professionalism, and ethics..

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.008
GPT teacher head0.192
Teacher spread0.184 · 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