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Using E-Learning to Promote Excellence in Polytechnic Education

2007· book-chapter· en· W2488969093 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIGI Global eBooks · 2007
Typebook-chapter
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsBritish Columbia Institute of Technology
Fundersnot available
KeywordsExcellenceCitizen journalismProcess (computing)Political scienceEngineeringPedagogyEngineering ethicsKnowledge managementMedical educationSociologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

This chapter describes the participatory role faculty members have played in the first year of a five-year initiative that uses e-learning to promote educational excellence in learning, teaching, and research at a polytechnic institute. It argues that faculty engagement will ultimately determine the success of this e-learning initiative and, as such, faculty need to be active members in a collaborative process informed by participatory design. As this chapter outlines, faculty have used constructivist learning principles to create the educational vision that drives the initiative and provides its focus. They have participated in decision-making processes on the management team and advisory committee, and have piloted tools, learning approaches, and technical and educational support structures to inform the institute-wide implementation of this vision. This chapter aims to provide a model to inform the strategic direction of other institutes implementing similar e-learning initiatives and, therefore, concludes with preliminary lessons learned from year one.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.273
Teacher spread0.254 · 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