Predição de Sucesso Acadêmico de Estudantes: Uma Análise sobre a Demanda por uma Abordagem baseada em Transfer Learning
DOI:
https://doi.org/10.5753/rbie.2019.27.01.01Keywords:
Predição de Sucesso Acadêmico, Learning Analytics, Mineração de Dados Educacionais, Transfer Learning, Covariate ShifAbstract
Interações de estudantes com Ambientes Virtuais de Aprendizagem (AVA) geram logs que permitem reconstruir cada atividade realizada. A análise destes dados tem proporcionando uma melhor compreensão do comportamento de estudantes e dos processos de ensino e aprendizagem. Neste contexto, inúmeros trabalhos têm relatado resultados promissores na tarefa de predição de desempenho de estudantes, permitindo que ações proativas possam ser tomadas no sentido de evitar insucessos acadêmicos. Usualmente, técnicas de mineração de dados empregadas na construção de modelos preditivos utilizam registros históricos (passados) de dados, assumindo-se, desta forma, a premissa de que o preditor construído irá realizar predições em contextos futuros que sejam similares aos contextos (passados) que foram utilizados na sua concepção. Ainda que seja razoável assumir que a diversidade de contextos educacionais existentes se reflita nos dados gerados, poucos são os trabalhos que discutem o impacto de tal premissa na área de Mineração de Dados Educacionais (MDE), o que resulta em modelos que podem apresentar desempenho insatisfatório quando utilizados em condições educacionais não previstas. Este trabalho propõe uma análise empírica no sentido de verificar indícios de diferenças entre dados provenientes de contextos educacionais distintos na tarefa de predição de insucesso acadêmico de estudantes. Emprega-se dados de logs de mais de 3.000 estudantes de ensino superior na modalidade EAD. A metodologia adotada é baseada na própria abordagem de classificação supervisionada, comumente utilizada em tarefas de predição, sendo que busca-se, especificamente, verificar se contextos educacionais distintos são de fato separáveis em termos dos dados que geram. Ainda que o cenário de dados envolva atividades comuns a estudantes de uma mesma disciplina, os experimentos indicam uma acurácia de até 83% na separação de dados provenientes de períodos letivos distintos. Embora empíricos, os resultados indicam uma direção similar àquela apontada por outros trabalhos, contribuindo sobre a necessidade da utilização de técnicas de transfer learning e/ou adaptação de domínio no projeto dos modelos preditivos voltados a prevenção de insucessos acadêmicos.
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Derechos de autor 2019 Daniel A. Guimarães De Los Reyes, Everton André Thomas, Lilian Landvoigt da Rosa, Wilson P. Gavião Neto
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