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Record W2909714530 · doi:10.24908/pceea.v0i0.13019

An experimental lab to enhance undergraduate electromagnetics education

2018· article· en· W2909714530 on OpenAlexafffundvenue
David Garrett, Elise Fear

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsElectromagneticsCurriculumField (mathematics)Computer scienceVisualizationFocus (optics)Computational electromagneticsElectromagnetic theoryElectromagnetic fieldEngineeringElectrical engineeringMechanical engineeringEngineering physicsPhysicsMathematicsPedagogyOptics

Abstract

fetched live from OpenAlex

Electromagnetic field theory is a required part of the undergraduate electrical engineering curriculum. This material is taught in either one or two semester courses, encompassing concepts related to static and time-varying fields. These classes often focus on Maxwell’s equations and application of mathematical tools required for problem solving. Simulation and visualization tools are often used to enhance student understanding of field distributions and wave propagation. However, hands-on experiences have the potential to deepen understanding and emphasize connections between theory and real-world applications. As RF and microwave equipment is expensive, it is difficult to sustainably outfit laboratories for large undergraduate classes. In this paper, we describe a low-cost, hands-on experimental lab that involves students designing and implementing an antenna. This lab is designed to reinforce concepts related to wave propagation. This work adds to the few papers in the literature reporting incorporation of innovative and low-cost experiments into the undergraduate electromagnetics curriculum.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0370.008

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 head0.002
GPT teacher head0.229
Teacher spread0.227 · 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 designBench or experimental
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
Published2018
Admission routes3
Has abstractyes

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