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Record W1547269140 · doi:10.21810/sfuer.v7i.378

Preparing for the Changing Faces of Education: Effective Professional Development Models

2014· article· en· W1547269140 on OpenAlex
Yvonne DeWith

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSFU Educational Review · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsConstructiveProcess (computing)Professional developmentReflection (computer programming)Computer scienceFace (sociological concept)Mathematics educationMultimediaPedagogyPsychologySociology

Abstract

fetched live from OpenAlex

The face of education is constantly changing. The traditional classroom with rows of desks facing a chalkboard is being demolished and replaced with movable tables, Smart Boards and laptops, project-based learning, differentiated lessons and more authentic assessment. To be effective and to accommodate a rapidly changing educational system, teachers must be trained and equipped. Regardless of the innovation or change, the process of traveling from a novice to an expert teacher is an ongoing journey which requires adequate training. The question remains as to what is the most effective method of moving teachers along the trajectory from novice to expert. Research has shown how ineffective single “one-off” workshops are at resulting in real change in a teacher’s practice. Effective professional development to develop expertise in any area, however, should allow for sufficient time for practice, collaboration, self-reflection, and constructive feedback.

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)0.002
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: none
Teacher disagreement score0.932
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.036
GPT teacher head0.413
Teacher spread0.377 · 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