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Record W2032099412 · doi:10.1109/fie.2014.7044060

Enhancing collaboration and dissemination through a faculty scheduled development management framework

2014· article· en· W2032099412 on OpenAlexaff
Shohreh Hadian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsCamosun College
Fundersnot available
KeywordsProfessional developmentFaculty developmentContext (archaeology)Tracking (education)Knowledge managementMedical educationDiversity (politics)Computer scienceEngineering managementBusinessEngineeringPsychologyMedicinePolitical sciencePedagogy

Abstract

fetched live from OpenAlex

Colleges provide professional development opportunities to faculty to promote knowledge growth and improvement of skills. At Camosun College, Scheduled Development (SD) time for faculty is based on the educational practice and recognition of the need for continuous professional development of faculty members. The paper presents the development of an online environment to enhance and promote active collaboration among faculty in the context of professional development. The Scheduled Development (SD) Connect tool is under development, implementation and is currently undergoing testing. SDConnect® is a multi-facet targeted software tool that aids in dissemination, tracking, integration, and collaboration, of faculty SD for post secondary institutions. The tool will also enable Camosun College faculty and staff to access the information database on SD activities, with the goal that it may lead to active collaborations and synergies among the faculty, departments and schools. This project will also enable the administration to efficiently administer SD proposals and approvals and to establish historical records and trends of the SD activities of faculty for better resource management. SDConnect® is part of an ongoing project called DEAL (Diversity In an Environment of Accessible Learning) at Camosun College.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.410
Teacher spread0.388 · 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
Published2014
Admission routes1
Has abstractyes

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