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Record W2314987373 · doi:10.7227/ijmee.35.2.1

A Freshman Engineering Curriculum Integrating Design and Experimentation

2007· article· en· W2314987373 on OpenAlexaboutno aff
Elizabeth DeBartolo, Risa Robinson

Bibliographic record

VenueInternational Journal of Mechanical Engineering Education · 2007
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsnot available
FundersKate Gleason College of Engineering, Rochester Institute of Technology
KeywordsCurriculumTeamworkClass (philosophy)EngineeringQuarter (Canadian coin)Engineering managementCourse (navigation)Engineering design processMathematics educationComputer scienceMechanical engineeringPedagogyPsychologyArtificial intelligenceManagement

Abstract

fetched live from OpenAlex

A pilot freshman curriculum has been designed and implemented in the Mechanical Engineering Department at the Rochester Institute of Technology. The four-course sequence gives freshmen an overview of a broad range of mechanical engineering activities, ranging from system design and project management, electronics and programming, to technical writing and presentations. Students take a two-quarter ‘Introduction to Mechanical Engineering Design’ course and a two-quarter ‘Measurements, Instrumentation, and Controls’ course. In each sequence, the first course gives students most of the basic tools they will need and the second is centered on an electromechanical Rube Goldberg design project, undertaken by the whole class. Students develop the design concept, build the system, and prove that it works. They are able to practice skills such as communications, teamwork, time management, and experimentation. The integrated first-year course sequences have been offered for two years and have proved successful. The students were clearly satisfied with their experience.

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)0.010
metaresearch head score (Gemma)0.009
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: Methods · Consensus signal: Methods
Teacher disagreement score0.041
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0040.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0410.014

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 head0.296
Teacher spread0.285 · 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
GenreMethods

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

Citations16
Published2007
Admission routes1
Has abstractyes

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