## 5 Discussion and Results
The courses consist of a main module that provides detailed information on the type of study, the matter, and the assigned tutor. In the same way, the student will find the syllabus and the study guide that allows him to know exactly the topics to be reviewed and the activities to be completed. Within each week, the module is divided into sections that contain the resources, activities, and corresponding information to assign an asynchronous meeting with the tutor. In the resources section, each tutor is in charge of uploading all the material corresponding to the topic of the week. These resources must be aligned according to the learning results of the subject. The tutor usually uploads his own material, such as a presentation, the resolution of an exercise, or a reading. In addition, it must include supporting material, such as videos, readings, scientific articles, etc. In the activities section, the student finds everything to do during each week. An activity is an opinion forum, where the student comments critically and objectively on a topic raised by the tutor. Another activity that the student must complete is a task that meets the requirements set forth in [PERSON]'s taxonomy. The objective of this theory is that after completing a learning process, the student acquires new skills and knowledge. For this reason, it consists of a series of levels built with the purpose of ensuring meaningful learning that lasts throughout life. The levels of [PERSON]'s taxonomy are know, understand, apply, analyze, evaluate, and create. In addition, the student must complete a questionnaire-type evaluation, the purpose of which is to encourage students to read the resources. The last section maintains the information corresponding to the asynchronous meeting with the tutor. The objective of the meeting is that students can make all the queries directly to the tutor or can receive feedback on the activities or topics discussed. Each meeting lasts 60 min. In this model, these meetings are not mandatory and the student can review the recording as many times as they deem necessary. Once the scenario where the model is integrated has been defined, the variables that explain the dropout are established. The set of variables is the university degree corresponding to the numerical value of the general average of a student's secondary studies, the number of subjects passed, the number of enrollments in the defined periods, the subjects taken (between 1 and 20, coded according to the average number of subjects taken), the sex, and the age of the students between 19 and 30 years old. The problem addressed refers to the detection of the causes of university dropout; previous works have considered desertion to constitute the failure of a student in a consecutive period. In the first exercise, big data requires access to all logs of activities carried out by teachers and students that are usually stored in MySQL. All the data obtained from the different sources went through a processing and transformation phase in order to obtain clean data that are analyzed by Hadoop in search of the patterns that the students follow. In Figure 5, the patterns of the first exercise are presented, where the results of the activities carried out by the students during the established period are obtained. In the \"x\" axis, the activities, where H1 is the forums, H2 the tasks, and H3 the questionnaire-type evaluations, are shown. On the \"y\" axis, the obtained grade is presented. It is necessary to indicate that the grades respond to the use of rubrics that guarantee learning. These grades range from 1 to 10. On this axis, six is marked as an acceptable grade that meets the minimum learning criteria. In the forums, it is observed that the learning level is high in most cases, and the low grades are mostly due to the fact that the student did not register their participation or that the contributions were not objective.