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Record W2040528684 · doi:10.4018/jmbl.2009092204

E-Professional Development and Rural Teachers

2009· article· en· W2040528684 on OpenAlexaffabout
Andrew Kitchenham

Bibliographic record

VenueInternational Journal of Mobile and Blended Learning · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsProfessional developmentPsychologyBlended learningProfessional learning communityFaculty developmentMedical educationEducational technologyPedagogyQualitative researchMathematics educationSociologyMedicine

Abstract

fetched live from OpenAlex

This paper describes the outcomes of a study on teacher supply and demand issues in Northern Canada. Using questionnaires and interviews, participants were asked to comment on professional development models currently used and models to be considered. In particular, comments on the use of blended learning as a viable method of e-professional development model were favorable. In subsequent research to follow up those comments, the researcher provided professional development model exemplars and asked the participants to discuss the advantages and disadvantages for rural teaching professionals. The researcher argues that the chosen blended learning model is superior to others as it is based on adult-learning principles. The results of this study are promising as the majority of participants chose blended learning as their primary choice for professional development.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.946

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.017
GPT teacher head0.362
Teacher spread0.345 · 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 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

Citations1
Published2009
Admission routes2
Has abstractyes

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