## 3 Experiments and Results
### Learning Features  
The learning features proposed in this work are based on the assumption that a certain number of variables can be used and combined by machine learning techniques with the aim of understanding the learning process. In general, our underlying hypothesis is that the classifier should take the users' interactions with the e-learning instruments over time into account instead of simply considering a quiz result in terms of a score achieved. A similar approach has been defined in [23], where the authors suggest consideration of the whole academic achievements of a learner over time to improve the prediction results for their learning model. For this reason, variables such as the number and type of documents present in a course (e.g., slides, images, interactive material, etc.) as well as the number and type of interactions of the user with the platform are extracted. Even if the preliminary results have been presented with reference to the OULAD dataset, to ensure an effective and automatic information retrieval using custom e-learning courses based on the Moodle platform, a custom software plugin has been developed in PHP 7.0 for this purpose. This software plugin could be extremely useful to reduce bias that could be experienced in case of manual data extraction from custom databases. Moreover, the goal was to create a plugin useful for extracting the data necessary for the analysis from any type of Moodle course. With reference to the feature extraction performed for OULAD dataset, reported in Section 3.1, the following macrofeatures have been identified to be extracted from Moodle:  
* TotDocuments: Total number of documents present in the course.
* TotResouces: Total number of resources present in the course.
* User id.
* NLogin: Number of logins made by the user.
* SessionTime: The length of stay on the platform is indicated for each login.
* NSyncInteraction: Number of synchronous interactions made by the user.
* SynCInteractionTime: Duration of synchronous user interactions.
* NASyncInteraction: Number of asynchronous interactions made by the user.
* NCompleted Documents: Number of documents completed by the user.
* NCompleted Resources: Number of resources completed by the user.
* QuizId: ID of the quiz carried out by the user.
* Attempts: Attempts made at the same quiz by the user.
* Grades: Grades obtained for each attempt.
* DurationTestPassed: Duration of the test inherent in the successful attempt.
* NAcess: Number of test accesses for each attempt.  
It is worth noting that for these macrofeatures, aggregate functions such as minimum, maximum, sum, and average values over time are extracted to be comparable to the features described in Section 3.1. The plugin generates a file for each course on the platform in the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Values (\%)** \\ \hline Accuracy & 95.18 \\ \hline Sensitivity & 97.40 \\ \hline Specificity & 93.19 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy, Sensitivity, and Specificity of the boosting ensemble strategy.  
\"admin/chi\" folder of Moodle. The generated files can be directly used as the input for subsequent classification algorithms. The extraction process is scheduled over time, as it relies on Moodle's Cron service (more specifically it runs every week).