## 4 Case Study
### Particular Image  
The next step is building a particular image from the basic image. For this, the data from the graph \(G_{w}\) showing the learning process from the structural point of view is shown in Figure 8a. Level \(R\) shows tests and assignments that measure students' performance; the root \(W^{\prime\prime\prime}\) represents the curriculum (horizontal semantic links are omitted, the hierarchy is formed by semesters, courses, topics, and learning units inside them). Figure 8b shows a graph of the learning process from the functional point of view \(G_{z}\), where the lowest level \(R\) represents tests and assignments for measuring key professional skills, and the top level \(Z^{\prime\prime\prime}\) represents the professional profile of the (possibly generalized) student. Figure 8: Fragments of graphs depicting a learning situation from the structural (**a**) and functional (**b**) points of view. The distribution of estimates \(K_{p}\) for the learning program \"Informatics and Computing\" by the zones of the UGVA figure is shown in Figure 6c. The following principles were used to adapt the basic image to the task:  
* Graphic primitives closer to the image center show the indicators for the basic component of the curriculum; the farther primitives show the indicators for the varied component of the curriculum;
* The left arm (the axis showing the \(S_{1}\) skill group) will show an artifact-icon (a paper piece with a block diagram) if the curriculum contains a special course with a course project requiring developing a software application;
* The headgear (which works as the axis for the \(S_{4}\) skill group), showing regional sectoral specifics, belongs to the upper stratum because many graduate learning programs lack a regional employer who specifies the requirements for \(S_{4}\);
* The color of the graphic primitives, which is used to show the results of particular students, will use the scale red-white-green;
* The zone of integral indicator (the figure's head, drawn as a circle), in this research, will show the only integral indicator \(\mu\), associated with the form of the figure's \"mouth\" and will not be affected by any other parameters. The particular image shows only curriculum (without the data about activity of students), so the image will omit the lower stratum because the curriculum in this model is considered static, and the decision maker does not need to compare this curriculum to the previous one. If any of the indicators \(K_{p}q\) are zero (full uncertainty), it will be highlighted in red. To display a specific system (in this case, a curriculum), using a particular image, we need to gather and concentrate the data about it. We used the curriculum for the academic concentration 09.04.01.03 \"Information systems and flight control centers\" at the Siberian Federal University (graduate program in 2021) as an example. We analyzed the curriculum and evaluated the contribution of each course to the key professional skills (components \(S_{p}\) of the parametric model). In this learning program, students take 23 courses (90 credit points total excluding internships and a master's thesis, which equates to 3240 academic hours). We formed a table of expert evaluations of contribution \(x_{p}j\in[0;1]\) of each course \(j\) to development of key professional skills \(S_{p}\) (see Table 2). The courses also have additional parameters: the curriculum component that the course belongs to basic (\(a=1\)), varied (\(a=0,7\)), or elective (\(a=0,5\)); the form of the final exam (\(b_{1}\)); workload in academic hours in percents of the total academic load (\(b_{2}\)), etc. We calculated the contribution of each course \(x_{p}j\) into the integral indicator \(K_{p}q\) for each course \(j\), belonging to the basic (\(q=0\)), varied (\(q=1\)), or elective (\(q=2\)) component of the curriculum according to Formula (2). If \(x_{p}j=0\), the course does not affect the skill \(S_{p}\); if \(x_{p}j=1\), the course is aimed at the development of the relevant skill.