Developing a Native Model of Inclusive Education for Students with Special Needs in the Exceptional Education Organization

Purpose: The aim of this study was to provide a native model of inclusive education for students with special needs in the Exceptional Education Organization.  Methodology: The research method was mixed (qualitative and quantitative). The research method was qualitative, descriptive and thematic analysis with heuristic inductive approach. In this study, up to data saturation, 10 experts and professors of educational management who had done research in the field of inclusive education for exceptional children were selected and semi-structured interviews were conducted with them. The selection was made using MAXQDA 2018 software. In the quantitative part of the statistical population of the present study, all teachers of the General Directorate of Education, who are working in the field of exceptional children and inclusive education in Tehran. According to the statistics of 24 support schools for exceptional children in Tehran, the number of teachers for these children was 500 and according to Cochran's formula, 217 people were distributed among them as a sample questionnaire. The sampling method was cluster according to the regions of Tehran. PLS software was used for analysis and modeling. Findings: The findings of the qualitative section showed that a total of 7 dimensions, 16 components and 40 indicators for inclusive education were identified in the Exceptional Education Organization of Iran. Dimensions obtained included the development of cultural capital; the development of human resources; the relationship between family and school; the preparation of educational infrastructure; the response to educational needs; the continuous monitoring and follow-up; the continuous interaction with education.  Concologen: In a small part, the GoF criterion was used to fit the overall model; this criterion controls both the measurement and structural parts of the model, so the value obtained shows the number 0.71, which indicates a strong fit of the model.