The GSLB option provides, in a dynamic and incremental way, students’ clustering based on the prediction of their final marks. This option is accomplished by using the FIR methodology, described briefly in Section 2. For example, the teacher decides that a message is sent automatically at a certain moment of the course to the cluster of students who are not obtaining good results, telling them that they are not on the right track, and announcing a virtual class to clarify concepts and answer questions about the topics covered so far. The teacher decides the content of the automatic feedback and to which clusters of students should be applied. The platform offers teachers the possibility of sending automatic feedback to clusters of students, i.e., the set of students with failing grades predictions. That means that students’ learning behavior can be obtained and analyzed at any time through the course and after the end of it, giving teachers the possibility to offer efficient and on time feedback to students in order to improve their course performance. This is done by performing a prediction of the student’s final grade using a FIR model. The ASLP option provides a continuous evaluation of students during course development. 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. 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. The works in e-Learning that are closest to our research are the approaches based on rule models, specifically the association rules, and the random forest algorithms. Fuzzy rule models enable to mimic the logic of human thought, are expressive, easy to understand and suitable for approximate reasoning. The use of fuzzy logic based methodologies allows to incorporate this uncertainty into the model. 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. All the functionalities developed in the platform are based on fuzzy logic and on reasoning under uncertainty. 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. The results obtained by the functionalities offered by the platform allow teachers to make decisions and carry out improvement actions in the current course, i.e., to monitor specific student clusters, to analyze possible changes in the different evaluable activities, or to reduce (to some extent) teacher workload. The introductory and didactic planning courses were analyzed using the proposed toolbox. The proposed toolbox has been applied to two different datasets gathered from two courses at the Latin American Institute for Educational Communication virtual campus. In addition, the presented toolbox enables, in an incremental way, detecting and grouping students with respect to their learning behavior, with the main goal to timely detect failing students, and properly provide them with suitable and actionable feedback. The toolbox is mainly based on the fuzzy inductive reasoning (FIR) methodology and two of its key extensions: (i) the linguistic rules extraction algorithm (LR-FIR), which extracts comprehensible and consistent sets of rules describing students’ learning behavior, and (ii) the causal relevance approach (CR-FIR), which allows to reduce uncertainty during a student’s performance prediction stage, and provides a relative weighting of the features involved in the evaluation process. In this paper, an e-Learning toolbox based on a set of fuzzy logic data mining techniques is presented.
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