Clustering of LMS Use Strategies with Autoencoders
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 d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/12/7334 |
| _version_ | 1797596222087757824 |
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| author | María J. Verdú Luisa M. Regueras Juan P. de Castro Elena Verdú |
| author_facet | María J. Verdú Luisa M. Regueras Juan P. de Castro Elena Verdú |
| author_sort | María J. Verdú |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T02:47:31Z |
| format | Article |
| id | doaj.art-35406bdcd02049b594c3dc2f2daa08bf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T02:47:31Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-35406bdcd02049b594c3dc2f2daa08bf2023-11-18T09:12:22ZengMDPI AGApplied Sciences2076-34172023-06-011312733410.3390/app13127334Clustering of LMS Use Strategies with AutoencodersMaría J. Verdú0Luisa M. Regueras1Juan P. de Castro2Elena Verdú3Higher Technical School of Telecommunications Engineering (ETSIT), Universidad de Valladolid, 47011 Valladolid, SpainHigher Technical School of Telecommunications Engineering (ETSIT), Universidad de Valladolid, 47011 Valladolid, SpainHigher Technical School of Telecommunications Engineering (ETSIT), Universidad de Valladolid, 47011 Valladolid, SpainEscuela Superior de Ingeniería y Tecnología, Universidad Internacional de La Rioja, 26006 Logroño, SpainLearning 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.https://www.mdpi.com/2076-3417/13/12/7334autoencodersclusteringdeep learningeducational data mininglearning management systemunsupervised learning |
| spellingShingle | María J. Verdú Luisa M. Regueras Juan P. de Castro Elena Verdú Clustering of LMS Use Strategies with Autoencoders Applied Sciences autoencoders clustering deep learning educational data mining learning management system unsupervised learning |
| title | Clustering of LMS Use Strategies with Autoencoders |
| title_full | Clustering of LMS Use Strategies with Autoencoders |
| title_fullStr | Clustering of LMS Use Strategies with Autoencoders |
| title_full_unstemmed | Clustering of LMS Use Strategies with Autoencoders |
| title_short | Clustering of LMS Use Strategies with Autoencoders |
| title_sort | clustering of lms use strategies with autoencoders |
| topic | autoencoders clustering deep learning educational data mining learning management system unsupervised learning |
| url | https://www.mdpi.com/2076-3417/13/12/7334 |
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