Estratégias para Extração de Padrões Sequenciais no Ensino de Algoritmos: Uma Comparação entre Algoritmos Clássicos e Metaheurísticas Evolutivas
DOI:
https://doi.org/10.5753/rbie.2025.5119Keywords:
Mineração de Padrões Sequenciais, Metaheurísticas Evolutivas, Personalização do Ensino de ProgramaçãoAbstract
A descoberta de padrões sequenciais no comportamento dos estudantes em ambientes de ensino de programação online é essencial para a personalização do aprendizado e otimização do processo educacional. Com a transformação digital no ensino, plataformas voltadas ao aprendizado de algoritmos ganharam destaque, mas ainda carecem de um sequenciamento personalizado de atividades que auxilie na progressão educacional. Neste cenário, a aplicação da Mineração de Padrões Sequenciais (MPS) pode revelar padrões de aprendizagem e desafios recorrentes enfrentados pelos alunos, tornando-se uma ferramenta valiosa para personalização da experiência de aprendizagem. Assim, o presente estudo compara duas abordagens de MPS: algoritmos clássicos e metaheurísticas evolutivas, para avaliar suas eficácias na detecção de padrões. A pesquisa analisa as interações de 313 alunos em um ambiente de ensino de algoritmos, observando o desempenho das abordagens quanto à precisão, custo computacional e aplicabilidade. Os algoritmos clássicos mostram-se eficientes em bases menores, enquanto as metaheurísticas evolutivas revelam padrões complexos com maior eficácia em grandes volumes de dados. Os resultados indicam que ambas as abordagens podem beneficiar o ambiente de ensino, ajudando o professor a antecipar sinais de desmotivação e intervir de forma proativa para manter o aluno interessado e ativo na plataforma. Como contribuição, demonstra-se que, tanto os algoritmos clássicos quanto as metaheurísticas evolutivas, podem gerar insights valiosos, como a necessidade de revisar um problema específico ou fornecer mais exemplos práticos aos alunos.
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