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Record W1811836567 · doi:10.24908/pceea.v0i0.4671

AN INTERACTIVE WEB-BASED PEDAGOGICAL TOOL FOR THE TRAINING OF ENGINEERS IN ELECTRICAL ENGINEERING

2012· article· en· W1811836567 on OpenAlexaffvenue
Lyne Woodard, Alexandre Hatin Limoges, Nicolas Constantin, Sylvie Ratté, Vahé Nerguizian

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2012
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsCurriculumWeb applicationTraining (meteorology)Computer scienceControl (management)Range (aeronautics)MultimediaEngineering managementEngineeringWorld Wide WebPsychologyArtificial intelligencePedagogy

Abstract

fetched live from OpenAlex

The electrical engi neering curriculum at ÉTS includes industrial training which introduces interruption periods in the academic training. In addition, as the program contains a broad range of related courses, theoretical concepts need continuous revision and followups by students. Thus, considerable effort is required from the students and professors to review t hese concepts through various courses. As a solution to this concern, a motivating web-based pedagogical tool has been created. This tool has been used in a linear control course with traditional laboratories and lecturing. The r esults of student satisfaction obtained so far show that this tool is attractive for students and help them in their revision and learning. Moreover, it provides statistics to the professor helping the evaluation of the understanding level of the group through the entire semester. This paper presents the main features, challenges and results of the web-based pedagogical developed tool.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.004

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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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
Published2012
Admission routes2
Has abstractyes

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