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Record W1890939794 · doi:10.24908/pceea.v0i0.3079

Interactive First-year Engineering Seminar Series

2010· article· en· W1890939794 on OpenAlexaffvenueabout
Jason Bazylak, Susan McCahan

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2010
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSeries (stratigraphy)Computer scienceGeology

Abstract

fetched live from OpenAlex

Six years ago, a Seminar Series was implemented in the Engineering Strategies and Practice course for first-year engineering at the University of Toronto. The learning objectives of the seminar series are to teach students communication as an engineer, independent learning, and systems thinking. Additionally the seminar series creates a forum for faculty, and other professional mentors, to imbue a passion for learning in a more intimate environment than is normally associated with first-year courses. The interactive seminar series has proven popular with both the students and the 40+ volunteer seminar leaders recruited from academia, industry, the local community, and non-profit organizations. These seminar leaders mentor their students through a discussion of the technical, social, environmental, economic, legal, ethical, political, and/or human factors associated with an engineering-related topic of the seminar leader's choosing. The past, present, and future of the seminar series will be discussed in this paper. By measure of self-reported growth the learning objectives are being met and as such the seminar series is considered a success. It is predicted to continue, with goals of continuous improvement, for years to come.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.003
GPT teacher head0.187
Teacher spread0.184 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations1
Published2010
Admission routes3
Has abstractyes

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