# Clustering of LMS Use Strategies with Autoencoders
## 1 Introduction  
Learning Management Systems (LMS) are increasingly being integrated into traditional universities as a complement to face-to-face teaching. These systems provide teachers with many resources and utilities to offer materials to students, interact with them, and manage their courses. In the same way that some researchers have aimed to detect students' learning profiles from their behavior to provide appropriate recommendations and improve academic achievement [1; 2], detecting teachers' instructing styles from their course designs would allow for recommendations and best practices to be provided. However, a literature review suggests that a research gap exists in the fields of learning analytics and educational data mining from the perspective of teachers [3; 4; 5]. With the assistance of an intelligent LMS that analyses teaching style from course structure and LMS use patterns, teachers could improve the design of their courses [5]. This improvement is expected to directly impact the learning process and students' outcomes and satisfaction [6]. On the other hand, many national teacher accreditation systems are based on teaching quality programs that evaluate competence in technology-based learning. Therefore, universities need to certify the use of LMSs by teachers. Currently, this task is carried out manually by experts or with overly basic metrics based on the presence or absence of LMS activity. It would be interesting to be able to automatize this certification process. We see the process of defining teaching styles from LMS data as an unsupervised problem. Supervised methods require a significant quantity of well-labeled training datasets created by significant manual feature engineering efforts [7]. It is very hard to obtain labeled data in this context. In addition, as there is not a generally accepted model ofonline teaching styles [8; 9], it is unfeasible for experts to assign meaningful labels to online courses [10]. Even unsupervised methods require some important manual data engineering in the phase of data pre-processing and transformation. In our previous work on LMS course clustering [11], we lost information in the processes of reducing the dimensionality and discretizing the input features into only three bins (low, medium and high). We grouped in the same bin values that could have fuzzy limits. In the process of reducing dimensionality, the discarding of some dimensions inevitably led to the loss of information. Thus, the primary problem to be solved is how to obtain a low-dimensional model while keeping the most important characteristics of the original data. Motivated by these limitations, as well as by recent work on deep learning techniques, we propose a course classification approach based on deep learning and clustering. Deep neural networks outperform linear models in many tasks. For unsupervised learning, one recent approach is the use of autoencoders, which provides an informative representation of the data that can be used, for example, for effective clustering [7; 12]. Autoencoders are a type of neural network where the input is the same as the output [13]. They are an unsupervised learning technique. Thus, they can be trained with unlabeled data. The autoencoders have two main components: an encoder and a decoder (see Figure 1). The output of the encoder is a latent (or semantic) representation of the input, usually of a lower dimension. The decoder can reconstruct the input using that lower-dimensional representation. Then, autoencoders allow for the dimensions of the input data to be reduced, while extracting their main characteristics. Typical applications of autoencoders include dimensionality reduction [13] and feature extraction [12; 14]. Traditional ways to reduce the dimensionality of large datasets are removing variables with a high correlation and/or a high number of missing values, as well as using PCA (Principal Components Analysis).