Developing and supporting the dual professionalism of CAAT faculty members
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.028 | 0.027 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".