Identification of students with neurobehavioral and academic performance complaints in basic education with use of Big Data resources

• Grant number: 18/01063-0
• Support Opportunities: Regular Research Grants
• Start date: August 01, 2018
• End date: October 31, 2020
• Field of knowledge: Humanities - Psychology - Fundamentals and Assessments in Psychology
• Principal Investigator: Maria Cristina Triguero Veloz Teixeira
• Grantee: Maria Cristina Triguero Veloz Teixeira
• Host Institution: Centro de Ciências Biológicas e da Saúde (CCBS). Universidade Presbiteriana Mackenzie (UPM). Instituto Presbiteriano Mackenzie. São Paulo , SP, Brazil
• Associated researchers: Alessandra Gotuzo Seabra ; Ana Claudia Rossi ; Decio Brunoni ; Leandro Augusto da Silva ; Luiz Renato Rodrigues Carreiro

Abstract

Teachers are fundamental agents for the identification of students with academic difficulties, emotional and/or with changes indicative of neurodevelopmental disorders. The overall objective will be to develop and deploy in an educational network, through the use of Big Data resources, four standardized models of recognition of indicators of neurodevelopmental disorders in students. The sample will be composed by: a) 65 professionals of basic education (60 teachers of 2nd and 4th years of 10 elementary schools); b) 5 professionals who attend students with school difficulties and neurobehavioral complaints); c) students from 2nd and 4th years of these 10 elementary schools with neurobehavioral and learning complaints (being 3 classrooms of 2nd year and 3 classrooms of 4th year), and a control group without neurobehavioral complaints composed by the same amount of students from the classroom matched by sex and age. The instruments will assess cognitive functioning, mental health, school performance skills, reading, and writing. It is expected to build an automated environment for the digital data collection using the open source software of Business Process Management System-BONITA. It is hoped that the data collected promote a Big Data environment for the educational network allowing to test how the transfer of evaluation technologies can contribute to a precise definition of the problems of the students, to soften the impoundments waiting for evaluations, as well as a more effective use of public resources. (AU)