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Record W3002291650 · doi:10.24908/pceea.vi0.13766

DISCOVERE: AN OUTREACH PROGRAM AT THE SCHOOL OF ENGINEERING

2019· article· en· W3002291650 on OpenAlexaffvenueabout
Yang Cao, Seach Chyr Goh, Ahmad Rteil, Debbie Roberts, Kevin Golovin

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOutreachCurriculumVariety (cybernetics)PassionEngineering educationEngineeringEngineering ethicsEngineering managementCivil engineeringPedagogySociologyComputer sciencePolitical sciencePsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Canadian universities are devoting more and more resources to develop K-12 engineering education outreach programs. The School of Engineering at the University of British Columbia’s Okanagan campus offers a variety of outreach and community programs for students of all ages. This paper provides details on the DiscoverE Engineering Summer Program which has been delivered since 2016. The objective of the program is to introduce students general design processes through hands-on civil, electrical, and mechanical engineering projects. The overall goal is to foster a passion in engineering and at the same time attract students who have already had an interest in engineering and are eager to learn more about the discipline. The program is taught by faculty members and exposes students to a variety of hands-on projects in civil, electrical and mechanical engineering. This paper will provide curriculum details for each topic and reflect on the student learning experience based on observations. Future qualitative plans on the measure of the impact of this program qualitatively will be explored.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.209
Threshold uncertainty score0.416

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0130.002
Scholarly communication0.0020.001
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0680.008

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 head0.003
GPT teacher head0.189
Teacher spread0.186 · 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 designNot applicable
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
Published2019
Admission routes3
Has abstractyes

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