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Record W7098134545

ENGINEERING STAKEHOLDERS

2016· article· en· W7098134545 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsRubricAccreditationProcess (computing)Set (abstract data type)Engineering educationAuthentic assessment
DOInot available

Abstract

fetched live from OpenAlex

Abstract- This paper discusses the evolution of a set of rubrics for the 12 CEAB graduate attributes in the Faculty of Engineering at the University of Manitoba. The rubrics are intended as a pedagogical assessment tool for instructors of individual courses as applicable, and for assessment at the program level. Individuals from faculty, industry and the University of Manitoba Centre for the Advancement of Teaching and Learning have been involved in the process of evaluating and revising both the content and wording of the rubrics in order that they meet the following criteria: (i) the foci and indicators adequately communicate the knowledge, skills, attitudes, values and behaviours that our engineering stakeholders agree do define each attribute; (ii) the competency level for each indicator is representative of what engineering educators and stakeholders agree defines proficiency; and (iii) the language in the rubrics is consistent and agreeable to all engineering stakeholders. These rubrics are expected to accomplish a number of outcomes-based pedagogical and accreditation goals, including: dividing the attributes into teachable and measurable foci and indicators; defining competency levels; and becoming a vehicle for the development of a common language for faculty, students and industry when they discuss, teach, assess and acquire the knowledge, skills and behaviours of the CEAB graduate attributes. This paper reports on the evolution of these rubrics, and outlines plans for their continued development and use within the faculty.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.674
Threshold uncertainty score0.705

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)0.0010.000

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 head0.017
GPT teacher head0.178
Teacher spread0.161 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2016
Admission routes1
Has abstractyes

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