Reading Instruction Time and Homogeneous Grouping in Kindergarten: An Application of Marginal Mean Weighting Through Stratification
Bibliographic record
Abstract
A kindergartner’s opportunities to develop reading and language arts skills are constrained by the amount of time allocated to reading instruction. In the meantime, the student’s engagement in learning tasks may increase if the instruction has been adapted to his or her prior ability through homogeneous grouping. This study investigates whether the grouping effects on kindergartners’ reading growth depend on the amount of reading instruction time and the intensity of grouping. To answer the study’s research questions requires causal inferences about concurrent multivalued instructional treatments. The authors develop a procedure of applying the method of marginal mean weighting through stratification to multilevel educational data. Results from the Early Childhood Longitudinal Study Kindergarten cohort data set lend support to the theoretical hypothesis that when teachers allocate a substantial amount of time to reading instruction, homogeneous grouping helps kindergartners to gain more in reading. The authors find no effect of homogeneous grouping when the total amount of reading time is limited. They also find that the benefit of increasing reading instruction time becomes evident only if kindergarten teachers adapt instruction through homogeneous grouping.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".