Understandings, Beliefs, and Reported Decision Making of First-Year Teachers from Different Reading Teacher Preparation Programs
Bibliographic record
Abstract
In this study we followed 101 preservice teachers through their first year of teaching in order to explore differences in the understandings, beliefs, and decision making of beginning teachers from 3 types of reading teacher preparation programs. 73 of the elementary teachers graduated from programs at 1 of 8 colleges or universities identified by the International Reading Association's National Commission on Excellence in Elementary Teacher Preparation for Reading Instruction as excellent in undergraduate reading teacher preparation. 3 of these recognized programs were reading specialization programs (i.e., a major or minor in reading) housed at universities that also offered a general education program; the remaining 5 featured concentrated experiences and courses in reading that all undergraduates enjoyed. The reported experiences of these 40 reading specialization program teachers and 33 graduates of reading embedded programs were compared with the experiences of 28 graduates from general education programs from the 3 sites that offered a general program. Structured telephone interviews were conducted at 3 points in the beginning teachers' first year of teaching (September, January, June). Inductive data-driven analyses yielded 3 themes-instructional decision making, negotiations, and community-that distinguished the responses of graduates of the IRA recognized programs from those of graduates of general education programs. Graduates of recognized programs tended to speak in clear and thoughtful ways about their instruction, focus on assessing and meeting students' needs, and were more likely to report seeking ongoing support for and development of their own learning. These findings were robust for graduates across the 3 reading specialization sites as well as the 5 other recognized programs, suggesting that the overall quality and features of a program rather than its type or label can make a difference in preservice teacher learning.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".