Abstract
We describe our approach to the computation and visual representation of the learning dynamics of a Massive Open Online Course (MOOC), where the educational strategy of Peer Assessment is used. The state of the MOOC, at a point in time, is representable through the student models and the relationships and data produced during the Peer Assessment. Such representation is rendered through a Graph Embedding approach, supported by Principal Component Analysis, as a point in a 2-dimensional space. The evolution of the MOOC, during a series of Peer Assessment sessions, is then representable as the path of the points where the MOOC status has been. Basing on a simulated MOOC, with 1000 students, modeled by a normal distribution of the student model features, we show that the proposed representation can picture effectively the evolution of the MOOC in time.
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Botticelli, M., Gasparetti, F., Sciarrone, F., Temperini, M. (2022). Deep Learning to Monitor Massive Open Online Courses Dynamics. In: De la Prieta, F., et al. Methodologies and Intelligent Systems for Technology Enhanced Learning, 11th International Conference. MIS4TEL 2021. Lecture Notes in Networks and Systems, vol 326. Springer, Cham. https://doi.org/10.1007/978-3-030-86618-1_12
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DOI: https://doi.org/10.1007/978-3-030-86618-1_12
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