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Record W2259641476

The Co-Op portfolio: An essential tool for assessment and student development in co-operative engineering programs

2009· article· en· W2259641476 on OpenAlexaff
Jennifer Johrendt, Pawan Kumar Singh, Schantal Hector, Michelle Watters, Geri Salinitri, Karen Benzinger, Arunita Jaekel, Derek O. Northwood

Bibliographic record

Venue20th Annual Conference for the Australasian Association for Engineering Education, 6-9 December 2009: Engineering the Curriculum · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsRubricPortfolioCareer portfolioSummative assessmentMathematics educationScope (computer science)Medical educationPedagogyPsychologyComputer scienceCareer developmentBusinessFormative assessmentMedicineFinance
DOInot available

Abstract

fetched live from OpenAlex

The Centre for Career Education at the University of Windsor has recently introduced a learning portfolio as part of its cooperative education program. The portfolio doubles as a reflective activity for students and a resource for assessment of learning outcomes achievement. The portfolio provides evidence of students' accomplishments, skills and abilities; and documents the scope and quality of their experience and training throughout the cooperative education program. An assessment rubric has been developed for reviewing each student's co-op portfolio, assessing the format, organization, and included documents. In addition, by reviewing key portfolio inclusions, the Centre for Career Education uses a random sample of portfolios to assess the extent to which students have achieved a series of pre-set learning outcomes.

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)0.028
metaresearch head score (Gemma)0.089
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.089
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.006
Science and technology studies0.0010.001
Scholarly communication0.0060.006
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.011

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 head0.384
Teacher spread0.367 · 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 designQualitative
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

Citations3
Published2009
Admission routes1
Has abstractyes

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Same venue20th Annual Conference for the Australasian Association for Engineering Education, 6-9 December 2009: Engineering the CurriculumSame topicReflective Practices in EducationFrench-language works237,207