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Record W1548266165 · doi:10.1109/cie.2002.1186050

Learning technologies in higher education: Supporting transformative practice

2003· article· en· W1548266165 on OpenAlexaff
Cheryl Whitelaw, Myrna Sears, Dennis C. S. Law, K. Campbell

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransformative learningContext (archaeology)Faculty developmentGeneral partnershipProfessional developmentMedical educationComputer scienceInstructional designKnowledge managementProgram evaluationEngineering managementPedagogyPsychologyEngineeringMultimediaMedicinePolitical science

Abstract

fetched live from OpenAlex

This paper presents a summary of an evaluation study (2000 - 2002) on a faculty professional development initiative called the Partnership Program. The program paid for faculty release time to develop technology-enhanced instructional projects supported by an instructional development and evaluation team. The program was evaluated in the context of faculty instructional transformation and was designed to assess the program's impact on their core values, and models of teaching practices; Department's and Faculties' acceptance and support of learning technologies; the context in which they are evaluated for innovative instructional practice; and the influence of a Centre for faculty professional development on the university's learning culture. Outcomes for the study include an evaluation of and recommendations for programming for faculty professional development and support and models for peer review and evaluation of innovative instructional practices.

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)0.023
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.053
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.005
Scholarly communication0.0100.008
Open science0.0020.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.029
GPT teacher head0.374
Teacher spread0.344 · 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 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

Citations2
Published2003
Admission routes1
Has abstractyes

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