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Record W4385950128 · doi:10.1080/13636820.2023.2246330

Developing and supporting the dual professionalism of CAAT faculty members

2023· article· en· W4385950128 on OpenAlexaffabout
Mary Michelle Overholt

Bibliographic record

VenueJournal of Vocational Education and Training · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCurriculumProfessional developmentSociologyPedagogyFocus groupFaculty developmentPsychologyMedical educationMedicine

Abstract

fetched live from OpenAlex

This qualitative, exploratory study is focused on how teachers in Ontario Colleges of Applied Arts and Technology (CAATs) are prepared to teach. Using focus groups and semi-structured interviews, I sought the perspectives of front-line staff within academic development units and academic leaders to create a detailed depiction of how teacher preparation and development currently happen in CAATS and how it can be strengthened at the institutional and provincial levels. Using activity theory as my main theoretical framework and as the structure for my interview protocol, I worked with participants to collaboratively map the activity system of teacher preparation at individual institutions and across the CAAT system. Overall, CAATs provide basic teacher training – on planning and conducting lessons, designing course materials, and setting up courses on learning management systems – for faculty members but lack resources to support faculty members’ subject-matter expertise. CAATs can work together, under the direction of senior leadership, to develop better support both for educational developers and CAAT faculty members. CAAT academic development units can collaborate to create a provincial CAAT teacher training curriculum/credential that can be implemented at the institutional level to ensure consistency as well as the necessary level of institutional focus for faculty 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 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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
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 head0.312
GPT teacher head0.535
Teacher spread0.223 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2023
Admission routes2
Has abstractyes

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