## 5 Discussion  
The results obtained with the test group allow us to state that LA, properly used, is a valid and efficient means of analysing student data that is contained in virtual classrooms, offering the teacher decision-making tools to employ in educational customisation and tutoring processes for students experiencing academic difficulties. However, when building the system, we identified a limitation in the selection and adaptation of the more relevant information, its analysis, and the subsequent modelling of student behaviour.  
Therefore, LA tracks the footprint that the student leaves throughout education processes, which is recorded in the LMS systems through the use of smart devices, mobile phones, tablets, and portables, as well as through participation in social media, photo blogs and chat rooms. The goal of analytics is to strengthen and individualise the educational process for each student, in each establishment, using measuring methods, collection models and database analysis contextualised according to specific interests, educational methods, and the dynamics of virtual teaching. Therefore, LA should improve the educational system and the learning environment supported by ICTs by incorporating sociological, psychological and statistical techniques, resources and methods into the management and analysis of student data, thereby allowing teachers to respond effectively to specific behavioural situations, preferences and other factors.  
Although it is true that some LMS have their own internal follow-up tools, it is currently necessary to use tools and standards that enable the structuring and storage of student interactions during various activities. There are standard options such as Tin Can APA, which extracts data from student activity and interacts with other applications, including SNAPP, LOCO-ANALYST, STUDENT SUCCESS SYSTEM D2L, Social Networks Adapting Pedagogical Practice, SAM, BESTAR INSIGHT, ORANGE, RAPIDMINER and KNIME. However, it is necessary to conduct further research to structure and develop report processing elements and mechanisms for learning analytics that align with current educational guidelines, and to strengthen instructional and decision-making processes.  
Figure 9: **(a)** The plotting for students’ grades in the first section versus the second. **(b)** The plotting for the same five students’ grades in the second segment versus the third.  
The sensor for identifying students that are at risk of failing a subject, using LA for subsequent customised tutoring, plays an important role in beginning to solve these needs, though there is much yet to be done and developed in this area to meet the above-noted requirements. This is a tool that differs from others in that it integrates four LA phases, enabling the teacher to access student information in the virtual classroom, as well as empowering the student to become aware of the progress and pattern of their academic performance. The commonly identified tools are focused on administrative processes and teachers, but none of them offer the student a comparative analysis of their progress as compared to that of the group. The sensor provides access to three of the four phases; a student can see their individual performance in relation to the group's performance (Explanation), locate themselves within the corresponding quartile (Diagnosis), and lastly, use the risk matrix to obtain an estimate of success or failure in a given subject (Prediction). Table 3 provides a comparison between the different tools, showing the function of the solution that we have developed.