Sistema inteligente para a predição de grupo de risco de evasão discente
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Data
2014-02-24
Autores
Martinho, Valquíria Ribeiro de Carvalho [UNESP]
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Universidade Estadual Paulista (Unesp)
Resumo
A evasão escolar é um dos problemas mais complexos e cruciais no âmbito da educação. Está presente e é motivo de preocupação nos vários níveis e modalidades de ensino, além de ferir o princípio da dignidade humana. No que tange ao ensino superior, internacionalmente, o fenômeno é objeto de atenção e de cuidado, no intuito de aumentar os índices de permanência e conclusão dos estudantes de graduação e minimizar os prejuízos sociais, econômicos, políticos, acadêmicos e financeiros causados a todos os envolvidos no processo educacional. Nesse contexto, é imprescindível o desenvolvimento de métodos e instrumentos eficientes e eficazes para predição, avaliação e acompanhamento de estudantes em risco de evasão, possibilitando o planejamento e a adoção de medidas proativas no intuito de minimizar a situação. Assim sendo, esta pesquisa tem por objetivo apresentar as potencialidades de um sistema inteligente capaz de identificar, de maneira proativa, continuada e acurada, o grupo de risco de evasão discente, da educação clássica-presencial, no ensino de nível superior. No desenvolvimento deste sistema foi utilizada uma das técnicas da inteligência artificial, as Redes Neurais Artificiais, mais especificamente, a Rede Neural ARTMAP-Fuzzy, uma rede neural da família ART (Adaptive Resonance Theory) que possibilita o aprendizado continuado do sistema. Para o treinamento e teste da Rede Neural e, posteriormente, a validação do sistema proposto foram utilizados os dados socioeconômicos e acadêmicos dos estudantes matriculados nos cursos superiores de tecnologia do Instituto Federal de Mato Grosso - IFMT. Os dados que compuseram os vetores de entrada do sistema foram coletados a partir de dois bancos de dados do sistema de informação do IFMT, respectivamente, o Q-seleção e o Q-Acadêmico. Este sistema faz a classificação dos padrões de entrada em propensos...
School dropout is one of the most complex and crucial problems in the field of education. It permeates and afflicts the several levels and teaching modalities, besides hurting the principle of human dignity. In relation to higher education, internationally, the phenomenon is an object of attention and care, aiming to increase the indexes of permanence and completion rate of the undergraduate students and minimize social, economic, political and financial damage caused to all involved in the educational process. In this context, it is fundamental to develop efficient and effective methods and instruments for prediction, assessment and monitoring of the students at risk of dropping out, making the planning and the adoption of proactive actions possible for the improvement of the situation. Thus, this study aims to present the potentialities of an intelligent system able to identify, in a proactive, continued and accurate way, the student dropout risk group in higher education classroom courses. In the development of this system one of the artificial intelligence techniques was used, the Artificial Neural Networks, more specifically, the Fuzzy-ARTMAP Neural network, a neural network of the ART (Adaptive Resonance Theory) family which makes the continued learning of the system possible. For the training and test of the Neural Network and, later, the validation of the system proposed the socio-economic and academic records of the students enrolled in the technology courses of the Federal Institute of Mato Grosso – IFMT were used. The data that constituted the input vectors of the system were extracted from two database of the IFMT information system, respectively, the Q-selection and the Q-Academic. This system classifies the input patterns in school dropout propensity. The consistence of the results, showing a success rate of the dropout group around 95% and 100% and the overall mean accuracy around ...
School dropout is one of the most complex and crucial problems in the field of education. It permeates and afflicts the several levels and teaching modalities, besides hurting the principle of human dignity. In relation to higher education, internationally, the phenomenon is an object of attention and care, aiming to increase the indexes of permanence and completion rate of the undergraduate students and minimize social, economic, political and financial damage caused to all involved in the educational process. In this context, it is fundamental to develop efficient and effective methods and instruments for prediction, assessment and monitoring of the students at risk of dropping out, making the planning and the adoption of proactive actions possible for the improvement of the situation. Thus, this study aims to present the potentialities of an intelligent system able to identify, in a proactive, continued and accurate way, the student dropout risk group in higher education classroom courses. In the development of this system one of the artificial intelligence techniques was used, the Artificial Neural Networks, more specifically, the Fuzzy-ARTMAP Neural network, a neural network of the ART (Adaptive Resonance Theory) family which makes the continued learning of the system possible. For the training and test of the Neural Network and, later, the validation of the system proposed the socio-economic and academic records of the students enrolled in the technology courses of the Federal Institute of Mato Grosso – IFMT were used. The data that constituted the input vectors of the system were extracted from two database of the IFMT information system, respectively, the Q-selection and the Q-Academic. This system classifies the input patterns in school dropout propensity. The consistence of the results, showing a success rate of the dropout group around 95% and 100% and the overall mean accuracy around ...
Descrição
Palavras-chave
Sistemas inteligentes de controle, Redes neurais (Computação), Evasão universitaria, Intelligent control systems
Como citar
MARTINHO, Valquíria Ribeiro de Carvalho. Sistema inteligente para a predição de grupo de risco de evasão discente. 2014. 145 f. Tese (doutorado) - Universidade Estadual Paulista Júlio de Mesquita Filho, Faculdade de Engenharia de Ilha Solteira, 2014.