RT info:eu-repo/semantics/article
T1 Clustering of LMS Use Strategies with Autoencoders
A1 Verdú Pérez, María Jesús
A1 Regueras Santos, Luisa María
A1 Castro Fernández, Juan Pablo de
A1 Verdú Pérez, Elena
K1 autoencoders; clustering; deep learning; educational data mining; learning management system; unsupervised learning
AB Learning Management Systems provide teachers with many functionalities to offer materials to students, interact with them and manage their courses. Recognizing teachers’ instructing styles from their course designs would allow recommendations and best practices to be made. We propose a method that determines teaching style in an unsupervised way from the course structure and use patterns. We define a course classification approach based on deep learning and clustering. We first use an autoencoder to reduce the dimensionality of the input data, while extracting the most important characteristics; thus, we obtain a latent representation of the courses. We then apply clustering techniques to the latent data to group courses based on their use patterns. The results show that this technique improves the clustering performance while avoiding the manual data pre-processing work. Furthermore, the obtained model defines seven course typologies that are clearly related to different use patterns of Learning Management Systems.
PB Dimitris Mourtzis
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/65510
UL https://uvadoc.uva.es/handle/10324/65510
LA eng
NO Verdú, M.J.; Regueras, L.M.; de Castro, J.P.; Verdú, E. Clustering of LMS Use Strategies with Autoencoders. Appl. Sci. 2023, 13, 7334. https://doi.org/10.3390/app13127334
DS UVaDOC
RD 12-jul-2024