Application of evolutionary metaheuristics in sequential learning pattern mining in algorithm teaching environments

• Djefferson Maranhão Federal University of Maranhão
• Carlos de Salles Soares Neto Federal University of Maranhão

DOI: https://doi.org/10.5753/sbie.2024.242581

Abstract

The present work qualitatively analyzes the use of evolutionary metaheuristics in mining learning patterns in a virtual environment designed for teaching algorithms. The central question is how to provide a better learning experience to students on this platform by mining navigation patterns through the content available in this environment. The proposed approach enables educators to comprehensively understand students' learning experiences, providing valuable insights into their navigation patterns and level of student engagement. With this information, educators can promptly identify signs of demotivation and implement specific interventions to keep students interested and engaged on the platform. This study's main contribution demonstrates that this approach can yield valuable insights, such as the need to review a specific problem or provide more practical examples to students.

Keywords: sequential pattern mining, evolutionary metaheuristics, algorithm teaching

References

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.