## 4 Service Demonstration
### Course Selection  
As stated in the previous section, in order for the service to provide course and skill recommendations to a user, the latter should upload a detailed CV, which includes the target sector, the acquired skills, and the work experience, to the service. In this specific example, let us assume that a user named [PERSON] targets the information technology sector, and he is experienced in Java and MySQL, as illustrated in Figure 3 (CV upload). As observed, his proficiency level in Java is medium, as he has 2 years of experience with Java, and advanced for MySQL with 3 years of experience. It is worth mentioning that the * symbol in the form of Figure 3 indicates fields that are mandatory. This is the case also for other forms that are presented.  
After saving the uploaded CV, the service calculates the most appropriate skills for [PERSON] based on his profile. The recommended skills for [PERSON] are illustrated in Figure 4:  
As presented in Figure 4, the recommended skills that [PERSON] does not seem to possess are Git, Docker, Jira, SQL, Databases, Azure, Cloud, and Jenkins. There are more recommended skills for the specific user, but only the most important, in terms of preference by the labor market, are presented in descending order according to the demand for each one of them.  
As a next step, [PERSON] can either decide which skills he should try to learn through the help of the Multi-Criteria Decision Support service or receive course recommendations from his university. As the course selection is tightly coupled with the targeted skills, the user chooses to receive course recommendations as presented in Figure 5:  
Figure 4: Recommended skills.  
Figure 3: CV uploaded by learner.  
Figure 5: Recommended courses.  
Figure 6: Multi-Criteria Decision Support service input.  
The user selects the MAUT method, and the weights of the criteria are 40% for relevance, 15% for difficulty, 20% for state of the art, and 25% for interest. The difficulty criterion should be minimized, while all the other criteria should be maximized with the selected course. The relevance scores are the scores obtained by the Course Recommender, while the other scores are obtained by the user according to his perception for them. The result is illustrated in Figure 7.  
As shown in Figure 7, the most suitable courses for the user are Distributed Systems and Databases, while the Operating Systems course follows with a slightly smaller overall score, and Human-Computer Interaction is the last choice with a much smaller overall score. Of course, the user might need to dig further to decide which course to choose. By selecting two courses, he can further receive bipartite comparisons, comparing them for every criterion, as shown in Figure 8 for the courses Databases and Distributed Systems.  
As observed, the Databases course is considered to be easier, but it is less interesting and also less effective in providing state-of-the-art skills. Thus, the Distributed Systems course seems to be the best choice.  
Figure 8: Bipartite comparison between courses.  
Figure 7: MAUT method results.  
The procedure is similar for users who want to select skills on which they should focus, and therefore, it is not presented in the context of this publication.