Strategies for Extracting Sequential Learning Patterns in the Teaching of Algorithms: A Comparison between Classical Algorithms and Evolutionary Metaheuristics
DOI:
https://doi.org/10.5753/rbie.2025.5119Keywords:
Sequential Pattern Mining, Evolutionary Metaheuristics, Personalization of Programming EducationAbstract
Discovering sequential patterns in student behavior in online programming teaching environments is essential for personalizing learning and optimizing the educational process. With the digital transformation in education, platforms focused on algorithm learning have gained prominence but still lack personalized sequencing of activities to assist in academic progression. In this scenario, the application of Sequential Pattern Mining (SPM) can reveal learning patterns and recurring challenges students face, becoming a valuable tool for personalizing the learning experience. Thus, the present study compares two SPM approaches: classical algorithms and evolutionary metaheuristics, to evaluate their effectiveness in detecting patterns. The research analyzes the interactions of 313 students in an algorithm teaching environment, observing the performance of the approaches in terms of accuracy, computational cost, and applicability. Classical algorithms prove efficient in smaller datasets, while evolutionary metaheuristics reveal complex patterns more effectively in large volumes of data. The results indicate that both approaches can benefit the teaching environment, helping the teacher anticipate signs of demotivation and intervene proactively to keep the student interested and active on the platform. As a contribution, it is demonstrated that both classical algorithms and evolutionary metaheuristics can generate valuable insights, such as the need to review a specific problem or provide more practical examples to students.
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References
Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. Proceedings of the eleventh international conference on data engineering, 3–14. https://doi.org/10.1109/ICDE.1995.380415 [GS Search]
Almeida, D. J., Fernandes, M. A., & da Costa, N. T. (2022). Sequencing and Recommending Pedagogical Activities from Bloom’s Taxonomy using RASI and Multi-objective PSO. Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU, 105–116. https://doi.org/10.5220/0011090000003182 [GS Search]
Ayres, J., Flannick, J., Gehrke, J., & Yiu, T. (2002). Sequential pattern mining using a bitmap representation. Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 429–435. https://doi.org/10.1145/775047.775109 [GS Search]
Bhaskar, M., Das, M. M., Chithralekha, T., & 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. [GS Search]
Chiu, D.-Y., Wu, Y. -H., & Chen, A. L. (2004). An efficient algorithm for mining frequent sequences by a new strategy without support counting. Proceedings. 20th International Conference on Data Engineering, 375–386. https://doi.org/10.1109/ICDE.2004.1320012 [GS Search]
Dorigo, M. (1992). Optimization, Learning and Natural Algorithms [PhD Thesis]. Politecnico di Milano. [GS Search]
Eberhart, R., & Kennedy, J. (1995). A New Optimizer Using Particle Swarm Theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 39–43. https://doi.org/10.1109/MHS.1995.494215 [GS Search]
Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C. -W., Tseng, V. S., et al. (2014). Spmf: a java open-source pattern mining library. J. Mach. Learn. Res., 15(1), 3389–3393. https://doi.org/10.5555/2627435.2750353 [GS Search]
Geem, Z., Kim, J., & et al. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2), 60–68. https://doi.org/10.1177/003754970107600201 [GS Search]
Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., & Hsu, M. -C. (2000). FreeSpan: frequent pattern-projected sequential pattern mining. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 355–359. https://doi.org/10.1145/347090.347167 [GS Search]
Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM sigmod record, 29(2), 1–12. https://doi.org/10.1145/335191.335372 [GS Search]
Hnida, M., Idrissi, M. K., & Bennani, S. (2016). Adaptive teaching learning sequence based on instructional design and evolutionary computation. 2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET), 1–6. https://doi.org/10.1109/ITHET.2016.7760739 [GS Search]
Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press. https://doi.org/10.7551/mitpress/1090.001.0001 [GS Search]
Kang, J., Liu, M., & Qu, W. (2017). Using gameplay data to examine learning behavior patterns in a serious game. Computers in Human Behavior, 72, 757–770. https://doi.org/10.1016/j.chb.2016.09.062 [GS Search]
Li, J.-W., Chang, Y. -C., Chu, C.-P., & Tsai, C. -C. (2012). A self-adjusting e-course generation process for personalized learning. Expert Systems with Applications, 39(3), 3223–3232. https://doi.org/https://doi.org/10.1016/j.eswa.2011.09.009 [GS Search]
Machado, M. d. O. C., Barrére, E., & Souza, J. (2019). Solving the Adaptive Curriculum Sequencing Problem with Prey-Predator Algorithm. International Journal of Distance Education Technologies (IJDET), 17(4), 71–93. https://doi.org/10.4018/IJDET.2019100105 [GS Search]
Maranhão, D., Borges, P., & Neto, C. (2023). Descoberta de padrões sequenciais de aprendizagem em um ambiente voltado ao ensino de algoritmos. Anais do XXXIV Simpósio Brasileiro de Informática na Educação, 1385–1396. https://doi.org/10.5753/sbie.2023.235102 [GS Search]
Maranhão, D., & Neto, C. S. (2024). Aplicação de Metaheurísticas Evolutivas na Mineração de Padrões Sequenciais de Aprendizagem em Ambientes de Ensino de Algoritmos. Anais do XXXV Simpósio Brasileiro de Informática na Educação, 1837–1850. https://doi.org/10.5753/sbie.2024.242581 [GS Search]
Martins, A. F., Machado, M., Bernardino, H. S., & de Souza, J. F. (2021). A comparative analysis of metaheuristics applied to adaptive curriculum sequencing. Soft Computing, 25(16), 11019–11034. https://doi.org/10.1007/s00500-021-05836-9 [GS Search]
Mirzaei, M., & Sahebi, S. (2019). Modeling students’ behavior using sequential patterns to predict their performance. Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II 20, 350–353. https://doi.org/10.1007/978-3-030-23207-8_64 [GS Search]
Parpinelli, R. S., & Lopes, H. S. (2011). New inspirations in swarm intelligence: a survey. International Journal of Bio-Inspired Computation (IJBIC), 3, 1–16. https://doi.org/10.1504/IJBIC.2011.038700 [GS Search]
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., & Hsu, M. -C. (2001). PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. Proceedings of the 17th International Conference on Data Engineering (ICDE’01), 215–224. https://doi.org/10.1109/ICDE.2001.914830 [GS Search]
Sharma, R., Banati, H., & Bedi, P. (2012). Adaptive content sequencing for e-learning courses using ant colony optimization. Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011, 579–590. https://doi.org/10.1007/978-81-322-0491-6_53 [GS Search]
Slim, A., Heileman, G. L., Al-Doroubi, W., & Abdallah, C. T. (2016). The impact of course enrollment sequences on student success. 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), 59–65. https://doi.org/10.1109/AINA.2016.140 [GS Search]
Srikant, R., & Agrawal, R. (1996). Mining sequential patterns: Generalizations and performance improvements. Advances in Database Technology—EDBT’96: 5th International Conference on Extending Database Technology Avignon, France, March 25–29, 1996 Proceedings 5, 1–17. https://doi.org/10.1007/BFb0014140 [GS Search]
Yang, Z., Wang, Y., & Kitsuregawa, M. (2007). LAPIN: effective sequential pattern mining algorithms by last position induction for dense databases. Advances in Databases: Concepts, Systems and Applications: 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007, Bangkok, Thailand, April 9-12, 2007. Proceedings 12, 1020–1023. https://doi.org/10.1007/978-3-540-71703-4_95 [GS Search]
Zaki, M. J. (2000). Sequence mining in categorical domains: incorporating constraints. Proceedings of the Ninth International Conference on Information and Knowledge Management, 422–429. https://doi.org/10.1145/354756.354849 [GS Search]
Zaki, M. J. (2001). SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning, 42(1-2), 31–60. https://doi.org/10.1023/A:1007652502315 [GS Search]
Zhang, Y., & Paquette, L. (2023). Sequential pattern mining in educational data: The application context, potential, strengths, and limitations. Em Educational Data Science: Essentials, Approaches, and Tendencies: Proactive Education based on Empirical Big Data Evidence (pp. 219–254). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-0026-8_6 [GS Search]
Zhou, M., Xu, Y., Nesbit, J. C., & Winne, P. H. (2010). Sequential pattern analysis of learning logs: Methodology and applications. Handbook of educational data mining, 107, 107–121. https://doi.org/10.1201/b10274-10. [GS Search]
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