# An e-Learning Toolbox Based on Rule-Based Fuzzy Approaches
## 1 Introduction
For instance, it is possible to obtain information about the students' accesses to the course, the number of accesses made by the students to the resources of the course, and a graph reporting the submission of assignments and quizzes [22]. In [23], learning analytics are applied for the early prediction of students' final academic performance in a calculus course delivered in a blended way. Principal component regression is used to forecast the students' performance, by using different kind of variables like: out-of-class practice behaviors, homework and quiz scores, video-viewing behaviors, and after-school tutoring. In [24], four classification methods: J48, PART, Random Forest and Bayes Network, are applied to analyze data from socio-economic, demographic and academic information in order to identify the student performance and to prevent drop out. The authors apply WEKA [25] as a data mining tool. [PERSON], et al. apply process-mining methods to analyze students' interactions when they take online quizzes. The objective is to identify specific types of interaction sequences that show the strategies used by students when they take quizzes [26]. In [27] an ensemble based tree model classifier is proposed for predicting students' performance. The features used to perform the forecasting are: exams marks, labs tests, class tests, students' demographics, education information, and family behaviors. An educational DM platform that extracts patterns by using association rule mining from educational datasets is presented in [28]. The platform also applies the clustering k-means algorithm to group the survey answers of the participants. [PERSON] et al. propose the use of DM techniques to help the student choose the most suitable career for him within the field of engineering. To perform these predictions, SVM, Random Forest, and Naive Bayes classifiers are applied. The features involved in the classification are taken from the admission process like demographic features, the profession of their parents and his/her previous high school and higher secondary records [29]. After analyzing the literature, we can see that the existing tools are specialized in some particular aspect of e-Learning, such as the interaction between students, the prediction of student performance, the grouping of students based on similar behaviors, etc. In this sense, the e-Learning toolbox presented in this work has the peculiarity and advantage over previous works that it integrates a set of useful functionalities for teachers, students, and modelers in a single platform. To the best of our knowledge, this is the first time that an e-Learning toolbox uses as DM approaches, methodologies based on fuzzy logic and fuzzy modeling. All the functionalities developed in the platform are based on fuzzy logic and on reasoning under uncertainty. We consider that the approach we propose in this article is novel and relevant in the area of e-Learning, since the information derived from the students' learning process invariably contains partial knowledge, uncertainty, and/or incomplete information. The use of fuzzy logic based methodologies allows to incorporate this uncertainty into the model. Fuzzy rule models enable to mimic the logic of human thought, are expressive, easy to understand and suitable for approximate reasoning. The works in e-Learning that are closest to our research are the approaches based on rule models, specifically the association rules [24; 28], and the random forest algorithms [30]. Association rules are not as expressive and easy to understand as fuzzy rules, due to the fact that the consequence of an association rule includes input features. In the case of random forests, they generate decision trees comparable with learning rules; however, they are not as expressive and interpretable for the user. The tool that we propose in this article offers different utilities that help teachers and provides useful information to the student.