The characteristics of teacher expertise in elementary school inclusive classrooms
Bibliographic record
Abstract
This thesis is an examination of the characteristics of teacher expertise in inclusive elementary school classrooms. A series of six case studies are presented that provide a focused look at selected teachers and their inclusive classrooms. These six teachers stand out from other teachers, and this inquiry is aimed at finding out what it is that makes them unique. The teachers were selected because of their high scores on two separate measures, one of teacher practice and one of teacher beliefs. The participants were observed in their classrooms, then participated in a prompted recall interview, the Teacher Experience Interview. The interview focused on observed interactions with students with exceptional needs and/or who are at-risk for school failure, as well as specific beliefs that the teachers held and influences they nominated as significant in their backgrounds. Utilizing a grounded theory approach, results of the analysis indicate that these teachers employ a variety of instructional strategies, focusing on engaging all students in the learning process. Relationships between teachers and their students are also identified as a significant part of how the participant teachers see their role. The participants also share formative experiences with students with exceptional needs and/or who are at-risk for school failure prior to entering teaching. Characteristics of teacher expertise in inclusive classrooms are seen as a combination of teacher practices and beliefs and attitudes that exemplify an ethic of care and build resilience in students, particularly those with exceptional needs and/or who are at risk for school failure.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".