## 4 Case Study
### Results  
In the previous section, we have shown how to create a set of images in the UGVA notation (configuration \(\lambda 5 Bit\)) for the task of managing a learning process. Here, we look at the practical decision-making tasks that can be solved used these images.  
The first task is evaluating the learning situation for the graduate student in its complexity. Consider the left image in Figure 10. It shows that student ID 01022 hasproblems in some of the courses (there are pink and white primitives). However, he has good knowledge of the skill group \(S_{3}\) \"application of mathematical and algorithmic methods of information processing\" based on the previous level of learning (the lower stratum of the left part of the image for \(OK\)-12). This lets the supervisor think that the problems with these courses can be solved. However, the results of the course project (the artifact in the right hand) for the competence \(OK\)-12 require special attention to the whole block of courses for skill group \(S_{1}\) (c.f. the other competences in the right part of the images in Figure 11). Modeling other competences also shows that skill group \(S_{2}\) is very problematic for \(PK\)-2 for this student (the bright red rectangle in the right image in Figure 11).  
When students are compared, the full map of the student group is used. A unified parametric model and the same configuration of the middle stratum allow quick recognition of key tendencies and problematic aspects. For example, by analyzing the images shown in Figure 10, we can see that the \(S_{1}\) (the left part of the middle stratum) is problematic for the majority of the group during developing competence \(OK\)-12. Even when the profiles for the previous level of education are the same (c.f. images for students ID 01034 and ID 01034), the mined data from the LMS will show the difference in the current learning situation by coloring the image zones differently.  
To assess the dynamics of the selected parameter (monitoring task), the colors of the corresponding zones should be compared. To do this, a series of images in the UGVA notation for the same student in different moments of time is generated. For example, the analysis of dynamics of competence development for the competence \(OK\)-12 for student ID 01022 (Figure 12) finds that the skills from groups \(S_{1}\) (right limbs) and \(S_{3}\) (left limbs) were progressing slowly, whereas skill group \(S_{2}\) (center) was learned much more effectively.  
The results of quick visual analysis can lead to practical conclusions for the decision maker and the students. For example, the leftmost student shown in Figure 10 was recommended to work on the course \"design of automated information systems\" (topics #3 and #4, assignments #2, #4, and #5) and the relevant textbook material and the course \"object-oriented programming\" (topics #4 and #5, assignment #4). These data will be automatically retrieved from the mined graph \(G\) using the edges of subgraph \(G_{w}\) and their weights for \(R\), analyzed relative to the identified problem with the key professional skill group \(S_{1}\) for the competence \(OK\)-12 (which is obvious from the color of the block diagram artifact and the right forearm). Adding the data about the student (e.g., student's learning goals, obtained from questionnaires and the dialogue options the student selected) will substantiate the decision of the ITS much better.  
SFU faculty used cognitive visualization in the UGVA notation for managing the learning process of students using the university learning management system in 12 courses of the curriculum \"information systems and flight control centers\" during three semesters for the students enrolled in 2019, 2020, and 2021. It allowed both teachers and students to better understand inter-course links in the curriculum and improve their learning-management decisions. The preliminary results of this implementation are discussed in Section 5.