Descoberta de padrões sequenciais de aprendizagem em um ambiente voltado ao ensino de algoritmos
Abstract
This paper qualitatively analyzes the use of Sequential Pattern Mining to discover learning patterns in an environment dedicated to teaching algorithms. The question to be faced is how to improve the students’ learning experience through the investigation of navigation patterns by the computational problems available in this environment. The proposed approach is able to reveal to the teacher how the student’s learning experience is going, so that he can act to prevent a possible demotivation towards the platform. The main contribution of this work was to show that this approach can generate valuable information for the teacher, such as the need to review a problem or to provide more examples for students
References
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Ayres, J., Flannick, J., Gehrke, J., and Yiu, T. (2002). Sequential pattern mining using a bitmap representation. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 429–435.
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.
Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., and Hsu, M.-C. (2000). Freespan: frequent pattern-projected sequential pattern mining. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 355–359.
Han, J., Pei, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., and Hsu, M. (2001). Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In proceedings of the 17th international conference on data engineering, pages 215–224. IEEE.
Kang, J., Liu, M., and Qu, W. (2017). Using gameplay data to examine learning behavior patterns in a serious game. Computers in Human Behavior, 72:757–770.
Liu, T. and Israel, M. (2022). Uncovering students’ problem-solving processes in gamebased learning environments. Computers & Education, 182:104462.
Lo, D., Khoo, S.-C., and Liu, C. (2008). Efficient mining of recurrent rules from a sequence database. In Database Systems for Advanced Applications: 13th International Conference, DASFAA 2008, New Delhi, India, March 19-21, 2008. Proceedings 13, pages 67–83. Springer.
Mirzaei, M. and Sahebi, S. (2019). Modeling students’ behavior using sequential patterns to predict their performance. In Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II 20, pages 350–353. Springer.
Molenaar, I. and Wise, A. F. (2022). Temporal aspects of learning analytics–grounding analyses in concepts of time. The handbook of learning analytics, pages 66–76.
Slim, A., Heileman, G. L., Al-Doroubi, W., and Abdallah, C. T. (2016). The impact of course enrollment sequences on student success. In 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), pages 59–65. IEEE.
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.
Yang, Z., Wang, Y., and Kitsuregawa, M. (2007). Lapin: effective sequential pattern mining algorithms by last position induction for dense databases. In 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, pages 1020–1023. 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.
Zheng, J., Li, S., and Lajoie, S. P. (2022). Diagnosing virtual patients in a technology-rich learning environment: A sequential mining of students’ efficiency and behavioral patterns. Education and Information Technologies, pages 1–17.
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.
Ayres, J., Flannick, J., Gehrke, J., and Yiu, T. (2002). Sequential pattern mining using a bitmap representation. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 429–435.
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.
Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., and Hsu, M.-C. (2000). Freespan: frequent pattern-projected sequential pattern mining. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 355–359.
Han, J., Pei, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., and Hsu, M. (2001). Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In proceedings of the 17th international conference on data engineering, pages 215–224. IEEE.
Kang, J., Liu, M., and Qu, W. (2017). Using gameplay data to examine learning behavior patterns in a serious game. Computers in Human Behavior, 72:757–770.
Liu, T. and Israel, M. (2022). Uncovering students’ problem-solving processes in gamebased learning environments. Computers & Education, 182:104462.
Lo, D., Khoo, S.-C., and Liu, C. (2008). Efficient mining of recurrent rules from a sequence database. In Database Systems for Advanced Applications: 13th International Conference, DASFAA 2008, New Delhi, India, March 19-21, 2008. Proceedings 13, pages 67–83. Springer.
Mirzaei, M. and Sahebi, S. (2019). Modeling students’ behavior using sequential patterns to predict their performance. In Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II 20, pages 350–353. Springer.
Molenaar, I. and Wise, A. F. (2022). Temporal aspects of learning analytics–grounding analyses in concepts of time. The handbook of learning analytics, pages 66–76.
Slim, A., Heileman, G. L., Al-Doroubi, W., and Abdallah, C. T. (2016). The impact of course enrollment sequences on student success. In 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), pages 59–65. IEEE.
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.
Yang, Z., Wang, Y., and Kitsuregawa, M. (2007). Lapin: effective sequential pattern mining algorithms by last position induction for dense databases. In 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, pages 1020–1023. 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.
Zheng, J., Li, S., and Lajoie, S. P. (2022). Diagnosing virtual patients in a technology-rich learning environment: A sequential mining of students’ efficiency and behavioral patterns. Education and Information Technologies, pages 1–17.
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.
Published
2023-11-06
How to Cite
MARANHÃO, Djefferson; BORGES, Paulo Victor; NETO, Carlos de Salles Soares.
Descoberta de padrões sequenciais de aprendizagem em um ambiente voltado ao ensino de algoritmos. In: BRAZILIAN SYMPOSIUM ON COMPUTERS IN EDUCATION (SBIE), 34. , 2023, Passo Fundo/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 1385-1396.
DOI: https://doi.org/10.5753/sbie.2023.235102.
