Connecting the Dots: From Teachers’ Perceived Ability to Teach Reading and Their Knowledge of Language and Literacy Concepts to Students’ Reading Growth
Bibliographic record
Abstract
The purpose of this study was two-fold: (a) to examine the joint contribution of teachers' knowledge of foundational language and literacy concepts and their perceived ability to teach reading to their students' reading growth, and (b) to examine whether the effects of these factors were mediated by teachers' perceived ability to differentiate instruction. A total of 32 language arts teachers and their 582 Grade 3 to 9 students (48% female) participated in the study. Teachers completed a survey on their knowledge of phonological awareness, phonics and morphology, and also rated their ability to teach different reading skills and to differentiate reading instruction. Children were assessed at the beginning and end of the school year on the Test of Word Reading Efficiency-2 and on the Test of Silent Reading Efficiency and Comprehension. Results of multilevel modeling indicated that teachers' knowledge had a direct effect on students' performance at the end of the school year, even after controlling for students' earlier reading ability. Teachers' perceived ability did not predict students' reading growth either directly or indirectly. Taken together, these findings suggest that we need to invest in increasing teachers' knowledge around foundational literacy skills.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".