MétaCan
Menu
Back to cohort
Record W4239235732 · doi:10.18260/1-2--35635

Work in Progress: Exploring Pedagogical Alternatives for Incorporating Simulations in an Introductory Power Electronics Course

2020· article· en· W4239235732 on OpenAlexaff
Mohamed Elshazly, Hamid Timorabadi

Bibliographic record

Venue2020 ASEE Virtual Annual Conference Content Access Proceedings · 2020
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWorkloadComputer scienceBridging (networking)WorkstationPower electronicsElectronicsSet (abstract data type)Engineering managementSoftware engineeringComputer engineeringElectrical engineeringEngineeringVoltageOperating system

Abstract

fetched live from OpenAlex

In Fall 2018, we developed LabSim, a set of circuit simulators for a power electronics fundamentals course based on a combination of Simulink and PLECS visual blocks.LabSim was developed to provide third-year electrical engineering students with an avenue for independent exploration beyond theory-heavy lectures and strictly controlled labs.The pedagogical approach then was to offer LabSim as a completely voluntary ancillary tool, with no involvement in lab or tutorial assignments, in order to minimize additional workload.While a student survey showed that LabSim accomplished its main goal of bridging the gap between lectures and labs, a common theme in the provided feedback was that students would benefit more from LabSim if it were incorporated more directly into their assignments.In response, we have updated the pedagogical approach in the Fall 2019 offering of the course to involve developing simulationcentred questions in pre-lab preparation assignments, dedicating office hours for simulationrelated questions, developing additional simulators for crucial converter circuits, and providing dedicated simulation workstations on campus.We present this updated pedagogical approach in detail along with an overview of technical updates and examples from the aforementioned assignments.We also present an evaluation of this new approach through an end-of-semester student survey coupled with LabSim usage data collection in assignments where it is optional. Motivation & Introduction to Fundamentals of Electrical Energy Systems (FEES)Electrical engineering students specializing in energy systems are introduced to power electronics in their third year at our university.The introductory course, entitled Fundamentals of Electrical Energy Systems (FEES), covers DC/DC converters, DC/AC converters, three-phase circuits, as well as the basics of transformers and electrical machines.The bulk of the course material, however, is dedicated to switch-mode power conversion.The majority of lectures and tutorials are dedicated to explaining relevant concepts such as switch realization, pulse-width modulation (PWM), harmonics/power quality, and continuous/discontinuous conduction modes.Four out of a total of six laboratory sessions are centred around power electronic circuits.Prior to Fall 2018, student feedback consistently revealed some shortcomings with the pedagogical structure of FEES.The course was rigidly divided into a theoretical portion (lectures + tutorials) and a practical portion (laboratory sessions).The lectures are math heavy and packed with information, while laboratory sessions are strictly controlled due to the safety concerns associated with high-power circuits.Students are expected to follow and understand the theoretical operation of switch-mode circuits in lectures and tutorials, which requires proficiency in many areas of fundamental science and electrical engineering such as circuit theory, electronic devices, signal processing, and complex analysis.The lab sessions, while highly beneficial in

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)0.015
metaresearch head score (Gemma)0.041
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: none
Teacher disagreement score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0090.007
Open science0.0040.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.005

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 head0.150
GPT teacher head0.338
Teacher spread0.188 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venue2020 ASEE Virtual Annual Conference Content Access ProceedingsSame topicExperimental Learning in EngineeringFrench-language works237,207