Profiles of Pre-service Teacher Education: An Investigation into the Nature of Selected Exemplary Programs in Jamaica and Michigan
Bibliographic record
Abstract
This qualitative multi-case study investigated thre exemplary pre-service teacher education programs in Jamaica and Michigan in order to provide an account of how they are structured in different contexts of tertiary institutions and, to identify how they ensure that their graduates are prepared to function effectively in today’s schools. Five categories of stakeholders across the three institutions were interviewed regarding their perception and expectations of pre-service teacher education in general as well as in the context of their program. The responses from these persons were described in narrative form, then analyzed and compared based on the similarities and differences that existed among them. The analysis led to the emergence of various themes across the three institutions, and these were used to draw conclusions relative to the structure of pre-service teacher education. The findings revealed eight distinguishing features of exemplary/effective pre-service teacher education programs whether university or college-based. (a) coherent program vision (b) cultural competence (c) collaborative partnership (d) contextualization (e) quality standards (f) well-planned and implemented field experiences (g) continuous assessment (h) experienced committed faculty and (i) a harmonious blend of theory and practice. To be effective, pre-service teacher education programs must prepare prospective teachers to adequately meet the challenges of teaching in today’s classrooms. To effect change, quality teachers are needed, and to produce quality teachers, quality preparation is a necessity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".