<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1517-9702</journal-id>
<journal-title><![CDATA[Educação e Pesquisa]]></journal-title>
<abbrev-journal-title><![CDATA[Educ. Pesqui.]]></abbrev-journal-title>
<issn>1517-9702</issn>
<publisher>
<publisher-name><![CDATA[Faculdade de Educação da Universidade de São Paulo]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1517-97022014000100006</article-id>
<title-group>
<article-title xml:lang="pt"><![CDATA[Modelagem do crescimento da aprendizagem nos anos iniciais com dados longitudinais da pesquisa GERES]]></article-title>
<article-title xml:lang="en"><![CDATA[Modeling of the growth of learning in the early years with longitudinal data of GERES research]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Brooke]]></surname>
<given-names><![CDATA[Nigel]]></given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Fernandes]]></surname>
<given-names><![CDATA[Neimar da Silva]]></given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Miranda]]></surname>
<given-names><![CDATA[Isabela Pagani Heringer de]]></given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Soares]]></surname>
<given-names><![CDATA[Tufi Machado]]></given-names>
</name>
</contrib>
</contrib-group>
<aff id="A06">
<institution><![CDATA[,IUniversidade Federal de Minas Gerais  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<aff id="A06">
<institution><![CDATA[,IIUniversidade Federal de Juiz de Fora  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>01</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>01</month>
<year>2014</year>
</pub-date>
<volume>40</volume>
<numero>01</numero>
<fpage>77</fpage>
<lpage>94</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://educa.fcc.org.br/scielo.php?script=sci_arttext&amp;pid=S1517-97022014000100006&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://educa.fcc.org.br/scielo.php?script=sci_abstract&amp;pid=S1517-97022014000100006&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://educa.fcc.org.br/scielo.php?script=sci_pdf&amp;pid=S1517-97022014000100006&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo Este artigo compara duas abordagens de valor agregado para dados oriundos do survey educacional de recorte longitudinal, chamado GERES - Estudo Longitudinal da Geração Escolar 2005, que acompanhou uma coorte de alunos de mais de 300 escolas públicas e privadas ao longo dos primeiros quatro anos do Ensino Fundamental. Ambas as abordagens utilizam modelos lineares hierárquicos, permitindo o agrupamento natural dos dados educacionais provenientes dos três níveis: aluno, turma e escola. Na primeira abordagem de valor agregado, constroem-se modelos cuja variável dependente é a proficiência do aluno em cada ano avaliado. Com um modelo distinto para cada ano é possível detectar fatores do aluno, da turma e da escola associados ao desempenho dos alunos. A segunda abordagem cria modelos para mostrar o efeito das covariáveis de aluno, turma e escola nas curvas de evolução da proficiência ao longo do período do estudo. Quando comparados os dois tipos de modelos de valor agregado, o primeiro foi o mais eficiente em diagnosticar os efeitos do ambiente e da prática pedagógica do professor, mas somente em determinados anos. Já o segundo tipo de modelo foi capaz de identificar curvas de evolução de proficiência de formatos distintos de acordo com determinadas características das escolas e dos alunos, mas foi menos sensível na identificação de variáveis associadas ao processo de formação de grupos e à prática pedagógica do professor. Os dois tipos de modelos de valor agregado oferecem indicações de processos de aprendizagem diferenciados para as disciplinas Língua Portuguesa e Matemática que mereceriam estudos adicionais.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This article compares two value-added approaches to data from the longitudinal education survey called Estudo Longitudinal da Geração Escolar 2005 (GERES - Longitudinal Study of 2005 School Generation), which followed a cohort of students from more than 300 public and private schools over the first four years of primary education. Both approaches use hierarchical linear models, allowing the natural grouping of educational data from three levels: student, class and school. The first value-added approach builds models whose dependent variable is the student proficiency in each year evaluated. With a separate model for each year, it is possible to detect factors of the student, class and school associated with student performance. The second approach creates models to show the effect of the covariates of student, class and school on progress curves of proficiency throughout the study period. When comparing the two types of value-added models, the first one was the most efficient in diagnosing the effects of the environment and the teacher’s pedagogical practice, but only in certain grades. The second type of model was able to identify progress curves of proficiency of different formats according to certain characteristics of schools and students, but was less sensitive to identify variables associated with the group formation process and the teacher’s pedagogical practice. The two types of value-added models offer indications of differentiated learning processes for the disciplines of Portuguese and Mathematics, which deserve further study.]]></p></abstract>
<kwd-group>
<kwd lng="pt"><![CDATA[Estudo Longitudinal]]></kwd>
<kwd lng="pt"><![CDATA[Avaliação Educacional]]></kwd>
<kwd lng="pt"><![CDATA[Valor Agregado]]></kwd>
<kwd lng="pt"><![CDATA[GERES]]></kwd>
<kwd lng="pt"><![CDATA[Modelos Lineares Hierárquicos]]></kwd>
<kwd lng="en"><![CDATA[Longitudinal Study]]></kwd>
<kwd lng="en"><![CDATA[Educational Assessment]]></kwd>
<kwd lng="en"><![CDATA[Value Added]]></kwd>
<kwd lng="en"><![CDATA[GERES]]></kwd>
<kwd lng="en"><![CDATA[Hierarchical Linear Models]]></kwd>
</kwd-group>
</article-meta>
</front><body><![CDATA[  <font face="Arial, Helvetica, sans-serif"> <h2>Modelagem  do crescimento da aprendizagem nos anos iniciais com dados longitudinais da  pesquisa GERES.</h2> <h3>Modeling of the growth of learning in the early years with  longitudinal data of GERES research.</h3>     <p>&nbsp;</p> <h4>Nigel  Brooke, Neimar da Silva Fernandes, Isabela Pagani Heringer de Miranda, Tufi  Machado Soares</h4> </font>      ]]></body><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="journal">
<source><![CDATA[Revista Ensaio]]></source>
<year></year>
<numero>61</numero>
<issue>61</issue>
<publisher-loc><![CDATA[Rio de Janeiro ]]></publisher-loc>
</nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="journal">
<source><![CDATA[Revista Brasileira de Educação, ANPEd]]></source>
<year></year>
<numero>17</numero>
<issue>17</issue>
<page-range>127-133</page-range></nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="journal">
<source><![CDATA[Journal of Educational and Behavioral Statistics]]></source>
<year></year>
<volume>20</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>201-204</page-range></nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="journal">
<source><![CDATA[Revista Educational Evaluation and Policy Analysis]]></source>
<year></year>
<volume>20</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>253-268</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
