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A Teaching Assistant for Microelectronic Circuits Problems

2021· article· en· W3207557835 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 Waterloo
Fundersnot available
KeywordsTriodeElectronic circuitComputer scienceMicroelectronicsTransistorElectronic engineeringDiodeKey (lock)Electrical engineeringComputer engineeringEngineeringCapacitorVoltage

Abstract

fetched live from OpenAlex

Ever since their introduction, personal computers have been used as a tool for education. An Intelligent Tutoring System (ITS) is one such tool that can provide automated feedback to students when solving problems. This paper discusses the design and implementation of an ITS to aid instructors of microelectronic circuits, a topic often taught to undergraduate electrical and computer engineering students. The proposed ITS allows an instructor to create and add problems related to metal-oxide-semiconductor (MOS) transistors, and to simulate the underlying circuits using the commonly available LTspice tool. Students are then able to load that problem and enter equations to solve it, while the ITS provides immediate feedback and hints, as applicable. Currently, the proposed ITS can handle MOS transistors at DC, including Ids equations for triode and saturation regions of operation. The intent is to extend the capabilities of the proposed ITS to handle different circuit elements such as diodes and bipolar transistors, and to facilitate open-ended design problems.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.783
Threshold uncertainty score0.537

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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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