# An e-Learning Toolbox Based on Rule-Based Fuzzy Approaches
## 3 The Toolbox
### Teacher  
Teachers have several options available to help them better understand student learning behavior and the usefulness of the assessment process. These options are: assessment of students' learning performance (ASLP), grouping of students' learning behavior (GSLB), understanding students' learning behavior (USLB), and analysis of the course evaluation process (ACEP); see left hand side of Figure 1.  
The ASLP option provides a continuous evaluation of students during course development. This is done by performing a prediction of the student's final grade using a FIR model. 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. The platform offers teachers the possibility of sending automatic feedback to clusters of students, i.e., the set of students with failing grades predictions. The teacher decides the content of the automatic feedback and to which clusters of students should be applied. 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. This option is accomplished by using the FIR methodology, described briefly in Section 2. The GSLB option provides, in a dynamic and incremental way, students' clustering based on the prediction of their final marks. For instance, in the first month of the course, only information about the navigability, usability and the first test results are available; however, at the last part of the course richer information is accessible, such as different tests grades, homework marks, partial projects marks, collaborative resources marks, etc. In turn, a better model identification and, of course, more reliable prediction results can be accomplished. The USLB tool provides an interpretable and comprehensible way to describe students' learning behavior, by means of linguistic rules. The rules are automatically mined from the data gathered from the course and students' interaction within the e-Learning platform. This allows knowing the course performance patterns and, therefore, using this knowledge in future courses design or for decision support. Mined rules reveal interesting and valuable knowledge, which can be applied in educational contexts to improve the learning experience. This functionality is accomplished by means of the LR-FIR algorithm (see Section 2). The ACEP option identifies the most relevant features (i.e., input variables) involved in the evaluation process. This knowledge allows teachers to focus on the relevant variables (e.g., confer more weight) and take concrete actions related to the less relevant variables (e.g., not spend much time grading those items or delegate correction to an assistant). The results of the ACEP can be useful for teachers and course coordinators to analyze the way the course is being evaluated and, if necessary, modify it. This functionality is performed through the CR-FIR algorithm, described briefly in Section 2. Figure 2 describes graphically the dynamic strategy for all the functionalities. For a given course, different time periods are defined by the educators (teachers and course coordinators). For each time period, a FIR model is identified using as input only those parameters that are available at that point in time. For example, FIR model 1 is obtained before course starts, so only initial student's information data are available. The goal is to find patterns, if exists, that allow to predict the final grade of the student based on their initial information. FIR model 2 uses additional features, already related to the course, such are some evaluation tests, exercises, etc. since these features are already available at this time.