# An e-Learning Toolbox Based on Rule-Based Fuzzy Approaches
## 1 Introduction  
E-Learning involves the use of electronic devices for learning, including the delivery of content via electronic media such as Internet/intranet/extranet, audio or video, satellite broadcast, interactive TV, and so on. E-Learning gives several advantages to students: cost effectiveness, timely content, and access flexibility [1; 2]. E-Learning systems, such as virtual campus environments, have gradually established themselves as a plausible alternative to, and a complement of, traditional distance education models. It is a helping and useful tool in the process of teaching and learning. However, experience has shown that the teacher considerably increases the workload in the virtual environment. They must carry out continuous feedback tasks to virtual students and correct a fairly high number of evaluable activities [3; 4; 5]. This issue has a greater relevance when we move from training to undergraduate and graduate courses, where competencies are closely related to higher thinking skills. By their very nature, timely and constant feedback is essential in these courses, increasing the workload of teachers. It should also be borne in mind that the assessment task that the teacher must carry out is arduous and takes a long time. Since collaborative resources are the mostused in distance education tools, the formula for calculating the final grade is made up of many factors that have little weight and hinder a clear and well-defined evaluation process. This topic is a relevant and challenging line of work in the area of e-Learning. The analysis, using data mining techniques (DM), of the data generated in the environment of e-Learning systems can be a good strategy to identify the most relevant evaluable activities, allowing to attack the correction overload problem from another less cumbersome prospect for the teacher. It is possible to find in the literature interesting works where different DM methods are developed or used to improve some particular aspect of e-Learning systems. In [6], an analysis of the state of the art on Educational Data Mining (EDM) in higher education is presented. The authors conclude that EDM discipline is growing and new contributions are needed to be more useful, fully operative, and available for all users. [PERSON] [7] analyzed a large amount of EDM research works in the literature and stated that the evolution of DM tools and their application is mainly oriented to facilitate data processing, simplify the feature selection stage, provide learning support to students, and enhance the collaboration between students and teacher. Some of the most popular Learning Management Systems (LMSs) are AlfaNet [8; 9], AHA! [10; 11], LON-CAPA [12; 13], ATutor [14], LExIKON [15], Blackboard [16], ELM-ART [17; 18] and Moodle [19]. From all of the previously mentioned LMSs, Moodle is probably the most known (and used) open source platform [19]. In recent years, several plugins have been developed for Moodle that make use of DM techniques to improve the e-Learning process. An example is Moodle's Engagement Analytics Plugin (MEAP+) [20], which analyses gradebook data, assessments submissions, and forum interactions to prepare personalized e-mails for students. MoodleMiner is another Moodle plugin, used to perform basic data mining analysis, such as grouping students by their levels of interaction, determining at-risk students, and identifying important features that impact student academic performance [21]. MoodleMiner applies classification algorithms to predict student academic performance and cluster analysis to group similar students. The Graphical Interactive Student Monitoring Tool for Moodle (GISMO) provides a visualization of student activities. GISMO generates graphical representations about the tracking behavior of the participants enrolled in a course.