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Record W3001792096 · doi:10.24908/pceea.vi0.13874

CHALLENGING STUDENTS TO REDISCOVER ENGINEERING

2019· article· en· W3001792096 on OpenAlexafffundvenue
Wayne Chang, William Bishop, Emily Peat, Eyram Dornor

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of Waterloo
FundersUniversity of Waterloo
KeywordsCreativitySession (web analytics)Experiential learningEngineering educationEntrepreneurshipPoint (geometry)PsychologyWork (physics)Dimension (graph theory)Mathematics educationEngineeringPedagogyComputer scienceEngineering managementPolitical scienceMathematicsMechanical engineeringSocial psychology

Abstract

fetched live from OpenAlex

The "Conrad Games in Engineering" encourages engineering students to develop entrepreneurship and innovation mindsets. Through a series of lunchtime sessions, the games re-awaken the fun and creativity that once encouraged students to pursue engineering. Students form teams to work on a design challenge that is introduced at the start of a session. Students accumulate points for their teams and houses, i.e. engineering departments. The point system is specifically designed to encourage students to take calculated risks and to challenge themselves. Design challenges are specifically chosen to be fun, engineering activities that last no more than 20 minutes. Special challenge teams, composed of faculty, alumni, and staff are invited to compete against the students for an additional dimension of risk and reward. Students apply the experiential learning cycle by revisiting challenges with additional criteria. Through this iterative approach, the games reinforce good engineering practice as well as group collaboration, communication, creativity and leadership skills.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.386
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.002
GPT teacher head0.180
Teacher spread0.178 · 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

Citations0
Published2019
Admission routes3
Has abstractyes

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