The Relevance of Prior Learning in Teacher Education Admissions Processes
Bibliographic record
Abstract
In this paper, we argue that teacher education admissions processes would benefit from attending more to prospective teacher candidates’ cognitive frames. We begin with the introduction of a three-stage heuristic for describing teacher education. We then review the literature about constructivist notions of prior learning and teacher education program admissions processes. These processes, we argue, fail to adequately account for candidates’ preconceptions about teaching and learning, which affect their beliefs and understanding. Virtually none of the admissions processes we examined explicitly attempts to map the cognitive frames of applicants to uncover the structure of their ideas about teaching and learning. Teacher education institutions might best concentrate upon candidates’ cognitive frames within two core areas: subject area content knowledge and pedagogical knowledge. These two areas have the greatest potential to influence candidates’ future cognitive frameworks, understandings, and points of reference. Keywords: teacher education admissions processes; identifying cognitive frames; subject area content knowledge; pedagogical knowledge
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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 | 0.030 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".