# An e-Learning Toolbox Based on Rule-Based Fuzzy Approaches
## 4 Results and Discussion
### Didactic Planning Course
#### 4.1.5 Using the Toolbox in the Next Course  
In the previous sections, the design and extraction of student behavior models at different times throughout the didactic planning course has been described. These models have been obtained from data from students who had taken this course in previous editions. The results of the functionalities offered by the toolbox, i.e., ACEP, USLB, ASLP, and GSLB, are useful for monitoring the new edition of the course, as well as for the analysis and possible adjustments of the features that are part of the course and its evaluation. The Time 1 model, i.e., before the start of the course, does not provide useful information neither the teacher nor the students. However, Time 2 model, which corresponds to the first month of the course, is already becoming an interesting tool for both actors. Firstly, the results obtained (see Table 3) indicate that feedback can be given to students whose predictions point to a final fail or pass result. In this sense, the obtained rules tell us that those students who did not deliver the IC task have high \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Analysis of the course evaluation process (ACEP)** & & & \\ Relative Relevance of the FIR model features: COEV = 0.32; IC = 0.33; ER = 0.35 & & \\ \hline
**Understanding students’ learning behavior (USLB)** & & & \\ Rules & & & \\ _if_ ER is low _then_ MARK _is_ Fail & & & \\ _if_ COEV _is high _and_ IC _is_ delivered _and_ ER _is_ high _then_ MARK _is_ Excellent & & 0.92 & 0.81 & 0.87 \\ _otherwise_ MARK _is_ Pass & & & 0.72 & 0.89 & 0.84 \\  & Macro-average precision: 0.81; Micro-average precision: 0.84 & & \\ \hline
**Assessment of Students’ Learning Performance (ASLP) and Grouping of Students’ Learning** & & & \\
**Behavior (GSLB)** & & & \\ Mean Square Error: 0.09 & Clustering: Fail 16/20; Pass 28/39; Excellent 67/73 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ACEP, USLB, ASLP, and GSLB toolbox results for the model 4 of the didactic planning course. The MARK variable was discretized into three classes: fail (from 0–6), pass (from 6.9–8.9), and excellent (from 9–10). COEV was discretized into three classes: low (from 0–5), medium (from 5.1–7.9) and high (from 8–10). IC was discretize into two classes: delivered and not delivered. ER was also discretized into two classes: low (from 0–8) and high (from 9–10). Spec., Sen., and Acc. stand for specificity, sensitivity, and accuracy, respectively. possibilities of failing the course. This is a quite evident conclusion, if the student does not work in the course, he will not pass it. However, the rule that has as a consequent the pass grade gives us much more information. In this case, the students have delivered the IC homework; however, they are mature students, and for some reason the prediction is that they will not get an excellent, only a pass. The five teachers involved in the course endorsed this rule, since they themselves found that mature students find it more difficult to understand the concept of initial class plan (IC) and the use of new technologies to carry it out. As a consequence, the teachers decided to send an e-mail to the fail and pass clusters, explaining that understanding the IC concept is fundamental to carry out the course successfully, and an online reinforcement class was added to clarify all the doubts that the students can had about it. On the other hand, analyzing the relative relevance of the FIR model features (ACEP functionality), it can be seen that the IC feature appears in Time 2, Time 3 and Time 4 models. In all of these models, the IC task has a very important relevance regarding the final grade of the course. As described previously (Section 4.1), all of the activities that students do throughout this course are centered on a document called a \"class plan\". Therefore, it makes sense that the initial class plan that the students carry out has a substantial weight in the subject and is a fundamental task within the course.