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Record W2171165506 · doi:10.1109/cie.2002.1186247

Faculty reward and promotion in distributed learning environments-pedagogy in implementation

2003· article· en· W2171165506 on OpenAlexaff
Myrna Sears, K. Campbell, Cheryl Whitelaw

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsScholarshipExcellencePromotion (chess)Computer scienceDeliverableKnowledge managementTeaching and learning centerInstructional designValue (mathematics)Set (abstract data type)Educational technologyInstructional technologyMathematics educationTeaching methodMultimediaPsychologyEngineeringPolitical science

Abstract

fetched live from OpenAlex

This paper focuses on the scholarship of teaching and the need to implement change in the evaluation practices of post-secondary institutions. The scholarship of teaching is closely linked to the development of instructional technology innovations. The principles underlying teaching excellence are inherent in the effective use of instructional technology innovations. Increasing the value placed upon teaching and learning is a primary concern of a research project called "Peer Review in Instructional Technology Initiatives" (PRITI). PRITI (2000-2002) is a collaborative initiative to develop a peer review model of evaluation, which will be used to assess instructional technological innovations for faculty reward and promotion. One of the deliverables is a set of evaluative criteria and templates to assist institutional academic program review bodies in the evaluation of existing and proposed offerings in distance and distributed learning environments.

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)0.084
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score0.444

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0840.090
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.009
Scholarly communication0.0130.007
Open science0.0020.014
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.034
GPT teacher head0.402
Teacher spread0.369 · 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.

Study designObservational
DomainIncentives
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
Published2003
Admission routes1
Has abstractyes

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