Abstract
Online learning environments generate educational data that can be used to model students’ behavior and predict their performance. In online learning environments, in which students are free to choose their next activity, various factors such as time spent on individual tasks and the choice of next learning material may impact students’ performance. The main goal of this research is to enhance student learning by modeling students’ behavior and testing whether these behavioral patterns correlate with their performance. Using sequential pattern mining methods, we will identify the most frequent patterns in students’ online learning activities and test whether/which patterns correlate with higher or lower performance. By identifying which student behavioral patterns correlate with higher or lower performance, this study has the potential to inform redesign of online learning platforms and study guidelines that help students learn more and perform better.
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References
Martinez, R., Yacef, K., Kay, J., Al-Qaraghuli, A., Kharrufa, A.: Analysing frequent sequential patterns of collaborative learning activity around an interactive tabletop. In: Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011), pp. 111–120 (2011)
Guerra, J., Sahebi, S., Brusilovsky, P., Lin, Y.R.: The problem solving genome: analyzing sequential patterns of student work with parameterized exercises. In: Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), pp. 153–160 (2014)
Fournier-Viger, P., Gomariz, A., Campos, M., Thomas, R.: Fast vertical mining of sequential patterns using co-occurrence information. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014. LNCS (LNAI), vol. 8443, pp. 40–52. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06608-0_4
Kim, H., Choo, J., Kim, J., Reddy, C.K., Park, H.: Simultaneous discovery of common and discriminative topics via joint non-negative matrix factorization. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 15), pp. 567–576 (2015)
Badea, L.: Extracting gene expression profiles common to colon and pancreatic adenocarcinoma using simultaneous nonnegative matrix factorization. In: Pacific Symposium on Biocomputing, pp. 267–278 (2008)
Doan, T., Lim, E.: Modeling location-based social network data with area attraction and neighborhood competition. Data Min. Knowl. Disc. 33, 58–95 (2019)
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Mirzaei, M., Sahebi, S. (2019). Modeling Students’ Behavior Using Sequential Patterns to Predict Their Performance. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11626. Springer, Cham. https://doi.org/10.1007/978-3-030-23207-8_64
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DOI: https://doi.org/10.1007/978-3-030-23207-8_64
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