Teacher Candidates as Read-Aloud Tutors: Trajectories of Growth Through a Field Experience Placement
Bibliographic record
Abstract
The purpose of this study was to identify and describe the relationship between active participation in a weekly tutoring program and the development of teacher candidates’ knowledge about teaching reading and comprehension. Three questions guided this study: How does a read-aloud tutoring program contribute to teacher candidates’ understanding of literacy development? How do we help teacher candidates to move beyond low-level questions to meaning-focused instruction? How do we help teacher candidates to individualize instruction and teach in responsive ways? We are particularly interested in understanding how the teacher candidates Better understand the value and benefits of reading aloud with children Develop confidence in their ability to effectively read aloud with children Learn to ask rich questions that promote deep thinking These questions were addressed through a case study methodology. Analysis identified the following themes related to teacher candidates’ learning: (a) theory/practice connections, (b) reading as engaged experience, and (c) trajectories of growth. Findings from this study will support the development of course work that aligns the theory and practice of literacy instruction, enhances pre-service teachers’ abilities to be strong literacy teachers, and contributes to the scholarship of pre-service teacher education and children’s literacy development.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".