## 5 Discussion and Results  
The new normality that humanity lives in forces institutions to seek new models that adapt to the needs of people. This paper takes this consideration into account and seeks to improve an online education model. The integration of technologies becomes the starting point to improve education and monitor student performance. It should be noted that the current reality has allowed online, virtual, or hybrid education models to become the expected response to continue with higher learning. This work is applied on the architecture and infrastructure of the university that participated in the study. This is considered an advantage, since, having the majority of the infrastructure deployed, it allows the concentration of efforts on the design of the machine learning model. If there is a need to modify any layer of the architecture, it is simply updated without the need to generate higher technical, human, or economic costs. With the integration of these technologies, the monitoring of student performance is improved, which generally depends clearly on the criteria of the teacher or those in charge of learning. With this model, the monitoring does not have human actors, the systems are in charge of carrying out a continuous analysis of each student, and the machine learning model will even detect the cases that have the highest risk of low academic performance. This feature allows thee generation of an early warning that is currently established when the academic monitoring department knows a certain number of grades. Early detection of the comprehensive model allows the generation of projections based on the student's history. For example, in students who had problems in the subject of introduction to calculus, the system recognizes them as possible cases with problems in calculus I and subjects whose prerequisite is introduction to calculus. This analysis can be very superficial; however, the system can even determine a possible case of repetition by analyzing the topics that make up a subject. For the recommendation of activities, machine learning has knowledge of the student's performance in each activity. Therefore, the decision is made based on the best results that the student obtains in each activity. For example, cases have been detected where type activities, rapid evaluations by means of true and false items, do not align with the need of a certain group of students. The model identifies these groups and recommends other types of activities to the course designer. For this, the development of active learning is taken as an essence. In this type of learning, a wide variety of activities have been developed that machine learning proposes to the student according to their needs. In order to evaluate the proposed model, several exercises were carried out in which the two parallels that belong to an administrative career were involved. Each parallel is made up of 24 students, and the follow-up period was 16 weeks, which generally lasts one academic period. Each level is made up of five subjects, among which students must take general, complementary, and professionalizing subjects. The sample of students belongs to the fourth level. The main reason why this group was chosen is for information obtained from the academic monitoring department. Here, it was found that the first two years of study is where the highest dropout rate is recorded. In addition, students at this level have taken all computer science subjects, allowing them to adapt more easily to a model based on the integration of technologies. The online education model of the university participating in the study complies for each course or subject with an already standardized model consisting of 16 weeks. These are divided into two partials each of seven weeks plus one partial evaluation. Within the LMS, specifically Moodle in the case of the university, each of the courses is created and registered and these have been divided into modules that respond to each week of the period.