Improvement; Teacher Qualifications IDENTIFIERS *QUALM Teacher Education Program; *University of
Bibliographic record
Abstract
This paper describes an ongoing comprehensive model of program evaluation which has as its major goal the improvement of teacher education. This project, developed at the University of Lethbridge (Alberta, Canada), is presented in four parts: (1) the process--how and why the project developed or is developing as it Is; (2) the conceptual framework--the model within which the evaluation occurs; (3) implementation--specific eviluation projects within the conceptual framework; and (4) the administrative framework for facilitating utilisation of the evaluation results. The evaluation model provides a focus for research discussions, a framework for designing collaborative projects, a basis for collecting and sharing common data, and an opportunity for sharing research findings. Flexible enough to accommodate any teacher education program, it promotes longitudinal research; allows for individual, group and collaborative research; and can be fitted into an administrative schema for decision-making. Five evaluation projects utilising the framework are briefly described: (1) selection and development of teacher education candidates; (2) teachers ' perceptions of their educational programs; (3) the evaluation of the preservice competence of Alberta teachers; (4) becoming a teaC.,er; and (5) alternative practicum experiences for education students. (PM)
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 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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".