# Monitoring Students at the University: Design and Application of a Moodle Plugin
## 1 Introduction
### Characteristics of the Learning Management System in the Detection of the At-Risk Student  
As with Moodle, LMS can incorporate different types of modules either to show simple resources, such as files, folders, books, pages, tags, etc., or more advanced modules, such as databases, chats,questionnaires, surveys, forums, glossaries, active areas, lessons, SCORM (Sharable Content Object Reference Model), packages, wikis, and workshops, etc. Various student interactions are collected from them all: the time at which the module is accessed, the viewing time, the time at which the access ends, and the modules that have and that have not been seen [35]. All this information can be downloaded in log files, which generate a Time Series Cross Section (TSCS). LMSs also include learning analytics systems, although most of these systems calculate only very simple metrics, which is why different investigations [35; 36; 37; 38] have pointed out the need to develop plugins, designed to increase the power of measurement, interpretation, and visualization in order to monitor student progress on the platform. For example, [PERSON] [35] used Excel for the analysis, visualization, and interpretation of the registered information. However, he concluded that Excel is not an ideal tool, as the number of rows and columns are a limitation for analysing student data. In turn, the studies by [PERSON] et al. [36] proposed the use of a Moodle plugin compatible with specific Educational Data Mining (EDM) techniques: supervised and unsupervised Machine Learning techniques [38; 39; 40; 41; 42]. These authors concluded that LMSs must integrate tools and plugins; not only simple learning analytics, but also EDM tools that permit the use of supervised learning techniques (prediction) [36; 37; 38] and unsupervised learning techniques (classification) that provide a user-friendly interface for the teacher, which facilitates their use throughout the course of study. These plugins will help with the detection of at-risk students [41] and the implementation of actions to avoid academic failure and high dropout rates [42]. In line with these conclusions, the studies by [PERSON] et al. [37] and [PERSON] et al. [42] placed special emphasis on the advantages of including a prediction module within the plugins in order to facilitate the academic monitoring of students. They also indicated that these plugins must be accompanied by visualization tools. The authors proposed their development through a Java SQL database server for processing the data with other interconnected tools, such as WEKA (Waikato Environment for Knowledge Analysis) [43], RapidMiner [44], Orange [45], and Knime [46], among others. Storage capacity problems can affect such plugins due to high data volumes that can influence their productivity levels. These problems can be solved using compression methods for higher storage capacity, thereby reducing the data-exchange time. In a study by [PERSON] et al. [47], the authors proposed to use a Hypertext Preprocessor programming language (PHP) using an algorithm implemented in a Moodle-based e-learning server. In summary, all these studies agreed that the extraction and the treatment of logs within LMSs are laborious and complex processes for the teacher. For this reason, LMSs must include plugins that offer teachers different functionalities for processing, interpreting, and visualizing the results. Within these plugins, the use of supervised and unsupervised learning techniques will be essential for monitoring students [48]. The plugins will facilitate an analysis of student learning patterns and their past grades, which will allow the teacher to offer a personalized educational response tailored to personal characteristics and needs throughout the learning process of each student on the degree course [48; 49]. Based on the studies presented above, the objectives of this work will be:  
1. To design a plugin for Moodle, known as \"eOrientation\", for the early detection of at-risk students. 2.