How Teachers Would Spend Their Time Teaching Language Arts
Bibliographic record
Abstract
As teacher quality becomes a central issue in discussions of children's literacy, both researchers and policy makers alike express increasing concern with how teachers structure and allocate their lesson time for literacy-related activities as well as with what they know about reading development, processes, and pedagogy. The authors examined the beliefs, literacy knowledge, and proposed instructional practices of 121 first-grade teachers. Through teacher self-reports concerning the amount of instructional time they would prefer to devote to a variety of language arts activities, the authors investigated the structure of teachers' implicit beliefs about reading instruction and explored relationships between those beliefs, expertise with general or special education students, years of experience, disciplinary knowledge, and self-reported distribution of an array of instructional practices. They found that teachers' implicit beliefs were not significantly associated with their status as a regular or special education teacher, the number of years they had been teaching, or their disciplinary knowledge. However, it was observed that subgroups of teachers who highly valued particular approaches to reading instruction allocated their time to instructional activities associated with other approaches in vastly different ways. It is notable that the practices of teachers who privileged reading literature over other activities were not in keeping with current research and policy recommendations. Implications and considerations for further research are discussed.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".