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Record W7053365676

Using Open Educational Practices to Support Institutional Strategic Excellence in Teaching, Learning & Scholarship

2015· article· en· W7053365676 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsnot available
Fundersnot available
KeywordsExcellenceScholarshipInstitutionBest practiceHigher educationWork (physics)Process (computing)
DOInot available

Abstract

fetched live from OpenAlex

This paper explores the integration of Open Educational Practices (OEP) into an institutional strategy to develop distinctive excellence in teaching, learning and scholarship. The institution in the case study is a public polytechnic university serving a metropolitan area in Canada. If emerging Open Educational Practices are to flourish at our university, support for OEP must integrate with and contribute to our broader efforts to clarify and enhance our strategic position. We have identified three focal points where our institution can focus attention in order to ensure that our use of emerging Open Educational Practices will best align with, contribute to, and benefit from our institutional strategy for distinctive excellence in teaching and learning: • Opening up the pedagogy underlying exemplary OER, to enable a deeper faculty engagement in integrating and mobilizing diverse sources of knowledge in teaching; • Opening up that process by which individual faculty improve teaching and learning, as a model for our students’ own engagements with knowledge; • Opening up our collective faculty work in innovation networks, as a model for students and as a signature institutional strength and outcome. We summarize the rationale and planned next steps for each of these focal points, which are intended to cumulatively build on each other as a value chain to support the development of distinctive graduate capabilities as signature outcomes of our teaching and learning.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0030.006
Open science0.0030.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.670
GPT teacher head0.662
Teacher spread0.008 · 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 designObservational
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

Citations2
Published2015
Admission routes1
Has abstractyes

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