Aplicação de Metaheurísticas Evolutivas na Mineração de Padrões Sequenciais de Aprendizagem em Ambientes de Ensino de Algoritmos
Resumo
O presente trabalho analisa qualitativamente o emprego de metaheurísticas evolutivas na mineração de padrões sequenciais de aprendizagem em um ambiente de ensino de algoritmos. A questão central é como proporcionar uma melhor experiência de aprendizagem aos alunos dessa plataforma mediante a mineração dos padrões de navegação pelo conteúdo disponibilizado nesse ambiente. A abordagem proposta permite ao professor compreender de forma mais ampla a experiência de aprendizagem e o grau de engajamento do aluno. Com esses dados, o professor pode antecipar sinais de desmotivação e intervir para manter o aluno interessado e ativo na plataforma. A principal contribuição deste trabalho é demonstrar que essa abordagem pode gerar insights valiosos, como a necessidade de revisar um problema específico ou fornecer mais exemplos práticos aos alunos.
Palavras-chave:
mineração de padrões sequenciais, metaheurísticas evolutivas, ensino de algoritmos
Referências
Agrawal, R. and Srikant, R. (1995). Mining sequential patterns. In Proceedings of the eleventh international conference on data engineering, pages 3–14. IEEE
Bhaskar, M., Das, M. M., Chithralekha, T., and Sivasatya, S. (2010). Genetic algorithm based adaptive learning scheme generation for context aware e-learning. International Journal on Computer Science and Engineering, 2(4):1271–1279.
Dorigo, M. (1992). Optimization, Learning and Natural Algorithms. Phd thesis, Politecnico di Milano, Milan, Italy.
Eberhart, R. and Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pages 39–43, Nagoya, Japan. IEEE.
Geem, Z., Kim, J., and et al. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2):60.
Hnida, M., Idrissi, M. K., and Bennani, S. (2016). Adaptive teaching learning sequence based on instructional design and evolutionary computation. In 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET), pages 1–6. IEEE.
Karimi-Mamaghan, M., Mohammadi, M., Meyer, P., Karimi-Mamaghan, A., and Talbi, E.-G. (2021). Machine learning at the service of metaheuristics for solving combinatorial optimization problems: A state-of-the-art. European Journal of Operational Research, 296(2):393–422.
Li, J.-W., Chang, Y.-C., Chu, C.-P., and Tsai, C.-C. (2012). A self-adjusting e-course generation process for personalized learning. Expert Systems with Applications, 39(3):3223–3232.
Parpinelli, R. S. and Lopes, H. S. (2011). New inspirations in swarm intelligence: a survey. International Journal of Bio-Inspired Computation, 3(1):1–16.
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., and Hsu, M.-C. (2004). Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In Proceedings of the 17th International Conference on Data Engineering (ICDE’01), pages 215–224. IEEE.
Pires, P. and Silva, J. (2016). Intelligent Systems: Concepts and Applications. Springer, Berlin.
Sharma, R., Banati, H., and Bedi, P. (2012). Adaptive content sequencing for e-learning courses using ant colony optimization. In Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011, pages 579–590. Springer.
Srikant, R. and Agrawal, R. (1996). Mining sequential patterns: Generalizations and performance improvements. In Advances in Database Technology—EDBT’96: 5th International Conference on Extending Database Technology Avignon, France, March 25–29, 1996 Proceedings 5, pages 1–17. Springer.
Zaki, M. J. (2000). Sequence mining in categorical domains: incorporating constraints. In Proceedings of the ninth international conference on Information and knowledge management, pages 422–429.
Zaki, M. J. (2001). Spade: An efficient algorithm for mining frequent sequences. Machine Learning, 42(1-2):31–60.
Zhang, Y. and Paquette, L. (2023). Sequential pattern mining in educational data: The application context, potential, strengths, and limitations. In Educational Data Science: Essentials, Approaches, and Tendencies: Proactive Education based on Empirical Big Data Evidence, pages 219–254. Springer.
Zhou, M., Xu, Y., Nesbit, J. C., and Winne, P. H. (2010). Sequential pattern analysis of learning logs: Methodology and applications. Handbook of educational data mining, 107:107–121.
Bhaskar, M., Das, M. M., Chithralekha, T., and Sivasatya, S. (2010). Genetic algorithm based adaptive learning scheme generation for context aware e-learning. International Journal on Computer Science and Engineering, 2(4):1271–1279.
Dorigo, M. (1992). Optimization, Learning and Natural Algorithms. Phd thesis, Politecnico di Milano, Milan, Italy.
Eberhart, R. and Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pages 39–43, Nagoya, Japan. IEEE.
Geem, Z., Kim, J., and et al. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2):60.
Hnida, M., Idrissi, M. K., and Bennani, S. (2016). Adaptive teaching learning sequence based on instructional design and evolutionary computation. In 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET), pages 1–6. IEEE.
Karimi-Mamaghan, M., Mohammadi, M., Meyer, P., Karimi-Mamaghan, A., and Talbi, E.-G. (2021). Machine learning at the service of metaheuristics for solving combinatorial optimization problems: A state-of-the-art. European Journal of Operational Research, 296(2):393–422.
Li, J.-W., Chang, Y.-C., Chu, C.-P., and Tsai, C.-C. (2012). A self-adjusting e-course generation process for personalized learning. Expert Systems with Applications, 39(3):3223–3232.
Parpinelli, R. S. and Lopes, H. S. (2011). New inspirations in swarm intelligence: a survey. International Journal of Bio-Inspired Computation, 3(1):1–16.
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., and Hsu, M.-C. (2004). Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In Proceedings of the 17th International Conference on Data Engineering (ICDE’01), pages 215–224. IEEE.
Pires, P. and Silva, J. (2016). Intelligent Systems: Concepts and Applications. Springer, Berlin.
Sharma, R., Banati, H., and Bedi, P. (2012). Adaptive content sequencing for e-learning courses using ant colony optimization. In Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011, pages 579–590. Springer.
Srikant, R. and Agrawal, R. (1996). Mining sequential patterns: Generalizations and performance improvements. In Advances in Database Technology—EDBT’96: 5th International Conference on Extending Database Technology Avignon, France, March 25–29, 1996 Proceedings 5, pages 1–17. Springer.
Zaki, M. J. (2000). Sequence mining in categorical domains: incorporating constraints. In Proceedings of the ninth international conference on Information and knowledge management, pages 422–429.
Zaki, M. J. (2001). Spade: An efficient algorithm for mining frequent sequences. Machine Learning, 42(1-2):31–60.
Zhang, Y. and Paquette, L. (2023). Sequential pattern mining in educational data: The application context, potential, strengths, and limitations. In Educational Data Science: Essentials, Approaches, and Tendencies: Proactive Education based on Empirical Big Data Evidence, pages 219–254. Springer.
Zhou, M., Xu, Y., Nesbit, J. C., and Winne, P. H. (2010). Sequential pattern analysis of learning logs: Methodology and applications. Handbook of educational data mining, 107:107–121.
Publicado
04/11/2024
Como Citar
MARANHÃO, Djefferson; SOARES NETO, Carlos de Salles.
Aplicação de Metaheurísticas Evolutivas na Mineração de Padrões Sequenciais de Aprendizagem em Ambientes de Ensino de Algoritmos. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 35. , 2024, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 1837-1850.
DOI: https://doi.org/10.5753/sbie.2024.242581.
