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.
Botticelli, M., Gasparetti, F., Sciarrone, F., & Temperini, M. (2022). Deep Learning to Monitor Massive Open Online Courses Dynamics. In Lecture Notes in Networks and Systems (pp.114-123). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-86618-1_12].
Titolo: | Deep Learning to Monitor Massive Open Online Courses Dynamics | |
Autori: | ||
Data di pubblicazione: | 2022 | |
Serie: | ||
Citazione: | Botticelli, M., Gasparetti, F., Sciarrone, F., & Temperini, M. (2022). Deep Learning to Monitor Massive Open Online Courses Dynamics. In Lecture Notes in Networks and Systems (pp.114-123). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-86618-1_12]. | |
Handle: | http://hdl.handle.net/11590/402043 | |
ISBN: | 978-3-030-86617-4 | |
Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |