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Record W2614862484 · doi:10.18260/1-2--1450

Improve Learning Efficiency With Integrated Math And Circuit Simulation Tools In Electrical And Computer Engineering Courses

2020· article· en· W2614862484 on OpenAlexaff
Colin D. Campbell, Fayçal Saffih, K.A. Nigim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of WaterlooMcMaster University
Fundersnot available
KeywordsPlot (graphics)Computer scienceCoupling (piping)SoftwareElectronic circuitSymbolic computationGraphThe SymbolicGraph theoryFunction (biology)Theoretical computer scienceComputer engineeringAlgorithmTopology (electrical circuits)Electrical engineeringMathematicsProgramming languageEngineeringMathematical analysisMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents coupling the use of the TINA circuit simulation software with the Mathcad mathematical software.This coupling permits students to simply (1) enter a circuit in TINA diagramatically, (2) export its symbolic solution y(t), or its transfer function, Y(s), to a Mathcad file, and (3) plot these solutions for multiple values of a parameter (e.g.R) on a 2-D or 3-D graph.The symbolic solutions and plots enhance understanding of both the physical and the mathematical foundations of the studied cases.We envision this coupling being used in classrooms by instructors, and by students.(This coupling only works in the case of linear circuits, so for example it does not work with diodes).

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)0.002
metaresearch head score (Gemma)0.013
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: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0050.004
Open science0.0030.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.007

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 head0.009
GPT teacher head0.202
Teacher spread0.194 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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