Abstract
Modern AIED systems develop sophisticated and multidimensional models of students. However, what is learned about students in one system—their skills, behaviors, and affect—is not carried over to other systems that could benefit students by using the information, potentially reducing both the effectiveness and efficiency of these systems. This challenge has been cited by a number of researchers as one of the most important for the field of AIED. In this paper, we discuss existing progress towards resolving this challenge, break down five sub-challenges, and propose how to address the sub-challenges.
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Baker, R.S. et al. (2021). Towards Sharing Student Models Across Learning Systems. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_10
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DOI: https://doi.org/10.1007/978-3-030-78270-2_10
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