## 4 Results
### Data Considered in the Analysis
In addition, it allows to establish the correct processing and inclusion of variables in the model without changing the infrastructure of the big data model. The application of data mining algorithms is responsible for the improvement process--by selecting the data that provide information to the analysis hypothesis. To identify the variables that directly affect the detection of patterns in the data, an analysis process was established using the J48 algorithm. For validation, the data provided showed that their efficiency was 94%, as indicated in Table 1.  
In addition, from the scenarios so far specified, the data analysis model was evaluated with additional variables in order to measure student learning. For this, at the beginning of the academic period an integrating project was proposed. This project is individual in nature, where the student is the one who proposes the subject under certain mandatory standards. Among these are several deliverables found in the LMS, where you receive feedback from the teacher. These projects are aligned with the fulfillment of the learning results of the subject that, in the analysis, are transformed into classes that are aligned with the learning results of the career. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Action** & **Instances** & **Percentage of Hits** \\ \hline Correctly Classified Instances & 141 & 94\% \\ Incorrectly Classified Instances & 9 & 72.115\% \\ Kappa statistic & 0.91 & \\ Mean absolute error & 0.0598 & \\ Root mean squared error & 0.193 & \\ Relative absolute error & 13.4523\% & \\ Root relative squared error & 40.9465\% & \\ Total Number of Instances & 150 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Stratified cross-validation. Figure 8: Analysis of the qualifications of the administration students due to the availability and use of ICT. This integrative project aims to generate a grade and evaluate learning. This is possible by using rubrics that evaluate each stage of the project and in each of the criteria a status is defined, where the student meets the learning outcomes. In this way, it is possible to have a final grade and at the same time evaluate learning. The following example demonstrates the process and then, in the discussion, raises the different changes that must exist in the entire educational model in order to transform it into an adaptive model. The following learning outcomes are part of the subject with which the data analysis model has been evaluated. * Identify computer systems, their elements and the interaction between them. * Apply the best industry practices in solving basic IT problems. The learning results of the subject guide the student on the conception of the project topic, objectives, scope, etc. These have been previously designed to align with the learning outcomes of the career and these are aligned with those of the university. With this sequence, it is possible to identify the levels of learning achieved by students. All criteria are part of a rubric evaluated with established marks such as very good, good, fair and insufficient. So far, the application in an LMS such as Moodle is totally transparent, but the LMS allows everything to be aligned to obtain a grade. Therefore, the work is transferred to the big data framework. In Table 2 that is presented, the different learning results aligned to each criterion are added; for example, in the element \"Integration of Hardware and Software Infrastructure\" two results are aligned with learning, one of the career and one given by the university-- in the table they are represented by numbers. Compliance has been marked with X according to the level where it is assumed that a student obtains the learning established in each standard. This process is repeated until the last item in a rubric is reached. Each relationship between elements and learning outcomes is raised in the framework. It is the framework that collects all the information found in the LMS database, creates the connections and establishes an initial state where learning begins to be generated.