Teachers’ Concern and Professional Development Needs in Adopting Inclusive Education in Saudi Arabia, Based on Their Gender for Vision 2030
Bibliographic record
Abstract
This study looks into the concerns and the required professional development for adopting an inclusive education system, as expressed by elementary school teachers, based on their gender in Saudi Arabia. Participants in this research were special and general education teachers randomly selected from elementary schools in Riyadh, Saudi Arabia, which have special education programs. The theoretical framework of the study was the Concern Based Adoption Model (CBAM). Non-experimental cross-sectional survey was used to collect data. Data were obtained from 332 teachers, i.e., the response rate was 83%. The Stages of Concerns Questionnaire (SoCQ) provided by CBAM indicated that respondent stages of concern 0–2 (Unconcerned, Informational, and Personal) ranked the highest, while stages 4–6 (Consequence, Collaboration, and Refocusing) ranked the lowest. This profile was identified as a “non-user profile”, meaning respondents wanted more information about inclusive education. Teachers, in general, showed interest for professional development on inclusive education, including immediate training and seminars/workshops. The only significant difference in interest for professional development was by gender. The t-test indicated that female teachers have more interest for professional development compared to male teachers.
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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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".