Supporting Vocabulary Teaching and Learning in Prekindergarten: The Role of Educative Curriculum Materials
Bibliographic record
Abstract
The purpose of this study was to support teachers' child-directed language and student outcomes by enhancing the educative features of an intervention targeted to vocabulary, conceptual development and comprehension. Using a set of design heuristics (Davis & Krajcik, 2005 Davis, E. A., & Krajcik, J. S. (2005). Designing educative curriculum materials to promote teacher learning. Educational Researcher, 34, 3–14.[Crossref] , [Google Scholar]), our goal was to support teachers’ professional development within the curriculum materials. Ten pre-K classrooms with a total of 143 children were randomly selected into treatment and control groups. Observations of teacher talk, including characteristics of lexically-rich and cognitively demanding language were conducted before and during the intervention. Measures of child outcomes, pre- and post-intervention included both standardized and curriculum-based assessments. Results indicated significant improvements in the quality of teachers’ talk in the treatment compared to the control group, and significant gains for child outcomes. These results suggest that educative curriculum may be a promising approach to facilitate both teacher and student learning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".