Teacher Portfolios in the Supervision Process: A Journey of Discovery
Bibliographic record
Abstract
Facilitating the personal and professional growth of teachers is a very effective way to positively impact students' self-esteem, skills development, and behavior. Certain professional development strategies, for example, developmental supervision, enable teachers to plan for their own growth. Developmental supervision is a nonevaluative approach to providing feedback that meets individual needs of teachers. The process utilizes research in the areas of professional development, teacher supervision, and adult learning. Portfolio projects could be a component of developmental supervision. However, there is minimal empirical research to support the use of portfolio projects in nonevaluative supervision. The purpose of this qualitative study was to describe the experiences of elementary teachers in a Canadian school district who completed a portfolio project as part of a developmental supervision process. Research questions guided data collection about the journeys each teacher experienced. Teachers revealed insights about the portfolio project process, their personal and professional growth, and how the meaning of the experience contributed to their development. The interview data and the portfolio projects document teacher growth through reflection on practice. An analysis of interview data and portfolio projects revealed three themes: continuous learning, enhancement of esteem, and a new sense of personal and professional meaning. This study documented that a nonevaluative supervision approach contributed to the personal and professional growth of teachers. Teachers revealed that the process of portfolio project development left lasting legacies, which included personal and professional affirmation, fulfillment, worthiness and competency, as well as the joy of learning. The study concludes with strategies for supporting portfolio project development for teachers, principals, and district administrators.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.023 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".