Um modelo de transferência de tecnologias para a educação básica para avaliação de alunos com sinais do transtorno do espectro autista e deficiência intelectual - Suriano, Raquel (2018)
Research within Brazilian context report difficulties that educators and management staff face to make sure that students with educational and developmental delay complaints are properly assessed and receive required educational intervention. The general objective of this project was to develop two standardized frameworks of assessment of basic education students with indicators of Autistic Spectrum Disorder (ASD) and Intellectual Disability (ID) through the use of a Big Data environment. The specific objectives were: a) to provide tools for teachers and management staff to use standardized assessment procedures for triage of students suspected of having a disorder; b) to identify in the students indicators compatible with TEA and DI according to the instruments of the models; c) to verify indicators of sensitivity to standardized triage procedures and cognitive and behavioral assessment considering the number of false positives. The sample was composed of: a) 16 psycho-pedagogues that work on the 16 sectors in which the educational network is divided to cover schools of each sector; b) 10 professionals of the Interdisciplinary Department for Inclusion (IDI) that support the process of educational inclusion; c) 2 teachers of the 2nd and 4th grades of elementary school of each sector (one from the 2nd grade, one from the 4th grade), 32 teachers in total; d) their respective students that presented signs of ASD and ID during the period of this project; e) parents of these students. Each classroom had an average of 25 to 30 students. The tools for data collection were: a) two checklists to identify signs of ID and ASD; b) Wechsler Abbreviated Scale of Intelligence (WASI); c) Brief Problems Monitor, parents and teachers from; d) Autism Behavior Inventory; e) guidebook with orientations to deal with students with signs of ID and/or ASD in the classroom. To assess the efficacy of the framework, false positives were calculated according to the diagnostic confirmation or not of the suspected ID or ASD. Descriptive analyses were made to characterize the sample of students, and predictive analyses through Data Mining techniques were carried out to verify the indicators that are most sensitive to the definition of complaints compatible with the disorders. The main results show that for ID indicators, considering the cognitive control and intellectual functioning skills assessed through WASI, 72.22% of the sample presented indicators of low intellectual functioning. This corresponds to 27.78% of false positives that tested normal and below average according to WASI. On the other hand, since it was not possible to reach a diagnosis for any of the students suspected of having ASD, the indicators of false positives corresponded to 100% of the sample. The signs that seemed most distinctive for the suspicion of ASD were linked to deficits in communication and social interaction. For ID cases, such signs were social commitments in daily life. After the data collection, the research enabled the creation of a Big Data environment for data analysis and decision-making for the identification of students suspected of having neurodevelopmental disorders compatible with ASD and ID. Teachers and management staff, actors of the process, were able to employ assessment procedures for students suspected of having disorders, thus displaying a more effective use of public resources.