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Record W2887792599 · doi:10.24908/pceea.v0i0.7347

Tron Days: Horizontal Integration and Authentic Learning

2017· article· en· W2887792599 on OpenAlex
Eugene Li, Chris Rennick, Carol Hulls, Mary Ann Robinson, Michael Cooper-Stachowsky, Eline Boghaert, William Melek, Sanjeev Bedi

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2017
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMechatronicsEvent (particle physics)Style (visual arts)Connection (principal bundle)Science and engineeringMathematics educationComputer scienceEngineeringPsychologyVisual artsEngineering ethicsArtificial intelligenceArtMechanical engineering

Abstract

fetched live from OpenAlex

Abstract—First year Mechatronics students at the University ofWaterloo consistently do not see the connection between their fundamentalmath and science courses with the practise of engineering.To address this issue, the first year instructors came together tolaunch a two day Hackathon style project for the students calledTron Days. Tron Days featured small warm up problems dealingwith advanced concepts in each of the courses, and big problemsthat drew from all of the first year courses. The challenges onlyhad communication marks associated with them and provided anopportunity for sustained engagement with the concepts. The metricsused to measure the event showed that it was successful at addressingthe desired outcomes, but could be further enhanced to address morematerial.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.566

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 head0.004
GPT teacher head0.185
Teacher spread0.181 · 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