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Record W1954521777 · doi:10.24908/pceea.v0i0.5717

Constructing a DC Brushless Motor- an Experiential Engineering Clinic Activity for Mechanical Students

2015· article· en· W1954521777 on OpenAlexaffvenue
Samar Mohamed, Neil Griffet, Sanjeev Bedi

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsExperiential learningDC motorPower (physics)Test (biology)Motor activityExperiential educationPsychologyComputer scienceMathematics educationEngineeringElectrical engineeringMedicine

Abstract

fetched live from OpenAlex

The Engineering Ideas Clinic initiative at the University of Waterloo focuses on designing, developing and implementing learning activities provide students with authentic, applied and hands-on experiences which integrate their learning, and expose them to genuine applications in support of their engineering science content.This paper focuses on “Constructing a Brushless DC Motor” activity which was piloted for second year Mechanical students in their “Electromechanical Devices & Power Processing” course. The main motivation behind designing this activity is the fact that the principles of electromagnetism are often taught in an abstract way, and students can struggle to link the concepts with real life applications. Hence the authors have developed this activity to materialize these abstract concepts to the students.Students in teams of three constructed a brushless DC motor, verify its construction and test its operation. In this activity students applied the principles in an experiential fun activity, compared the performance of their motors and worked collaboratively in teams.In this paper, the authors will share their experience in developing and running this activity. They will also share their observations on the students’ reaction and learning experience, as well as some comments from colleagues

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.267
Teacher spread0.253 · 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.

Study designSimulation or modeling
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
Published2015
Admission routes2
Has abstractyes

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