Methodology

To understand if a University programme develops certain skills we compared the textual description of each programme with the description of an ample set of skills. The description of university courses stems from the Scheda Unica Annuale (SUA, which was available from this website until the beginning of 2023: www.universitaly.it). The description of skills derives from the ESCO European Skills, Competences, Qualifications and Occupations (https://esco.ec.europa.eu/en) repository.

Each course description was segmented into overlapping windows of approximately 30 words, corresponding to the average length of a skill description, in order to facilitate meaningful comparison. We employed a state-of-the-art language model based on artificial intelligence to embed both course fragments and skill descriptions into vector representations capturing their semantic content. For each course-skill pair, semantic similarity was computed using the cosine similarity metric, which ranges from 0 (no similarity) to 1 (perfect similarity). These similarity scores were then aggregated through a flexible weighting function, designed to reflect the extent to which a given skill is represented within a course. This approach allows us to construct a relevance score that accounts not only for the presence of individual skill-aligned segments, but also for the distribution of relevance across the entire course description.

Data refer to Academic Year 2022-2023. For each NUTS3 region (Italian provinces) we report up to 5 courses locally ranked by green, digital and twin relevance scores (Local Rank). To avoid reporting courses with minor content associated to the focal skills, we consider only those with a score higher than the national median. We consider the nuts3 region where the teaching takes place.

In the tabs above we used the Italian terminology for the Programme Level and disciplinary areas to facilitate interactions from general public and prospective students. Do get in touch if you are interested in knowing more about the data.

Local Rank
For each NUTS3 region (Italian provinces) we report up to 5 courses locally ranked by green, digital and twin-relevance scores (Local Rank).
To avoid reporting courses with minor content associated to the focal skills, we consider only those with a score higher than the national median. We consider the NUTS3 region where the teaching takes place.

National Score
For each course we also report 3 bands (high, medium, low) of the score (National Score), based on its National distribution.