Abstract
Personalised or intelligent tutoring systems are being rapidly adopted because they enable tailored learner choices in, for example, exercise materials, study time, and intensity (i.e., the number of chosen exercises) over extended periods of time. This, however, poses significant challenges for profiling the characteristics of learner behaviors, mostly due to the great diversity in each individual’s learning path, the timing of exercise accomplishments, and varying degrees of engagement over time. To address this problem, this paper proposes an innovative approach that uses self-supervised deep learning to consolidate learner behaviors and performance into compact representations via irregular multivariate time series modeling. These representations can be used to highlight learners’ multi-dimensional behavioral characteristics on a massive scale for self-directed learners who can freely pick exercises and study at their own pace. With experiments on a large-scale real-world dataset, we empirically show that our approach can effectively reveal learner individuality as well as commonality in characteristics.
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References
Alexander, P.A., Murphy, P.K.: Profiling the differences in students’ knowledge, interest, and strategic processing. J. Educ. Psychol. 90(3), 435 (1998)
Ben Soussia, A., Roussanaly, A., Boyer, A.: An in-depth methodology to predict at-risk learners. In: De Laet, T., Klemke, R., Alario-Hoyos, C., Hilliger, I., Ortega-Arranz, A. (eds.) EC-TEL 2021. LNCS, vol. 12884, pp. 193–206. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86436-1_15
Ben Soussia, A., Treuillier, C., Roussanaly, A., Boyer, A.: Learning profiles to assess educational prediction systems. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education (AIED 2022). LNCS, vol. 13355, pp. 41–52. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-11644-5_4
Choi, Y., et al.: EdNet: a large-scale hierarchical dataset in education. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 69–73. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_13
Marras, M., Vignoud, J.T.T., Kaser, T.: Can feature predictive power generalize? Benchmarking early predictors of student success across flipped and online courses. In: 14th International Conference on Educational Data Mining, pp. 150–160 (2021)
Mejia-Domenzain, P., Marras, M., Giang, C., Käser, T.: Identifying and comparing multi-dimensional student profiles across flipped classrooms. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds.) Artificial Intelligence in Education (AIED 2022). LNCS, vol. 13355, pp. 90–102. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-11644-5_8
Mojarad, S., Essa, A., Mojarad, S., Baker, R.S.: Data-driven learner profiling based on clustering student behaviors: learning consistency, pace and effort. In: Nkambou, R., Azevedo, R., Vassileva, J. (eds.) ITS 2018. LNCS, vol. 10858, pp. 130–139. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91464-0_13
Wang, P., Fu, Y., Xiong, H., Li, X.: Adversarial substructured representation learning for mobile user profiling. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 130–138 (2019)
Acknowledgments
This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the HUMAN+ COFUND Marie Skłodowska-Curie grant agreement No. 945447.
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Xiao, Q., Pitt, B., Johnston, K., Wade, V. (2023). Multi-dimensional Learner Profiling by Modeling Irregular Multivariate Time Series with Self-supervised Deep Learning. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2023. Lecture Notes in Computer Science(), vol 13916. Springer, Cham. https://doi.org/10.1007/978-3-031-36272-9_55
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DOI: https://doi.org/10.1007/978-3-031-36272-9_55
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