# Clustering of LMS Use Strategies with Autoencoders
## 1 Introduction
Autoencoders have been shown to be better than PCA as a tool for dimensionality reduction [15; 16; 17], although they may involve more computation time and resources than PCA [18]. In fact, autoencoders have also been shown to be better than PCA as a tool for capturing the natural data structure in clustering and obtaining well-defined clustering structures [19]. Other studies have also shown that dimensionality reduction through deep neural networks allows the use of more scalable classification systems while maintaining or improving the accuracy [20; 21]. Autoencoders are being used in different fields. For example, Deep Convolutional Autoencoders are used for the unsupervised clustering of seismic data [7], obtaining precisions comparable to those achieved by supervised methods but without the need for labeled data, manual feature engineering and large training sets. In addition, variational autoencoders are used to learn a latent data representation that captures the natural clustering of bank customers' data according to creditworthiness [19]. In addition, autoencoders are successfully used for dimensionality reduction prior to applying k-means clustering to  
Figure 1: General Autoencoder architecture. functional magnetic resonance imaging data [22]. In the literature, there are many other examples of a successful unsupervised approach with autoencoders [23; 24; 25; 26]. In the field of learning analytics, neural networks have mainly been used in supervised problems for predicting students' performance, dropout rates and mood [27; 28; 29; 30; 31; 32; 33], as well as for classifying students according to a well-known learning style model [1; 34]. However, to the best of our knowledge, there is no previous work on latent representations of LMS courses using autoencoders or any other deep learning architecture. We first use an autoencoder that learns the latent representation of LMS courses (defined as the course structure together with the users' interactions). We then apply the latent data to clustering techniques to group courses based on their use patterns. That is, the autoencoder extracts features automatically from input course data and reduces the features' dimensions. The new dimension-reduced data are used for clustering instead of the original data, with the hypothesis of improving clustering performance compared to that obtained with manual preprocessing [11]. Therefore, in the problem of modelling course typologies according to LMS use, we aim to answer to the following research questions:  
* RQ1: Is it possible to avoid the manual data pre-processing work? * RQ2: Is it possible to improve the clustering performance by reducing dimensionality using deep learning instead of manually transforming data? * RQ3: Do we obtain a well-defined clustering structure when we start from the latent space? The remainder of this paper is organized as follows. In the next section, we describe the context and the data mining process implemented in this study, along with the methods used. Next, the results of this study and their analyses are presented. Finally, the last section contains the outcomes and insights regarding future work.