Índices educacionais como preditores da proficiência em ciências: um estudo multinível

• Autor(a) principal: Gusmão, Fábio Alexandre Ferreira
• Data de Publicação: 2011
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da PUC_SP
• Texto Completo: https://tede2.pucsp.br/handle/handle/16018
• topic: Avaliação educacional; CHAID; Desempenho em ciências; Ensino fundamental; Educação científica; Educational evaluation; Performance in science; Elementary school; Scientific education; CNPQ::CIENCIAS HUMANAS::EDUCACAO::FUNDAMENTOS DA EDUCACAO::PSICOLOGIA EDUCACIONAL

Brazil has reached the 21st century providing universal access to elementary education to almost the entire population of the 7 to 14 age group. The 21st century was unquestionably a period of major economic advances, increase in the population s political participation, and consolidating democracy. However, many educational indicators are still dismal, such as the illiteracy rate for the population over 25 years, the student turnover rate, and access rates of students who finish elementary and middle school. The goals of this study are: 1) to establish the relationship between the characteristics of students and schools and the performance results of 8th grade students in Science, and 2) to show how social inequalities are mirrored in the educational system as found by Bourdieu and Passeron (2009). This study used data from the 1999 SAEB (the Brazilian Elementary Education Assessment System) on students, teachers, principal, and school facilities. The data were used to understand the relation and/or correlation between independent variables, indicators of economic and social inequality (GDP, HDI, Gini coefficient) with the dependent variable. This analysis enabled us to capture the impact of different independent variables on student performance in the SAEB database. Parametric and nonparametric tests were subsequently applied to verify whether the differences found were significant. CHAID method was employed to find the Best predictors of performance in Science. In regard to the level of the students, the results show that all the differences found among independent variables and the average proficiency rate in Science were statistically significant. The CHAID analysis highlighted that the following independent variables school system, income, cultural level, flunking rate, and doing homework are statistically related to the average Science proficiency rate. Concerning the level of the schools, results show that school organization and management, school resources, teacher education and pay, and school environment are statistically related to the average Science proficiency rate. In terms of the level of the states, results show that the correlation among social and economic inequality indicators with performance in Science is influenced by the economic, social, and educational situation of Brazilian states and region. CHAID method proved to be effective in possible crossings of data, thus rejecting non-significant variation in data crossing, and focusing its subdivisions on variations resulting from data crossings potentially significant to this study and on identifying predictors of Science learning based on 1999 SAEB data