Developing a Practicum Model Through a Democratic Process
Bibliographic record
Abstract
This chapter developed from two points in the American Association of Colleges for Teacher Education [AACTE] (2018) report. The first point is that “local context matters when considering how to best operationalize clinical practice” (p. 4) and the second point is that there needs to be a “shared responsibility for teacher learning and development ... by university [and] school” (p. 35). The theoretical foundation of our work builds upon Zeichner et al. (2015) who considered that if we are preparing teachers for a democratic society, we need to use a democratic process to get us there. We need to build our teacher education programs through a process that is itself democratic. We need the voices of teacher candidates, teacher mentors, university faculty and community members to be considered as having equitable value. This chapter shares the perspectives of teacher candidates, teacher mentors and university faculty who are involved in designing and piloting a co-teaching practicum model for a particular local context, a teacher education program for secondary teachers of science and mathematics. We share our process, our achievements, our difficulties, and our hopes for the future. The practicum experiences described overlapped the beginning of the COVID-19 pandemic and so we also share some experiences of co-teaching when practicum went online.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".