We Taught Them about Literacy but What Did They Learn? The impact of a preservice teacher education program on the practices of beginning teachers
Bibliographic record
Abstract
This article reports a study of literacy instruction in our own elementary preservice program. It examines the views and practices of both the preservice faculty who teach literacy and a sample of graduates of the program during their first three years of teaching. The new teachers reported learning many things from their preservice program, including the importance of engaging learners, strategies for developing an inclusive class community, the names of high-quality works of children's literature, and a variety of general teaching strategies. However, there were gaps between what was taught and what the new teachers wanted to learn. The new teachers struggled with program planning, desired more direct instruction on developing a literacy program, and wanted closer links between theory and practice. The teacher educators tried to cover so much material that the new teachers were unable to develop a focused, coherent pedagogy. The authors describe how they are revising their courses in light of these findings, modifying their approach to preservice instruction, and giving priority to certain key aspects of teaching.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".