# Using Virtual Learning Environment Data for the Development of Institutional Educational Policies
## 1 Introduction  
Universities have been concerned with using the extensive data produced by their educational systems in aiming to improve the overall performance of students [1, 2, 3, 4, 5, 6]. According to [7], the scope of contemporary higher education is vast, and concerns about the performance of higher education systems are widespread. Among several challenges that have been faced by universities, one can mention low completion rates, which are commonly associated with inefficiencies in higher education, even though they also depend on other factors, such as the student profiles and their paths to completion [5, 7, 8]. Data mining techniques can be used to overcome some of these challenges. Two specific areas are used to refer to the application of data mining in educational settings: Educational Data Mining (EDM) and Learning Analytics (LA) [9; 10]. EDM is an interdisciplinary research field that deals with the development of methods to explore data sourced from the educational context [11; 12]. LA seeks to measure, collect, analyze, and report data about students and their contexts to understand and optimize their learning and learning environment [13]. Student and teacher interactions within Virtual Learning Environments (VLEs) provide data that feed the research in these areas, thus, enabling the discovery of new knowledge [14]. Learning Management Systems (LMSs) and student information systems containing socio-demographic and student enrollment data can be considered the technological foundation for higher education institutions [15]. Modern educational systems use VLEs to support classroom activities, even in face-to-face courses. In these environments, it is possible to share materials, perform tasks, and interact with other users with the ultimate goal of generating and acquiring knowledge, both individually and collectively [14; 16; 17]. Modular Object-Oriented Dynamic Learning Environment (Moodle) is one of the most widely used VLEs worldwide. In Uruguay, there are 413 installation sites [18]. Data mining in higher education is mainly used for techniques, such as classification, clustering, and association rules as well as to predict, group, model, and monitor various learning activities [5; 9; 19]. Current studies on LA vary in several dimensions, covering, for instance, the techniques employed (data mining, visualization, social network analysis, and statistics), the source of the data (LMSs, surveys, and sensors), the stakeholders involved (students, professors, and administrators), and the educational level to which the systems/experiments are directed [20]. This work aims to unveil educational patterns of student interactions with the VLE in higher education courses that use Moodle as a complementary tool for teaching and learning processes. Hence, a series of data mining experiments are applied to the data from the VLE and also to data from other sources, such as surveys and academic systems. The experiments intend to better understand the VLE's role in helping students' education inside the studied courses and to discover educational patterns and knowledge that can further help in planning future actions and policies inside the institution. For the present work, we propose the following research questions (RQ):  
* RQ1: Is the use of VLE associated with student approval? * RQ2: Which features from the different datasets (VLE, census, and academic system) are the most important for the early prediction of student performance? * RQ3: Which learning patterns can educational data mining help to unveil in the studied courses? In this work, data mining was used as a tool to unveil educational knowledge and possible existing patterns related to the final status of the students. Even though we report quantitative results about predictive models, our main goal is to uncover these patterns to better understand the role that VLEs and other variables have in students' performance so that future educational policies can be built based on empirical findings.