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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 OpenAlex

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.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
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

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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Same topicExperimental Learning in EngineeringFrench-language works237,207