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Record W7028325127

Faculty Engagement in Co-op Competency-Based Learning

2014· dissertation· en· W7028325127 on OpenAlexfundno aff

Bibliographic record

VenueArca (British Columbia Electronic Library Network) · 2014
Typedissertation
Languageen
FieldMathematics
TopicProbability and Statistical Research
Canadian institutionsnot available
FundersKwantlen Polytechnic University
KeywordsPosition (finance)Value (mathematics)Student engagementHigher educationExperiential learningStrategic planningProgram evaluationDistance education
DOInot available

Abstract

fetched live from OpenAlex

The objective of this research study was for the university to benefit from an enhanced educational and learning-based co-op program that is in alignment with its strategic vision. The Co-Op department would be better equipped to facilitate programs that maximize student learning and be in a position to communicate the educational value of co-op and its significance to institutional plans. Finally, my hope for the research was to engage and inspire Co-Op faculty at KPU to make enhancements to the program that enabled student learning and program growth and considered the supports and resources they require to feel engaged. The question this raised for the KPU Co-Op program was: How can the Centre for Cooperative Education and Career Services engage Co-Op faculty in contributing to the long-term success of a competency-based learning model in Cooperative Education at Kwantlen Polytechnic University (KPU)?

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)0.017
metaresearch head score (Gemma)0.050
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.050
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.006
Scholarly communication0.0120.006
Open science0.0020.020
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.002

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 head0.027
GPT teacher head0.307
Teacher spread0.281 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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