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MODALIDADE ARTIGOS CIENTÍFICOS
Dissertação de Mestrado
UNIVERSIDADE FEDERAL DE GOIÁS
FACULDADE DE ODONTOLOGIA
PROGRAMA DE PÓS-GRADUAÇÃO EM ODONTOLOGIA
ERICA TATIANE DA SILVA
ANÁLISE DE SEGMENTAÇÃO DE ESTUDANTES DE GRADUAÇÃO EM
ODONTOLOGIA: INFLUÊNCIA DO DESEMPENHO ACADÊMICO E
PERFIL SOCIOECONÔMICO
Orientador: Prof. Dr. Cláudio Rodrigues Leles
Co-Orientadora: Profª. Drª. Maria Goretti Queiroz
UFG
2009
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ii
ERICA TATIANE DA SILVA
ANÁLISE DE SEGMENTAÇÃO DE ESTUDANTES DE GRADUAÇÃO EM
ODONTOLOGIA: INFLUÊNCIA DO DESEMPENHO ACADÊMICO E
PERFIL SOCIOECONÔMICO
Dissertação de Mestrado apresentada ao Programa
de Pós-Graduação em Odontologia da Universidade
Federal de Goiás para obtenção do Título de Mestre
em Odontologia
Orientador: Prof. Dr. Cláudio Rodrigues Leles
Co-Orientadora: Profª. Drª. Maria Goretti Queiroz
UFG
2009
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Programa de Pós-Graduação em Odontologia
da Universidade Federal de Goiás
BANCA EXAMINADORA DA DISSERTAÇÃO DE MESTRADO
Aluna: Erica Tatiane da Silva
Orientador: Prof. Dr. Cláudio Rodrigues Leles
Co-Orientadora: Profª. Drª. Maria Goretti Queiroz
Membros:
1. Prof. Dr. Cláudio Rodrigues Leles
2. Prof. Dr. Luiz Roberto Augusto Noro
3. Profª. Drª. Maria Goretti Queiroz
4. Profª. Drª. Vânia Cristina Marcelo
Suplente:
1. Profª. Drª. Rejane Faria Ribeiro-Rotta
Data: 02.03.09
4
Dedico este trabalho...
A Deus,
Que aponta caminhos e age através de cada pessoa que passa pela minha vida.
Aos meus pais, Eduardo e Maria Aparecida,
Exemplos de perseverança e dedicação à família e ao trabalho, pelo amor,
carinho, incentivo e apoio constante, fundamentais para meu crescimento pessoal
e profissional. Obrigado por tudo que representam e me deram nesta vida.
Obrigada por viver meus sonhos e compartilhar comigo as realizações.
5
AGRADECIMENTOS
Ao meu orientador, Professor Cláudio R. Leles,
Exemplo de dedicação e competência profissional. Meus sinceros
agradecimentos pelas oportunidades, aprendizados e desafios vivenciados ao
longo destes anos, que contribuíram intensamente para o meu crescimento
acadêmico, profissional e pessoal. Expresso aqui minha admiração, gratidão e
respeito.
À minha co-orientadora, Professora Maria Goretti Queiroz,
Que, com seu olhar de “educadora”, ampliou meus questionamentos, buscas e
aprendizados na estruturação desta dissertação. Agradeço a receptividade,
ensinamentos e valiosa colaboração na realização do trabalho.
À Universidade Federal de Goiás,
Especialmente às Professoras Valquíria e Luciana (diretoras do Departamento de
Assuntos Acadêmico e Centro de Seleção, respectivamente), cuja receptividade e
disponibilidade foram fundamentais para a coleta dos dados desta pesquisa.
Ao meu irmão, avós, tios e primos,
Pelo suporte e união familiar, compreensão nas horas de ausência, incentivo e
momentos de alegria e amizade.
Ao meu noivo, Rommel,
Pelo intenso amor e companheirismo ao longo de nosso relacionamento. Pela
compreensão e força nos momentos de ausência e apreensões. “Você surgiu e
6
juntos conseguimos ir mais longe/ Você dividiu comigo a sua história/ E me
ajudou a construir a minha”.
Aos professores da Faculdade de Odontologia da Universidade Federal de Goiás,
Em especial os que integram o Programa de Pós-graduação, pelos valiosos
ensinamentos, profissionalismo e respeito com que conduzem suas atividades.
À doutoranda Maria de Fátima,
Pela contribuição no desenvolvimento do trabalho.
Ao mestrando Antônio Hélio,
Pelo companheirismo na convivência diária; pelos momentos de reflexão,
distração e contribuições para meu engrandecimento profissional e pessoal.
Aos colegas do Mestrado,
Pelo convívio durante esse período de aprendizado e troca de experiências.
Aos funcionários do Centro Goiano de Doenças da Boca e do Programa de Pós-
graduação,
Pela receptividade, atenção e dedicação com que desempenham o seu trabalho.
7
RESUMO
O objetivo deste trabalho foi analisar fatores influenciadores do desempenho
acadêmico de estudantes de graduação da Faculdade de Odontologia da
Universidade Federal de Goiás (FO/UFG). Foi delineado um estudo transversal
retrospectivo, considerando todos os estudantes da FO/UFG que concluíram a
graduação no período de 1988 a 2007 (n=1182). Os dados acadêmicos foram
coletados a partir do questionário socioeconômico, nota do vestibular e histórico
acadêmico, obtidos junto ao Departamento de Assuntos Acadêmicos e Centro de
Seleção da Universidade Federal de Goiás. Foi realizada análise de cluster,
seguida de análise bivariada (Teste do Qui-quadrado, Teste t pareado e ANOVA)
e regresão linear múltipla. Análise de cluster (K-means) segmentou os estudantes
em grupos de desempenho acadêmico alto (n=456; 38,6%), moderado (n=531;
44,9%) e baixo (n=195; 16,5%). O segmento de desempenho acadêmico baixo
apresentou menor número de estudantes nos clusters de desempenho global e
conforme grupos de disciplinas, variando entre 11,8% (disciplinas clínicas) e
19,2% (disciplinas do ciclo básico). Houve diferença no desempenho acadêmico
entre as disciplinas dos ciclos básico e profissionalizante (p<0,001) e entre as
disciplinas clínicas e não clínicas (p<0,001), sendo observadas maiores notas em
disciplinas profissionalizantes e clínicas. Melhor desempenho acadêmico foi
relacionado ao menor tempo entre ensino médio e ingresso na FO/UFG, gênero
feminino, melhor classificação no vestibular, maior freqüência e carga horária
cumprida pelo estudante (R
2
=0,491). A segmentação de grupos de estudantes de
acordo com o perfil socioeconômico (TwoStep Cluster) também identificou três
clusters (compreendendo 26,6, 39,2 e 34,2% de uma amostra de 158 estudantes),
os quais mostraram desempenho acadêmico satisfatório, com diferenças no
8
desempenho global (p<0,05) e por grupos de disciplinas (0,01<p<0,05), exceto
em disciplinas clínicas. Os padrões de desempenho acadêmico conforme perfil
socioeconômico evidenciou a necessidade de desenvolvimento de estratégias
educacionais diferenciadas para cada cluster, bem como a importância do auxílio
financeiro da Universidade para maximizar o sucesso de experiências
educacionais de estudantes com menor nível socioeconômico. Conclui-se que a
análise de fatores influenciadores do desempenho acadêmico configura-se como
uma eficiente ferramenta auxiliar no planejamento de instituições de ensino,
organizações profissionais e políticas públicas.
Descritores: Condições socioeconômicas; Currículo; Desempenho de
Estudantes; Estudantes de Odontologia.
9
ABSTRACT
The aim of this study was the recognition of variables that influence academic
performance in a retrospective sample including all undergraduate students who
entered in a Brazilian dental school, in a 20-years period between 1984 and 2003
(n=1182). Data related to academic performance in dental school admission test
and graduation, and to socioeconomic questionnaire completed by students at the
time of enrollment in university entrance examination were retrieved by the
University Registrar‟s Office. Cluster analysis, bivariate (Chi-square test, Paired-
samples t test and One-way ANOVA) and multiple regression analysis were used
for data analysis. In the first study, cluster analysis (K-means cluster) categorized
students into groups of higher (n=456; 38.6%), moderate (n=531; 44.9%), or lower
(n=195; 16.5%) academic performance. Lower performance groups had smallest
number of students in overall performance and discipline groups clusters, ranging
from 11.8% (clinical disciplines) to 19.2% (basic disciplines). Students‟
performance was higher in dental and clinical disciplines, compared with basic and
non-clinical disciplines (p<0.001). Higher academic performance was predicted by
lower time elapsed between completion of high school and dental school
admission, female gender, better rank in admission test, frequency in course and
student workload hours (R
2
=0,491). In the second study, cluster analysis
(TwoStep) categorized students (n=158) into three groups according
socioeconomic variables, enclosing 26.6, 39.2 and 34.2% of the sample. Clusters
showed satisfactory academic performance, with differences in overall
performance (p<0.05) and by discipline groups (0.01<p<0.05), except for clinical
disciplines. Patterns of academic performance according socio-economic
characteristics revealed need for development of specific educational strategies
10
for each cluster and importance of university‟ financial support to maximize
successful educational experience of socio-economically disadvantaged students.
It was concluded that analysis of predictive variables that influence academic
performance plays a strategic role in planning of educational institutes,
professional organizations and public policies.
Key Words: Curriculum, Cluster Analysis, Dental education, Dental students,
Socioeconomic status
11
LISTA DE ABREVIATURAS
CS
Centro de Seleção
DAA
Departamento de Assuntos Acadêmicos
FO/UFG
Faculdade de Odontologia da Universidade Federal de Goiás
IES
Instituições de Ensino Superior
IFES
Instituições Federais de Ensino Superior
REUNI
Programa de Apoio a Planos de Reestruturação e Expansão das
Universidades Federais
Sinaes
Sistema Nacional de Avaliação da Educação Superior
UFG
Universidade Federal de Goiás
12
SUMÁRIO
1
INTRODUÇÃO......................................................................................
13
2
OBJETIVOS .........................................................................................
17
3
PROCEDIMENTOS METODOLÓGICOS ............................................
18
4
PUBLICAÇÕES ....................................................................................
22
5
PARA NÃO CONCLUIR...MAS PARA DESAFIAR... ...........................
61
REFERÊNCIAS ....................................................................................
64
APÊNDICES ........................................................................................
73
ANEXO .................................................................................................
76
13
1 INTRODUÇÃO
A formação de Recursos Humanos para a Saúde foi apontada pela
Organização Pan-Americana da Saúde como uma prioridade para esta década
1
.
Em contraposição ao modelo tradicional de formação profissional norteado pelos
pressupostos dos relatórios Flexner e Gies
2,3
*, altamente valorizado no século XX,
emerge na atualidade um novo paradigma educacional, visando à formação de
um profissional generalista, crítico, tecnicamente competente, capaz de trabalhar
em equipes multiprofissionais e de dar respostas às necessidades sociais no
âmbito da profissão
3,4-9
.
No cenário do ensino odontológico brasileiro, ao estabelecer o perfil do
egresso e definir as competências e habilidades necessárias para a formação
profissional, as Diretrizes Curriculares Nacionais do Curso de Graduação em
Odontologia
10
representaram um grande avanço no sentido de alavancar
mudanças visando o equilíbrio entre as dimensões técnico-científicas e ético-
humanísticas na organização curricular
11
.
Entretanto, o processo de planejamento, implementação e manutenção
de medidas estratégicas voltadas à ressignificação da formação do cirurgião-
dentista é complexo e dinâmico. Consequentemente, há um crescente interesse
em pesquisas relacionadas à identificação de fatores que otimizem a experiência
educacional de cada estudante e o sucesso do programa de graduação, de modo
a subsidiar as Instituições de Ensino Superior (IES) nesta reorientação
12-15
.
* Os Relatórios Flexner e Gies foram publicados pela Fundação Carnegie, nos Estados Unidos, em
1910 e 1926, com o objetivo de normatizar o ensino médico e odontológico, respectivamente. No
Relatório Gies foi proposta uma reorganização da prática odontológica, na qual se buscou uma
maior autonomia da Odontologia frente à Medicina. Os princípios norteadores, entretanto, eram os
mesmos do Relatório Flexner, ou seja, tinha como base o paradigma cartesiano, o qual era
aplicado aos currículos e às disciplinas do meio biomédico, com ênfase na separação do todo em
partes, no domínio cognitivo e na noção instrumental dos saberes.
14
a subsidiar as Instituições de Ensino Superior (IES) nessa reorientação
12-15
.
Conforme evidenciado por trabalhos internacionais recentes que
analisaram o impacto de variáveis multidimensionais – tais como a estruturação
curricular
16-35
, experiências educacionais prévias à universidade
12,14
e aspectos
cognitivos
36-45
, socioeconômicos
12-15,46,47
e culturais
48
– no desempenho
acadêmico, este diagnóstico tem o potencial de nortear a melhoria da qualificação
dos cursos e do desempenho acadêmico, identificando fatores relacionados ao
desenvolvimento acadêmico e social dos estudantes, bem como dificuldades e
desafios no processo ensino-aprendizagem. Nesses trabalhos, a expressão
desempenho é utilizada para transmitir a idéia de performance, isto é, a ação de
conquistar algo, ser bem sucedido, através de esforço e habilidade
49
. No caso do
desempenho acadêmico, a avaliação é realizada por disciplina, incidindo sobre o
rendimento acadêmico, tradicionalmente verificado por meio de provas,
seminários, trabalhos de campo, exercícios teóricos e práticos, projetos, relatórios
e demais atividades realizadas pelos estudantes, às quais são atribuídas notas
50
.
Por outro lado, a literatura científica correspondente a pesquisas
odontológicas brasileiras que têm como população de estudo o corpo discente é
constituída por trabalhos centrados, fundamentalmente, em dados referentes à
ocasião de ingresso no ensino superior – identificação do perfil socioeconômico
51-
57
, dos motivos de escolha do curso
51,52,58-62
e da perspectiva profissional de
acadêmicos
51,52,57
– ou de conclusão do curso – identificação das expectativas de
acadêmicos quanto ao exercício da profissão
51,52
, e
análise dos resultados do
Exame Nacional de Cursos
63,64
e do Exame Nacional de Desempenho dos
Estudantes
57
, de modo a correlacioná-los com aspectos institucionais como o tipo
e localização regional da IES.
15
Há, portanto, uma lacuna na investigação dos fatores relacionados ao
desempenho acadêmico durante o processo de formação do cirurgião-dentista,
evidenciada pela escassez de trabalhos nacionais e internacionais, especialmente
no que se refere a pesquisas com amostras representativas de estudantes,
compreendendo todo o período do curso de graduação em Odontologia.
Já no cenário das Instituições Federais de Ensino Superior (IFES) do
Brasil, há a subutilização de dados acadêmicos potencialmente valiosos para a
gestão institucional
65
, especialmente frente à conjuntura atual caracterizada pelo
repensar do papel do ensino superior, na qual é discutida a institucionalização
da
avaliação promovida pelo Sistema Nacional de Avaliação da Educação Superior
(Sinaes)
66
**, a expansão universitária promovida pelo Programa de Apoio a
Planos de Reestruturação e Expansão das Universidades Federais (REUNI)
67
***
e, em âmbito local, o projeto de expansão universitária da Universidade Federal
de Goiás (UFGInclui) - o qual instituiu o sistema de cotas e a implementação do
REUNI.
Sendo assim, a análise do desempenho acadêmico apresenta-se como
recurso auxiliar para tomada de decisão da instituição de ensino quanto à
necessidade de reorientação curricular; além de fornecer indicadores sobre a
contribuição das IES para o alcance das metas nacionais em educação superior;
e subsidiar a análise das alterações ocorridas no corpo discente ao longo dos
anos de graduação
6,12-5
. Sendo assim, considerando o contexto atual do ensino
odontológico e a vasta quantidade de dados acadêmicos gerados anualmente
pelas IES, a análise dos preditores do desempenho acadêmico mostra-se
importante para o acompanhamento da trajetória acadêmica, fornecendo
** Criado pela Lei n° 10.861, de 14 de abril de 2004, o Sinaes é formado por três componentes
principais: a avaliação das instituições, dos cursos e do desempenho dos estudantes. O Sinaes
avalia todos os aspectos que giram em torno desses três eixos: o ensino, a pesquisa, a extensão, a
responsabilidade social, o desempenho dos estudantes, a gestão da instituição, o corpo docente,
as instalações e vários outros aspectos. Possui uma série de instrumentos complementares: auto-
avaliação, avaliação externa, Enade, Avaliação dos cursos de graduação e instrumentos de
informação (censo e cadastro). Os resultados das avaliações possibilitam traçar um panorama da
qualidade dos cursos e instituições de educação superior no País.
*** Programa instituído pelo Decreto nº 6.096, de 24 de abril de 2007, com o objetivo de propiciar a
inclusão, democratização do acesso e permanência de estudantes que apresentam condições
socioeconômicas desfavoráveis nos cursos de ensino superior. Suas diretrizes abordam a
preocupação em garantir a qualidade da graduação da educação pública a partir de uma formação
profissional mais abrangente, flexível e integradora. Para isto, faz-se necessária a reestruturação
curricular valorizando a interdisciplinaridade e favorecendo a superação da formação estritamente
profissionalizante e especialização precoce.
16
contribuição das IES para o alcance das metas nacionais em educação superior;
e subsidiar a análise das alterações ocorridas no corpo discente ao longo dos
anos de graduação
6,12-5
. É importante, portanto, para o acompanhamento da
trajetória acadêmica, fornecendo diagnóstico e subsídios para a implantação ou
manutenção de políticas educacionais que proporcionem melhor qualidade do
ensino.
Nesse sentido, considerando o contexto atual da educação superior,
especificamente do ensino odontológico, e frente à subutilização de uma vasta
quantidade de dados acadêmicos gerados anualmente pela Universidade Federal
de Goiás (UFG), o presente estudo propõe-se a analisar fatores influenciadores
do desempenho acadêmico de estudantes da Faculdade de Odontologia da UFG
(FO/UFG) que concluíram a graduação no período de 1988 a 2007. Considerando
as diversas e importantes interfaces a serem abordadas na análise do
desempenho do estudante, esse trabalho teve como enfoque a investigação de
fatores multidimensionais relacionados ao desempenho acadêmico traduzido sob
a forma de notas atribuídas aos estudantes nas disciplinas que compõem o
respectivo curso.
A investigação do tema proposto para análise nesse estudo constitui um
desafio e uma necessidade à FO/UFG, no sentido de fomentar discussões sobre
o ensino odontológico e subsidiar a instituição, inserida no movimento nacional de
reestruturação curricular desde 2004, em sua reorientação da formação
profissional.
17
2 OBJETIVOS
2.1 OBJETIVO GERAL:
Analisar fatores influenciadores do desempenho acadêmico de estudantes de
graduação da Faculdade de Odontologia da Universidade Federal de Goiás.
2.2 OBJETIVOS ESPECÍFICOS:
Conhecer a segmentação dos estudantes de acordo com o desempenho
acadêmico global e por grupos de disciplinas;
Conhecer a segmentação dos estudantes de acordo com o perfil
socioeconômico;
Analisar o perfil do desempenho acadêmico considerando os resultados das
análises de segmentação;
Investigar o impacto de variáveis multidimensionais (dados socioeconômicos,
classificação no vestibular e histórico acadêmico) no desempenho acadêmico
global e por grupos de disciplinas.
18
3 PROCEDIMENTOS METODOLÓGICOS
3.1 TIPO DE ESTUDO
Estudo transversal retrospectivo.
3.2 POPULAÇÃO DE ESTUDO
A população de estudo consistiu de todos os estudantes da FO/UFG que
concluíram a graduação no período de 1988 a 2007 (n=1182).
A FO/UFG é uma IFES da região central do Brasil, localizada em Goiânia,
Capital do Estado de Goiás, fundada em 1945, tornando-se pública e unidade
acadêmica da UFG em 1960. Os currículos existentes no período pesquisado
eram anuais, integralizados no período de cinco anos e compostos de ciclo básico
e profissionalizante, formando cerca de sessenta cirurgiões-dentistas anualmente.
3.3 PROCEDIMENTOS ÉTICO-LEGAIS
Seguindo as normas e preceitos éticos relativos a pesquisas que
envolvam seres humanos, o presente trabalho foi submetido ao Comitê de Ética
em Pesquisa da UFG, sendo aprovado sob o protocolo de número 081/2007
(Anexo 1). Além disso, a pesquisa foi autorizada pelo Departamento de Assuntos
Acadêmicos (DAA) (Apêndice A) e pelo Centro de Seleção (CS) (Apêndice B) da
UFG.
3.4 COLETA DE DADOS
Os dados foram coletados junto ao DAA e CS da UFG, no período de
janeiro a julho de 2008.
19
O DAA é o órgão responsável pelo controle da vida acadêmica dos
estudantes de graduação da UFG e pelos dados acadêmicos de egressos. Após
autorização da presente pesquisa pela diretoria, os extratos acadêmicos da
população de estudo foram identificados no sistema de gestão acadêmica e
impressos por funcionários dessa seção.
A partir do extrato acadêmico, foram obtidos os dados demográficos e de
identificação, além das notas, freqüência e situação do estudante (aprovado,
aprovado por média, reprovado por falta, reprovado por média, aproveitamento de
disciplina) em cada disciplina e carga horária cumprida ao final do curso. Para
cadastros incompletos no sistema, os dados foram complementados a partir do
histórico acadêmico arquivado em dossiês sob a guarda do DAA, mantidos no
setor de Serviço Geral da UFG.
O CS é o órgão responsável pelos processos seletivos para ingresso aos
cursos de graduação da UFG. Após aprovar a obtenção de dados dos processos
seletivos de estudantes da FO/UFG, a diretoria deste setor disponibilizou
planilhas eletrônicas em Excel referente às notas obtidas pelos ingressantes na
FO/UFG no período de 1991 a 2003, além das respostas do questionário
socioeconômico preenchido pelos candidatos inscritos no processo seletivo de
1999 a 2001. Os dados dos demais processos seletivos não fizeram parte da
pesquisa, pois não estão disponíveis nos registros do CS ou encontram-se
sumarizados em relatórios anuais (inviabilizando o acesso aos dados originais de
cada estudante, uma vez que as informações referem-se ao grupo de
ingressantes).
Em relação ao questionário socioeconômico (questionário elaborado pelo
CS que investiga o perfil socioeconômico dos ingressantes na UFG), foram
20
selecionadas as questões consideradas de maior relevância para o desempenho
acadêmico na graduação, conforme sugerido pela literatura científica
12-15,46,47
:
estado civil, moradia, emprego, nível de escolaridade dos pais, renda mensal e
tipo de auxílio que gostaria de receber da instituição, bem como informações
referentes ao ensino médio, domínio de língua estrangeira e à participação em
cursos pré-vestibulares e processos seletivos anteriores.
3.5 TRATAMENTO E INTERPRETAÇÃO DOS DADOS
A análise de cluster foi realizada para segmentação de grupos de
estudantes de acordo com o desempenho acadêmico (K-Means Cluster, a partir
do desempenho acadêmico global e por núcleos de disciplinas) e perfil
socioeconômico (TwoStep Cluster, a partir dos dados do questionário
socioeconômico).
Também conhecida como análise de segmentação, a análise de cluster é
um conjunto de técnicas estatísticas cujo objetivo é agrupar objetos segundo suas
características em grupos ou conglomerados homogêneos. Os conglomerados
obtidos devem apresentar tanto uma homogeneidade interna (dentro de cada
conglomerado), como uma forte heterogeneidade externa (entre conglomerados).
Consiste em uma técnica do tipo de interdependência, pois não é possível
determinar antecipadamente as variáveis dependentes e independentes. Ao
contrário, examina relações de interdependência entre todo o conjunto de
variáveis
68,69
.
O algoritmo K-Means Cluster é utilizado quando os dados recaem em um
número conhecido de agrupamentos a partir de variáveis contínuas
68,69
. Já o
21
algoritmo TwoStep Cluster encontra automaticamente o número apropriado de
agrupamentos, a partir de um conjunto de variáveis contínuas e/ou categóricas
69
.
Para verificação da validade externa, os clusters identificados foram
comparados entre si por meio de análise bivarida (Teste do Qui-quadrado e
ANOVA seguida do Teste de Tukey).
O desempenho acadêmico por grupo de disciplinas (média das notas do
estudante considerando as disciplinas dos ciclos básico e profissionalizante e as
disciplinas clínicas e não clínicas) foi analisado por meio do Teste t pareado e
Teste do Qui-quadrado.
A análise de regressão linear múltipla stepwise foi utilizada para investigar
a influência de variáveis independentes (classificação no vestibular e perfil do
estudante quanto ao gênero, idade, tipo de escola de conclusão do ensino médio,
tempo entre conclusão do ensino médio e ingresso na FO/UFG, tempo de
conclusão do curso, freqüência e carga horária cumprida) no desempenho
acadêmico global (média do desempenho do estudante considerando todas as
disciplinas do curso).
O desempenho acadêmico conforme os clusters identificados pelo perfil
socioeconômico dos estudantes foi investigado por meio de ANOVA seguida do
Teste de Tukey.
Para o tratamento estatístico dos dados, foi utilizado o programa
estatístico SPSS for Windows 16.0, considerando um nível de significância de 5%
(α=0.05).
22
4 PUBLICAÇÕES
Artigo 1 - Factors influencing students‟ academic performance in a Brazilian
undergraduate dental school
Artigo 2 - A cluster analysis of socioeconomic variables and their impact on
academic performance in a Brazilian undergraduate student sample
23
Artigo 1 - Factors influencing students‟ academic performance in a Brazilian
undergraduate dental school
ABSTRACT
Comprehensive assessment of students‟ academic performance plays an
important role in educational planning. The aim of this study was the recognition of
variables that influence academic performance in a retrospective sample including
all undergraduate students who entered in a Brazilian dental school, in a 20-years
period between 1984 and 2003 (n=1182). Age, gender and other educational
variables were used to predict academic performance in the overall curriculum and
course groups. Cluster analysis (K-means algorithm) categorized students into
groups of higher, moderate, or lower academic performance. Clusters of overall
academic performance showed external validity, as demonstrated by Chi-square
test and One-way ANOVA. Lower performance groups had smallest number of
students in overall performance and course groups clusters, ranging from 11.8%
(clinical courses) to 19.2% (basic courses). Students‟ performance was more
satisfactory in dental and clinical courses, rather than basic and non-clinical
courses (p <0.001). Better academic performance was predicted by lower time
elapsed between completion of high school and dental school admission, female
gender, better rank in admission test, rate of class attendance and student
workload hours (R
2
=0,491). Findings give evidence about predictors of
undergraduate students‟ performance and reinforce the need for curricular
reformulation focused on with improvement of the integration among courses.
Key Words: Dental education, Dental students, Curriculum, Cluster Analysis
24
INTRODUCTION
Recent efforts at a global level have focused on the discussion of professional
competences and quality standards in dental education. Despite the differences in
worldwide educational systems, there are convergent views toward curriculum
reformulation and improvement of knowledge about learning process in dental
education.
The American and the European Dental Education Association reinforce the need
of reform in dental education aiming to qualify graduates with satisfactory scientific
base for their professional practice and able to evaluate critically and integrate
selectively new scientific findings that emerge during their professional lifetimes.
1,2
In addition, they are expected to be able to work effectively with other health
professionals and to conduct their practices with a high level of sensitivity to the
ethical and psychosocial dimensions of patient care.
3-5
An ideal dental educational environment should enable students to acquire non-
clinical, clinical and interpersonal competences, which must be supported by
integration among knowledge of biomedical, behavioral, and dental courses, by
cognitive and psychomotor skills, and by professional and ethical values.
3,6
However, process of planning, implementation and sustaining of these deep
innovations is complex and dynamic.
5
Consequently, there has been increasing
interest in researching factors to maximize success of educational experience for
each student and outcome of undergraduate program.
Studies describing the educational experience of dental students traditionally have
focused on characteristics of the learner, academic environment and curriculum
25
structure.
3-26
Few reports regarding academic performance in dental school are
found in international literature.
27
Moreover, they were mainly cross-sectional
studies conducted in a small sample, and that analyzed relation between school
admission criteria and student performance in dental exams and basic or
preclinical courses.
Additional studies are essential to elucidate trends and predictors for successful
academic performance, plan and evaluate organizational development and
curriculum structure, as well as develop priority goals for research in dental
education. Thus, the aim of this study was to investigate the predictors of
undergraduate academic performance in a Brazilian school of dentistry.
MATERIALS AND METHODS
A retrospective cross-sectional study was designed including all students who
entered the School of Dentistry of Federal University of Goias, Brazil, during the
years 1984 to 2003. After the research protocol was approved by local Institutional
Review Board, data containing identification, demographic features and academic
performance in dental school admission test and graduation were retrieved by the
University Registrar‟s Office.
Academic performance in undergraduate courses was measured as the mean
value of four bimonthly examinations, rated quantitatively in a 0-10 scale. Outcome
variable was overall academic performance measured by grade average of all
courses within the undergraduate program. Independent variables included
gender, age, type of high school (private or public), elapsed time from completion
26
of high school until dental school admission, performance rank in admission test,
time to degree, rate of class attendance and student workload hours.
Academic performance in course groups was also measured for basic and dental,
and non-clinical and clinical courses. In this study, term „basic courses‟ refers to
biomedical and behavioral sciences, i.e. non-dental courses, while „non-clinical
courses‟ represents those that don‟t involve clinical practice, including dental
courses.
The whole sample was segmented into a pre-defined number of clusters according
to academic performance measures. The pattern of academic performance was
defined by cluster analysis with the K-means algorithm
28
. Cluster analysis aims to
identify natural groupings of data from a large data-set to produce a concise
representation of a system‟s behaviour. This statistical tool partitions subjects into
different groups on the basis of a minimal within-group and a maximal between-
group variation, without prejudgment. The algorithm in K-means cluster analysis
requires a priori definition of the number of clusters. The K-means algorithm
assigns each point to the cluster whose center (also called centroid) is nearest.
The center is the average of all the points in the cluster - that is, its coordinates are
the arithmetic mean for each dimension separately over all the points in the
cluster.
Classification procedure was performed based on overall academic performance
and by course groups, so that students categorized into a 3-cluster solution:
higher, moderate, or lower performance. Statistical differences among clusters
were investigated using Chi-square test and One-way ANOVA followed by Tukey
post hoc for nominal and continuous independent variables, respectively. Paired-
27
samples t test was used to compare student performance by different course
groups.
Stepwise multiple regression analysis was used to test the influence of
independent variables on the overall academic performance. Significance level
was set at p < 0.05. SPSS 16.0 for Windows was used for statistical analysis.
RESULTS
Study population included 1182 students, 63.1% females. Mean age was 19.54
years (SD=2.05) at the time of dental school admission and 23.54 years
(SD=2.07) at the time of graduate degree.
Table 1 includes descriptive analysis and comparison among clusters of overall
academic performance. Only the variables type of high school and rank in dental
school admission test were not statistically different among the clusters.
Taking into account the large data-set used in this research, cluster analysis was
used to identify satisfactorily three groups of students with differing patterns of
overall academic performance (lower, moderate or higher) and external validation,
as demonstrated by the bivariate analysis. Lower performance cluster
(n=195;16.5%) was characterized predominantly by males and older students, with
higher elapsed time from completion of high school until dental school admission,
as well lowest rate of class attendance, lowest student workload hours and higher
time to graduate degree. Higher performance cluster (n=456; 38.6%) presents
28
opposite patterns to the lower performance group. Moderate performance cluster
was the most prevalent (n=531; 44.9 %), with intermediary academic measures.
The analysis of academic performance by course groups (Table 2) revealed that
the segments of lower performance comprised the smallest number of students,
ranging from 11.8 (clinical courses) to 19.2% (basic courses). There were
differences in student performance between the courses groups (p <0.001), with
higher scores in dental and clinical courses.Table 3 shows student distribution
according academic performance by course groups.
Stepwise multiple regression analysis (Table 4) of the influence of independent
variables on overall academic performance resulted in R
2
value that indicates that
the final model accounts for 49% of the variance in outcome variable.
DISCUSSION
The effectiveness of educational principles and curriculum structure becomes
known when student performance is assessed.
29
This study focused on
identification of predictors variables of academic performance in a 5-year program
of dental school.
As well as for overall performance, segments of lower performance also showed
smallest number of students among academic performance clusters by course
groups. However the significant number of students with lower academic
performance reveals need for specific educational strategies for this segment.
29
Student performance was more satisfactory in dental and clinical courses, rather
than basic and non-clinical courses, respectively. This finding corroborates those
critical dichotomies in the curriculum: lack of integration between basic and clinical
courses, and non-clinical and dental courses.
1,6,7,9,10,13-16,18,20,24
Dentistry is often
criticized due to their technical nature and domination of biomedical model,
emphasizing the mechanical versus the biological nature of dentistry.
7,10
Fugill
30
found student dissatisfaction with lack of contextualization of knowledge
they received. According to that author, this may be attributed to the fact that
students do not have experience necessary to classify and bring together didactic
information and practice. Boyd
8
reports that students recognize the experience of
connecting what they learn in class with a “real” patient as being somewhat
disorienting. In this context, Gick and Holyoak
31
demonstrate that learning is
facilitated by contexts that demonstrate the usefulness of the knowledge in solving
problems. Therefore, it should not be surprising that students have difficulty
reorganizing information to make it useful for solving clinical problems.
10
Henzi et al.
32
investigated which strengths and weaknesses of dental school
curriculum, in point of view of students. Participants of this study were positive
about their learning experiences in dental schools, but recognized several
problematic areas, including large portions of the curriculum identified as being of
questionable relevance, mainly in the biomedical and behavioral sciences. Their
findings show that students desire a well-organized curriculum with the best
possible clinical experience. Similar results were found in survey commissioned by
American Dental Association Survey Center,
33
reinforcing that the desire of dental
30
students to condense non-clinical topics ought to be cause for deliberation among
dental educators.
Most of dental schools were still organized along traditional course
boundaries.
10,14,17,20
In the traditional 5-year Bachelor of Dental Surgery program,
students take courses in biomedical sciences and general education during the
first four semesters, while subsequent semesters focus on the clinical courses
and, more directly, on clinical training.
Gradually, it became recognized that approaches which integrate basic and
clinical courses provided a more meaningful, holistic preparation for
dentistry.
1,7,14,20,21,29,34
Curriculum integration is essential in the preparation of the
new general dentist able to solve patients‟ problems and incorporate new concepts
and therapies into health care.
1,10,14,16,21,29
This involves making a markedly significant cultural and attitudinal shift in dental
schools, with recognition that basic and other sciences are important to form a
competent dentist in 21st century.
7,35
Planning and implementing of these changes
represent considerable risk of financial burdens on schools, need for training
programs and workshops for faculty, increased training time for students, and re-
structuring licensure procedures and curriculum.
7
Findings of our study also revealed that overall academic performance was related
to elapsed time from completion of high school until dental school admission,
gender, dental school admission, rank in admission test, rate of class attendance
and student workload hours.
31
Student selection and recruitment are considered as vital in the successful
outcome of dental education.
11
Admission to graduate programs in the health
professions is based on different factors, including undergraduate/pregraduate
academic performance, extracurricular and research activities, interviews, and
psychomotor assessments, varying the degree of emphasis placed on these
factors according to the institutions.
11,27,36
In Brazil the dental school admission is
focused on purely academic criteria (grades achieved in university entrance
examination).
Admission information has historically been used as a predictor of academic
success in dental school.
36
Previous studies reports relationship between
admission criteria (college grade point average -GPA, subtest scores on the
Dental Admission Test – DAT, interviews) and scores on the National Board
Dental Examination (NBDE) or student performance on basic and predental
courses.
26,27,36-40
Our findings reveal that admission criteria are related with
academic performance during the formative years. According McManus and
Richards,
34
this may be attributed to the three arguments: achievement, ability and
motivation. This authors claim that successful performance in admission test is a
reflection of the intelligence and motivation of the student, which will have a
positive effect on their success in university performance. Others studies showed
that admission level has limited value as a predictor of students' performance.
41-45
Traditional studies of impact of gender on student performance found that men
outperformance women, attributing possible reasons as: women‟s lowest sense of
self-esteem, stereotype threat, differential speeds, aversion to risk taking, test
bias, fear of success, test anxiety, and certain other personal characteristics.
25,46,47
32
Recently, Fields et al.
38
investigated the impact of gender in academic
performance among dental students and found that there were no significant
differences. Authors related this to no presence of true differences or low power of
the sample to detect small differences. On other hand, our results show that trend
of feminization of dentistry
12,23,26
is accompanied by better performance of women
in academic assessments.
The relationship between academic performance and rate of class attendance and
student workload hours is possible related to higher student involvement in
academic experiences of learning and research activities. Impact of elapsed time
from completion of high school until dental school admission in academic
performance reinforces importance of previous educational experiences for
success in university entrance immediately after high school and academic
performance at graduation.
Assessment of academic performance plays a strategic role in pedagogical
planning of educational institutes. Our study gives evidence about predictors of
undergraduate academic performance and reinforces the need for curricular
changes with improvement of the integration among courses. This critical
evaluation proves significant information for dental schools currently engaged in,
or about to embark upon, the task of planning and implementing strategies for
training of general dentist according new required competences.
Regional and cultural differences in educational principles may influence academic
performance and suggest additional studies to corroborate our findings. Additional
longitudinal studies are needed to evaluate influence of academic performance in
professional behavior and involvement.
33
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39
Table 1 – Clusters characteristics according to independent variables.
Clusters
Variable
Lower performance
(n=195)
Moderate performance
(n=531)
Higher performance
(n=456)
P*
Age at admission (years)**
20.48 (3.39)
A
19.82 (2.06)
B
19.04 (1.74)
C
<0.001
Gender
Female
34.4
63.5
75.0
<0.001
Male
65.6
36.5
25.0
Type of high school
Private
90.1
91.6
92.9
N.S.
Public
9.9
8.4
7.1
Elapsed time from completion of
high school until dental school
admission (years)*
2.09 (2.76)
A
1.55 (1.65)
A
0.93 (1.11)
B
<0.001
Rank in admission test**
28.70 (14.43)
28.12 (16.17)
27.31 (16.07)
N.S.
Time to degree (years)**
5.10 (0.49)
A
4.99 (0.13)
B
4.95 (0.20)
B
<0.001
Rate of class attendance (%)**
89.61 (4.11)
A
92.44 (2.06)
B
93.92 (1.75)
C
<0.001
Student workload hours**
4351.46 (223.83)
A
4413.39 (311.20)
B
4570.82 (444.84)
C
<0.001
*
Chi-square test and the One-way ANOVA followed by Tukey post hoc test
** Mean (s.d.)
39
40
Table 2 – Comparison of academic performance by discipline groups.
n (%)
Mean (s.d.)
IC 95%
P*
Overall disciplines
High performance
456 (38.6)
8.13 (0.26)
8.10 – 8.15
Moderate performance
531 (44.9)
7.41 (0.21)
7.39 – 7.43
Low performance
195 (16.5)
6.60 (0.40)
6.54 – 6.66
Basic disciplines
<0.001
High performance
466 (39.7)
7.95 (0.41)
7.91 – 7.98
Moderate performance
482 (41.1)
6.85 (0.32)
6.82 – 6.88
Low performance
225 (19.2)
5.63 (0.54)
5.55 – 5.69
Dental disciplines
High performance
501 (42.4)
8.18 (0.24)
8.15 – 8.20
Moderate performance
552 (46.7)
7.52 (0.21)
7.50 – 7.53
Low performance
129 (10.9)
6.70 (0.43)
6.63 – 6.78
Non-clinical disciplines
<0.001
High performance
474 (40.1)
8.12 (0.32)
8.09 – 8.15
Moderate performance
521 (44.1)
7.28 (0.25)
7.25 – 7.30
Low performance
187 (15.8)
6.30 (0.49)
6.23 – 6.37
Clinical disciplines
High performance
500 (42.3)
8.13 (0.26)
8.11 – 8.16
Moderate performance
543 (45.9)
7.40 (0.21)
7.38 – 7.42
Low performance
139 (11.8)
6.59 (0.44)
6.51 – 6.66
* Paired-samples t test
41
Table 3 - Student distribution according academic performance by discipline groups.
Basic disciplines
Higher performance
Moderate performance
Lower performance
P*
Dental disciplines
Higher performance
382
84
0
<0.001
Moderate performance
117
339
26
Lower performance
1
122
102
Non-clinical disciplines
Higher performance
Moderate performance
Lower performance
P*
Clinical disciplines
Higher performance
377
95
2
<0.001
Moderate performance
120
363
38
Lower performance
3
85
99
*
Chi-square test
41
42
Table 4 - Stepwise multiple regression of the influence of independent variables in the overall student
performance.
β
Std
error
Standardized
Coefficients
95% CI β
P
*
Frequency in course (%)
0.111
0.006
0.514
0.099 – 0.123
< 0.001
Student workload hours
0.001
0.000
0.221
0.000 – 0.001
< 0.001
Elapsed time from completion of high school
until dental school admission (years)
-0.070
0.009
-0.195
-0.089 – -0.051
< 0.001
Gender: Female
-0.230
0.033
-0,189
-0.296 – -0.164
< 0.001
Rank in admission test
-0.003
0.001
-0.077
-0.005 – -0.001
< 0.01
*
Stepwise multiple regression
R
2
= 0,491
42
43
Artigo 2 - A cluster analysis of socioeconomic variables and their impact on
academic performance in a Brazilian undergraduate student sample
ABSTRACT
The recognition of socioeconomic characteristics of dental students provides strategic
information for planning educational policies in the university environment. The aims
of this study were to identify natural segmentation of freshman undergraduate dental
students based on socioeconomic variables, and to subsequently investigate their
impact on academic performance in a sample of Brazilian undergraduate students.
Cluster analysis (two-step algorithm) was used to segment students who entered
dental school in the time period from 1999 to 2001 (n=158) into groups based on
responses to a socioeconomic questionnaire completed by students at the time of the
admission examination. Clustering analysis revealed three natural groups. Age, the
parents‟ level of education, and performance on the first admission test were the most
important variables for cluster segmentation. Cluster 1 (n=42; 26.6%) was
characterized by female students with higher socioeconomic status and better
previous educational indicators. Cluster 2 (n=62; 39.2%) represented disadvantaged
socioeconomic profiles, with a predominance of females and older students. Cluster 3
(n=54; 34.2%) showed similar socioeconomic characteristics to cluster 1, except for
male prevalence, higher age, and experiencing difficulty in the admission test.
Betweengroups comparison showed significant differences (cluster 1> 2> 3) in overall
performance and performance in course groups (p<0.05). The impact and
significance of students‟ socioeconomic status on academic performance underlines
45
INTRODUCTION
Studies exploring socioeconomic characteristics of dental students provide valuable
information for planning educational strategies and policies, and improve faculty
expertise on learning issues. Numerous studies on dental student attributes have
mainly focused on socio-demographic characteristics and analysis of changes over
time, as well as investigating their reasons for choosing dentistry, academic
involvement, and future professional plans.
1-7
Recently, there has been increasing interest on inclusion policies and practices, such
as attainment of equality in access to higher education and the relevance of cultural
and social diversity in academic training of students and professionals in health care
areas.
8-16
The American Dental Education Association also has reinforced the need
for diversity in the workforce to achieve professional excellence and address iniquities
in the oral health care services.
17,18
Reports about the process of socialization and professionalization of medical students
stressed that the matrix of social relationships in which a student internalizes attitudes
and values will strongly determine his/her professional behavior.
19-24
Their findings
support that minority students are more likely to practice in areas of physician
shortage and treat disadvantaged and chronically ill patients.
19,23-24
Moreover, despite
the hypothesis that increasing the number of students from underrepresented
segments of the population would result in decreased quality standards for the
general undergraduate population, evidence has suggested that there is no difference
46
in the academic performance of these “underrepresented” students when compared
to other students.
19-22
In dental education literature, there is little to no evidence regarding the impact of
socioeconomic status on the academic performance of students. This is an especially
relevant issue in developing countries, where major inequality in the distribution of
wealth is a serious social concern. Improved knowledge of this relationship will
provide a basis to guide the design of dental education programs, thus playing an
important role in the current context of increasing demand for higher education and
implementation of access and inclusion policies in Brazil. Thus, the aims of this study
were to identify natural segmentation of freshman undergraduate dental students
based on socioeconomic variables, and to subsequently investigate the impact of
socioeconomic-based clusters on academic performance in a sample of Brazilian
undergraduate students.
MATERIAL AND METHODS
A retrospective cohort study was designed to include all students who entered the
School of Dentistry of Federal University of Goias, Brazil, from 1999 to 2001.
After the research protocol was approved by the local Institutional Review Board, data
were gathered from the University Registrar‟s Office, including student scores on the
dental school admission test, performance in undergraduate courses, and answers to
a socioeconomic questionnaire completed by students at the time of the university
47
admission test. Data retrieved from questionnaires provided the following
socioeconomic variables: age, gender, marital status, living arrangement in the last
two years, parental education, monthly family income, employment experience, the
need for financial aid or support from the university, information about high school,
language proficiency, participation in preparatory courses for university admission,
and previous submissions to the admission test.
Two-step cluster analysis was used to segment samples into n number of clusters
based on the socioeconomic variables, using an autoclustering algorithm. Cluster
analysis was used as an exploratory data analysis technique designed to reveal
natural grouping from latent patterns in a large dataset on the basis of a minimal
within-group and a maximal between-group variation, without prejudgment. The two-
step algorithm analysis allows subjects to be segmented in an optimal number of
clusters according to continuous and categorical variables.
25
The variable importance for cluster segmentation was ranked by Chi-square test (for
nominal variables) or t-test (for continuous variables) in which each cluster group was
tested against the overall group. Since multiple tests were performed, Bonferroni
adjustments were applied to control the false-positive error rate. An alternative
importance measure, which has the advantage of placing both types of variables on
the same scale, is based on statistical significance values using -log
10
of the statistical
significance (-log
10
P-value). This transformation stretches the original scale to 0 to
infinity (instead of a small band from 0 to 1), so that larger values of log
10
of P-value
equate to greater significance.
25
48
Subsequently, the influence of the student‟s group classification on academic
performance was tested. It was hypothesized that cluster analysis segmentation can
be associated with distinct academic performance levels. In this study, academic
performance in undergraduate courses was measured as the mean value of four
bimonthly examinations, rated quantitatively on a 0-10 scale. Overall academic
performance refers to the grade point average from all undergraduate courses.
Academic performance in course groups relates to performance in basic and dental,
and non-clinical and clinical courses. The term “basic courses” refers to biomedical
and behavioral sciences, i.e. non-dental courses, while “non-clinical courses”
represents those that don‟t involve clinical practice, including dental courses.
Bivariate analysis using Chi-square test and one-way ANOVA followed by Tukey post
hoc test were used to compare socioeconomic status and academic performance
among clusters with levels of significance defined as p<0.05. SPSS 16.0 for Windows
was used for clustering and all descriptive and hypothesis testing analyses.
RESULTS
The study sample included 158 students, 58.2% females. Mean age was 19.58 years
(SD=1.83) at the time of dental school admission and 23.55 years (SD=1.81) at the
time of graduation. The average student was female, young, and single. In general,
students from this study experienced comfortable lifestyles, possessed relatively little
employment experience, had a great investment in their education, and came from
parents with high levels of education and high monthly incomes.
49
The auto-clustering algorithm combined 100% of the cases in a three-cluster solution,
enclosing 26.6, 39.2 and 34.2% of the sample. Table 1 includes descriptive analysis
and comparison among clusters of socioeconomic status. Only the variables „marital
status‟, „time that the subjects attended high school (daytime/night-time)‟, and „rank in
dental school admission test‟ were not statistically different among the clusters.
Significant variables for segmentation of each cluster are shown in Table 2, where
within-group rank of variable importance for cluster segmentation is depicted for each
of the three clusters.
Cluster 1 (n=42; 26.6%) was characterized by a predominance of female and younger
students without previous employment experience, living in Goiania with parents who
have higher levels of education and monthly incomes. In these families, education
costs have the biggest impact on finances, which is evidenced by larger investments
in private schools and language courses. Students of this segment had significant
success in the university entrance examination immediately after high school.
Favorable socioeconomic status was related to a smaller need to receive financial aid
or support from the university since these students are primarily supported by their
families. In contrast, Cluster 2 (n=62; 39.2%) represented a more disadvantaged
socioeconomic status, with a predominance of females and older students. Cluster 3
(n=54; 34.2%) showed similar characteristics to the first cluster, except for a
prevalence toward males and older students, more participation in previous university
entrance examinations, more participation in preparatory courses for admission, and
fathers with higher levels of education.
50
Analysis of academic performance (Table 3) revealed that all clusters showed
satisfactory performance. Differences in overall academic performance (p <0.05) and
by course groups (0.01<p<0.05) were found amongst clusters, except for clinical
courses. In all cases, Cluster 3 demonstrated a significantly lower level of
performance.
DISCUSSION
Socioeconomic characteristics play an important role in the development of students‟
intellectual and non-intellectual faculties, and may influence their commitment level to
a profession.
22
Analysis of socioeconomic status of undergraduate students is
important in understanding their background, priorities, and socialization and
academic process.
4,7
The present study assessed socioeconomic status of Brazilian
dental students and the impact of these characteristics on their academic
performance.
Predominance of females among dental students is reported in Australia
1,3,6
,
Canada
19
, Denmark
7
, France
2
, New Zealand
3
, Nigerian
4
, United Kingdom
16
, and
United States
10
. Besides a growing trend of feminization in the field of dentistry,
previous studies have indicated that women follow a pattern different from their male
counterparts in relation to working organization, time spent at work, and income.
2,3,5,7
Brazilian universities accept dental school students directly from high school. Thus
students of our research are young, with a mean age of 19.58 years (s.d.=1.83) at the
51
time of dental school admission. In educational systems where students must
possess a previous degree prior to entering dental school, this age is obviously
higher. In Sydney, for example, Hennequin et al.
3
found that first-year dental students
have a mean age of 24.6 years (s.d.=3.9).
Most parents of dental students have high levels of education and income. Students
are predominately single, without employment experience, reside with their parents,
and are financially dependent.
1-7,19
Cluster 1 and 3 characteristics (n=96; 60.8%)
corroborate previously published trends that most dental students came from more
privileged socioeconomic groups.
1-7,19
In our study, almost 40% of students (Cluster 2) had lower socioeconomic status than
their collegues, which does not imply that this group corresponds to a representative
sample of the overall disadvantaged population. On the contrary, the presence of
economic, cultural, and social barriers to access higher education is still a major
problem in Brazilian society and a primary reason for social inequity. Hung et al.
10
reinforced that a lack of diversity on campus and limited social, academic, and
financial support are significant barriers for recruiting and retaining minority students.
The impact of students‟ socioeconomic status on academic performance was related
to multidimensional factors, revealing the importance of development of specific
educational strategies for each cluster. Cluster 1, characterized by female students
with higher socioeconomic status and superior secondary formation, had significant
success in university admissions immediately after high school and in the
undergraduate program. Cluster 3 demonstrated similar socioeconomic
52
characteristics to the first cluster, except for the prevalence of male students who did
not enter dental school directly from high school and lower academic performance.
Surprisingly, Cluster 2 represented the second best academic performance. Despite
disadvantaged socioeconomic status and a need for selfsupport during completion of
their degree, this segment revealed significant ability to overcome personal and
academic difficulties. This evidence reinforces the importance of financial support
from the universities to maximize the successful educational experience of these
students. Moreover, a better understanding of the reasons and associated factors for
these differences in academic performance among students is needed in future
studies.
In regards to the equality of access to education, our findings can guide dental
schools in the planning and implementation of policies for recruiting, admitting, and
retaining of minority students. Recently, universities have introduced academic
support programs as well as lower academic criteria for admission of
underrepresented segments.
8-16
On the other hand, in corroboration with previously
reported studies, our results show that socioeconomically disadvantaged students
have satisfactory academic performance, despite the need for financial support.
19-22
Bediako et al.
26
found that an academically rigorous high school program for minority
and economically disadvantaged students led to higher rates of application and
admission to medical school.
Assessment of the impact of today‟s societal and economic changes and expansion
on dental care systems in characteristics of freshman undergraduate students is
53
important for the current context of expansion of higher education.
2,3,7
Progressive
increases in the diversity of the student population will have an impact on
undergraduate, continuing, and postgraduate education, professional retention, and
practice location.
3,23
Additional studies are needed to confirm the present results since regional and
cultural differences may play a role in socioeconomic status and student
performance. Longitudinal approaches are essential to elucidate the effectiveness of
different strategies to attract, recruit, and retain minority students and assess their
impact in professional practice.
CONCLUSION
Clustering analysis showed significant socioeconomic differences among students,
which influenced overall performance and performance in course groups. The impact
and significance of students‟ socioeconomic status on academic performance
emphasizes the need of individualized educational programs and the importance of
financial support to maximize successful educational experiences of
socioeconomically disadvantaged Brazilian dental students.
54
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Hennequin M, Tubert S, Devillers A, Müller M, Michaïlesco P, Peli JF,
Pouëzat J. Socio-economic and schooling status of dental
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Marino RJ, Morgan MV, Winning T, Thomson WM, Marshall RI,
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Orenuga OO, da Costa OO. Characteristics and study motivation of
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Sivaneswaran S, Barnard PD. Some social characteristics and
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Vigild M, Schwarz E. Characteristics and study motivation of Danish
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Andersen RM, Carreon DC, Friedman JA, Baumeister SE, Afifi AA,
Nakazono TT, Davidson PL. What enhances underrepresented minority
recruitment to dental schools? J Dent Educ, 71:994-1008, 2007.
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Formicola AJ, Klyvert M, McIntosh J, Thompson A, Davis M, Cangialosi T.
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Hung R, McClendon J, Henderson A, Evans Y, Colquitt R, Saha S.
Student perspectives on diversity and the cultural climate at a U.S.
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11
Mahal AS, Shah N. Implications of the growth of dental education in India.
J Dent Educ, 70:884-91, 2006.
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Murray-García JL, Harrell S, García JA, Gizzi E, Simms-Mackey P. Self-
reflection in multicultural training: be careful what you ask for. Acad
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Price SS, Brunson WD, Mitchell DA, Alexander CJ, Jackson DL.
Increasing the enrollment of underrepresented minority dental students:
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Saha S, Guiton G, Wimmers PF, Wilkerson L. Student body racial and
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Veal K, Perry M, Stavisky J, Herbert KD. The pathway to dentistry for
minority students: from their perspective. J Dent Educ, 68:938-46, 2004.
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Bedi R, Gilthorpe MS. Social background of minority ethnic applicants to
medicine and dentistry. Br Dent J, 189:152-4, 2000.
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Sinkford JC, Valachovic RW, Harrison SG. Continued vigilance:
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Sinkford JC, Valachovic RW, Harrison SG. Underrepresented minority
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Dhalla IA, Kwong JC, Streiner DL, Baddour RE, Waddell AE, Johnson IL.
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Fredericks MA, Blanchet L, Mundy P. The relationship between social
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Fredericks M, Mundy P.The relationship between social class and national
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Fredericks M, Mundy P. The relationship between social class, stress-
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Moy E, Boreman BA. Physician race and care of minority and medically
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26
Bediako MR, McDermott BA, Bleich ME, Colliver JA. Ventures in
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58
Table 1 - Characterization of clusters according to socioeconomic variables.
Total
Clusters
Variables
1 (n=42)
2 (n=62)
3 (n=54)
P
†
Age at admission (years)*
19.58 (1.83)
18.21 (0.56)
A
20.30 (2.06)
B
19.83 (1.64)
B
<0.001
Gender – Female
58.2
78.57
66.13
33.33
<0.001
Marital status – Single
98.1
97.62
96.77
100
N.S.
Reside with parents
84.2
92.86
69.35
94.44
<0.001
Public high school
17.1
2.38
37.10
5.56
<0.001
Daytime high school
95.6
100
93.55
94.44
N.S.
Traditional high school (3 years)
95.6
100
90.32
98.15
<0.001
Participation in preparatory courses for admission
62.0
0
83.87
79.63
<0.001
Approved in first admission test – Yes
73.4
16.67
88.71
100
<0.001
Need for financial aid or support from university
75.9
66.67
98.39
57.41
<0.001
Need for own resources for support during undergraduate
34.2
11.90
51.61
31.48
<0.001
Employment experience at time of dental admission test
9.5
0
22.58
1.85
<0.001
Previous employment experience
17.1
4.76
32.26
9.26
<0.001
Family living in Goiania
71.5
78.57
56.45
83.33
<0.01
Father with high level of education
53.8
71.43
11.29
88.89
<0.001
Mother with high level of education
47.5
57.14
16.13
75.93
<0.001
Monthly family income greater than R$ 1.200,00**
67.1
92.86
33.87
85.19
<0.001
Education costs have high impact on family finances
67.7
78.57
56.45
72.22
<0.05
Foreign language proficiency
57.0
71.43
33.87
72.22
<0.01
Rank in admission test **
28.58 (16.59)
29.33 (18.71)
26.01 (14.72)
30.50 (16.08)
N.S.
* Mean (s.d.)
** Aproximately 4 times Brazilian minimum wage
†
Chi-square test and the one-way ANOVA followed by Tukey post hoc test
58
59
Table 2- Within-group rank of variable importance for cluster segmentation
Clusters
Variables
Chi-square or
t-test value*
-Log
10
P-Value**
P
1
Age at admission (Younger)
-15.77
18.18
<0.001
Approved in first admission test (Yes)
69.3
16.08
<0.001
Participation in preparatory courses for admission (No)
68.6
15.92
<0.001
Monthly income (Higher)
12.6
3.42
<0.001
Need for own resources for support during undergraduate (No)
9.3
2.63
<0.01
2
Father with high level of education (No)
45.1
10.72
<0.001
Monthly income (Lower)
31.0
7.58
<0.001
Mother with high level of education (No)
24.4
6.11
<0.001
Type of high school (Public)
17.5
4.55
<0.001
Need for financial support from university (Yes)
17.1
4.45
<0.001
Language proficiency (No)
13.5
3.62
<0.001
Participation in preparatory courses for admission (Yes)
12.6
3.4
<0.001
Employment experience at time of dental admission test (Yes)
12.4
3.36
<0.001
Living arrangement in the last 2 years (Living without parents)
10.2
2.86
<0.01
Previous employment experience (Yes)
10.1
2.82
<0.01
Age at admission (Older)
2.1
2.74
<0.01
3
Father with high level of education (Yes)
26.8
6.64
<0.001
Approved in first admission test (No)
19.6
5.01
<0.001
Mother with high level of education (Yes)
17.5
4.55
<0.001
Gender (Male)
13.8
3.68
<0.001
Participation in preparatory courses for admission (Yes)
12.3
3.34
<0.001
Need for financial support from university (No)
10.2
2.84
<0.01
* Chi-square or t-test was used for nominal or continuous variables, respectively
** -Log
10
P-Value : larger value is more significant
59
60
Table 3 - Comparison of academic performance among clusters.
Mean (s.d.)
IC 95%
P*
Overall courses
<0.05
1
8.13 (0.31)
A
8.03 – 8.23
2
8.00 (0.43)
AB
7.89 – 8.11
3
7.89 (0.44)
B
7.76 – 8.01
Basic courses
1
7.74 (0.45)
A
7.60 – 7.88
<0.01
2
7.59 (0.63)
AB
7.42 – 7.75
3
7.37 (0.75)
B
7.16 – 7.57
Dental courses
1
8.26 (0.29)
A
8.17 – 8.36
<0.05
2
8.13 (0.39)
AB
8.03 – 8.23
3
8.05 (0.38)
B
7.94 – 8.15
Non-clinical courses
1
7.92 (0.40)
A
7.79 – 8.04
<0.01
2
7.76 (0.55)
AB
7.62 – 7.90
3
7.58 (0.60)
B
7.41 – 7.74
Clinical courses
1
8.31 (0.26)
A
8.23 – 8.39
N.S
2
8.20 (0.37)
AB
8.10 – 8.29
3
8.14 (0.34)
B
8.05 – 8.23
* One-way ANOVA followed by Tukey post hoc test
61
5 PARA NÃO CONCLUIR... MAS PARA DESAFIAR...
A análise de fatores influenciadores do desempenho acadêmico fornece
subsídios para atuação de administradores, autoridades ligadas a políticas públicas e
pesquisadores do ensino superior. Os resultados da presente pesquisa corroboram
achados da literatura brasileira e internacional, evidenciando a necessidade de
reestruturação curricular e reorientação da formação profissional nos cursos de
graduação em Odontologia.
A diferença no desempenho acadêmico entre as disciplinas dos ciclos básico
e profissionalizante e entre as disciplinas clínicas e não clínicas revela a importância
da operacionalização de práticas integradoras no currículo de graduação, bem como
da sensibilização e capacitação pedagógica do corpo docente para rompimento da
ênfase dada às disciplinas clínicas e profissionalizantes, visando minimizar a
tendência de especialização precoce entre os estudantes e favorecer a formação
integral do futuro cirurgião-dentista.
Embora a maioria tenha apresentado desempenho acadêmico alto e médio,
o número considerável de estudantes com baixo desempenho sugere a necessidade
de estratégias educacionais diferenciadas para este segmento. A importância destas
medidas é ressaltada frente à tendência de os estudantes permanecerem nos
respectivos clusters de desempenho, independentemente do grupo de disciplina
analisado.
A verificação de um melhor desempenho acadêmico entre os estudantes
com menor tempo entre a conclusão do ensino médio e ingresso na faculdade
62
reforça a importância de dados de escolarização anteriores à graduação, revelando
que estudantes com melhor formação apresentam melhor desempenho no vestibular
e no curso de Odontologia. A relação entre desempenho acadêmico e frequência e
carga horária cumprida evidencia que estudantes com melhor desempenho
correspondem àqueles com maior envolvimento em atividades de ensino, pesquisa e
extensão. Já as diferenças no desempenho acadêmico de acordo com o gênero,
mostram que a tendência à feminilização da profissão odontológica é acompanhada
pelo melhor desempenho acadêmico das mulheres.
O perfil socioeconômico da amostra de estudantes de Odontologia
investigado nesse estudo corrobora evidências científicas prévias: a maioria dos
estudantes caracterizou-se por indivíduos jovens, solteiros, do gênero feminino,
sustentados por pais de alta escolaridade e renda mensal. A grande contribuição da
presente pesquisa refere-se à análise de segmentação dos estudantes de acordo
com o perfil socioeconômico, a qual identificou três clusters com características
distintas e diferentes padrões de desempenho acadêmico.
Os achados de que estudantes com perfil socioeconômico desfavorável
apresentam desempenho satisfatório sugerem que esse segmento possui
significante capacidade de superação das dificuldades pessoais e acadêmicas. Além
disso, a importância atribuída por esses estudantes ao auxílio da institucão para seu
sustento durante a graduação ressalta a importância da implementação de políticas
institucionais de assistência.
Faz-se necessário, portanto, o delineamento de políticas públicas e
institucionais que permitam a permanência e a conclusão do curso pelos estudantes,
63
em uma perspectiva de inclusão social, formação integral, melhoria do desempenho
acadêmico e de sua qualidade de vida.
Ressalta-se ainda que, apesar de seu uso recente em pesquisas
odontológicas, a análise de segmentação apresenta-se como recurso estratégico em
estudos voltados à análise de desempenho acadêmico. Como limitação do presente
trabalho, deve-se considerar que o desempenho acadêmico revelado através de
notas finais dos estudantes depende diretamente da competência dos professores
para avaliar os estudantes, bem como dos métodos utilizados. Contudo, os dados
coletados se apresentam abrangentes e confiáveis para alcance dos objetivos
propostos.
Nesse contexto, os resultados desta pesquisa têm o potencial de fomentar
discussões emergentes frente à conjuntura atual de reorientação da formação
profissional em Odontologia e de ampliação da demanda e democratização do
acesso ao ensino superior, além de fornecer diretrizes ao processo de
institucionalização da avaliação. Este estudo destaca, ainda, algumas áreas de
investigação relacionadas ao desempenho acadêmico, no âmbito das IES, de
políticas públicas e da atuação profissional.
64
REFERÊNCIAS****
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APÊNDICES
APÊNDICE A – Ofício de solicitação da autorização da diretoria do
Departamento de Assuntos Acadêmicos da UFG para acesso aos dados
acadêmicos requeridos para a presente pesquisa.
APÊNDICE B – Ofício de solicitação da autorização da diretoria do Centro de
Seleção da UFG para acesso aos dados dos processos seletivos da população
de estudo.
74
75
76
ANEXO
ANEXO 1 – Protocolo de aprovação do presente estudo pelo Comitê de Ética em
Pesquisa da UFG (n
o
081/2007).
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