Professional Portfolios during Preservice and First Year of Teaching: Creating a Base for Ongoing Professional Growth.
Bibliographic record
Abstract
This paper outlines a study involving the use of portfolios by first-year teachers after their experiences in a 10-month preservice teacher education program in which professional portfolios were implemented. Data were gathered from a questionnaire administered to teacher candidates at the end of their program, as well as from 2 sets of interviews with 11 subjects at the end of their first year of teaching. If and how new teachers valued the instruction and collaborative experience of creating professional portfolios in their preservice year was studied, and whether preservice teachers maintained their portfolios and continued using them as tools for growth were studied. Whether these first-year teachers implemented portfolios with their own students was also studied. Overall, the data reveal that the first-year teachers did value portfolios as an important form of collaborative assessment and reflection, both for themselves and for their students. Most of them did continue to use their professional portfolios in a modified way during their first year or had an intent to maintain their portfolios in the future, but their capacity to use portfolios with students during their first year of teaching varied. This study reports on the conditions that supported and hindered portfolio implementation during the first year of teaching, including cooperative relationships with peers. It also explores the implications for teacher/educators and for school and district leaders in supporting innovation by new teachers. (Contains 23 references.) (Author/8Lp) Reproductions supplied by EDRS are the best that can be made from the original document. Professional Portfolios during Preservice and First Year of Teaching: Creating A Base for Ongoing Professional Growth U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) TJJ -1:..tu,' ent has been reproduced as rceiv_ vom the person or organization originating it. 0 Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. Carol Rolheiser* Susan Schwartz Ontario Institute for Studies in Education of the University of Toronto (OISE/UT *Corresponding author: Carol Rolheiser, OISE/UT 252 Bloor Street West, Toronto, Ontario, Canada M5S 1V6 crolheiser@oise.utoronto.ca oo oo Tel: (416) 923-6641 Ext. 7501 Fax: (416) 975-1925
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".