Impacting Pre-service Teachers’ Attitudes toward Inclusion
Bibliographic record
Abstract
Despite federal mandates to educate students with disabilities in the least restrictive environment, teachers continue to have mixed feelings about their own preparedness to educate students with disabilities in the general education setting. However, research has documented that teachers with more positive attitudes toward inclusion are more likely to adjust their instruction and curriculum to meet individual needs of students and have a more positive approach to inclusion. With inclusion becoming the norm in today’s schools, teacher educators are now faced with the challenge of making significant changes to educational programs in preparing pre-service teachers to be ready to meet the needs of all students. These programmatic changes mirror the continuous melding transformations in progress now in traditional general education and special education programs. However, there is limited information about how these new teacher educator programs influence pre-service teachers’ confidence or attitudes toward inclusive education as future teachers. To investigate this influence of teacher preparation programs on pre-service teachers’ attitudes toward inclusion, a survey method was used to collect data from pre-service teachers in one teacher-preparation program. The responses from pre-service teachers were analyzed indicating that pre-service teachers from this particular teacher preparation program in which general education curricula were infused with special education curricula in special education survey courses had improved positive attitudes and confidence toward inclusion. The implications of this study for practice and future research are discussed.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".