Pre-Service Portfolios: A Base for Professional Growth
Bibliographic record
Abstract
We studied first-year teachers who had developed professional portfolios in their pre- service program. Data came from two interviews with 11 graduates. Participants valued the portfolio process, most continuing to maintain their professional portfolios and to use aspects of the process with their students. Two frameworks, “The Portfolio Organizer” and “retell, relate, reflect,” supported portfolio use. Factors affecting continued portfolio implementation included first-year teaching pressures, the influence of other teachers, and external expectations. The first-year teachers experienced a positive change in attitude and acquired confidence as they refined their use of portfolios. L’etude porte sur l’utilisation des portfolios professionnels par des enseignants en formation des maitres. Les donnees proviennent de deux entrevues menees aupres de 11 etudiants diplomes. Les participants apprecient la methode du portfolio ; la plupart les tiennent a jour et en utilisent certains elements avec leurs eleves. Deux systemes, « The Portfolio Organizer » et « retell, relate, reflect », favorisent l’utilisation des porfolios. Parmi les facteurs ayant une incidence sur le recours aux portfolios, les auteures mentionnent les pressions inherentes a l’enseignement lors de la premiere annee, l’influence des autres enseignants et les attentes externes. Au cours de leur premiere annee, les enseignants deviennent plus surs d’eux-memes a mesure qu’ils raffinent leur utilisation des portfolios.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".