The Supporting Effective Teaching Project: 1. Factors Influencing Student Success in Inclusive Elementary Classrooms
Bibliographic record
Abstract
The Supporting Effective Teaching project commenced in the early 1990s with studies of how classroom teachers work with students with special educational needs included in their elementary classrooms. Over the ensuing 20 years, the project team prepared and tested a model of the factors that influence student outcomes in inclusive classrooms, with emphasis on the beliefs and practices of regular elementary classroom teachers and on their sense of responsibility for meeting the diverse learning needs of their students. This article provides an overview of the SET project to show how the model evolved and to bring together the findings that were published previously. It takes a different tack from previous papers in that it begins at the most surprising outcome, the importance of teaching practices. Arguably the most significant empirical finding from the project is that teachers who believe it is their responsibility to include students with special education needs are more effective practitioners for all their students. The article then traces the factors that contribute to this finding: quality of instruction, teacher beliefs about ability and disability, teacher beliefs about learning and instruction, and school context. The purpose is to present a comprehensive review of the project findings in the context of recently published research on inclusion.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| 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".