Children as Levels: Early Understandings of Reading Development Conceptualized by Preservice Teachers
Bibliographic record
Abstract
This qualitative study surfaced beliefs around reading instruction and reading development at the onset of an elementary literacy methods course. Prior understandings and knowledge around reading instruction and reading acquisition emerge through various experiences and have the potential to contradict notions presented by teacher educators. This inquiry explored prior beliefs held by five preservice teachers (PSTs) about the nature of reading and the teaching of reading, drawing on a thematic analysis (Braun & Clarke, 2006) of a pre-course survey. Results indicated that early understandings of reading development and pedagogy appear to rely on levelling systems for assessing and identifying students’ acquisition of reading skills, as well as organizing students into levels for instruction. Beliefs that reading development progresses through a levelled gradient are problematic for both teacher and student, shifting attention away from the complex nature of reading acquisition and the skills required to develop proficiency. While no generalizable statement can be made regarding PSTs’ most frequently held beliefs, this pilot study puts forward the idea that understanding PSTs’ prior beliefs is a critical part of teacher education. Intentional opportunities to unpack prior beliefs and understandings may offer insight for teacher educators to engage students in discourse and experience cognitive dissonance around inconsistencies, making space for learning and unlearning. Keywords: preservice teachers, teacher education, levels, reading, reading assessment
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| 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".