Skills-driven Higher Education Institutions

The UOC (Universitat Oberta de Catalunya – Open University of Catalonia) aims to be a university that, connected in a network with other universities around the world, promotes the construction of a global knowledge space. The UOC innovates in its own educational model that focuses on the student, offering quality and personalised training, to promote their competitiveness and contribute to the progress of society.

In this context, for the UOC, data is a fundamental asset and a key element in expanding the limits of lifelong learning through the strategic application of learning and skills analytics and the opportunities offered by technologies such as Artificial Intelligence (AI).

To achieve this, The UOC has been implementing their own data strategy for building a data ecosystem to observe and improve educational systems and processes and contributes to understanding and modelling lifelong learning itineraries and educational experiences. At the same time, the UOC is also providing, through data, advising services for students to achieve labour market professions and skills and advising services for faculty staff to improve the quality of teaching & learning process.

The value of this use case is to combine learning data & skills data to provide a more skills-driven institution. In online (Open) universities like UOC most of the students are workers at the same time and they are more interested in skills achievement than in academic completion. 

In line with the previous value proposition, UOC is the coordinator of the OpenEU European University Alliance. Open EU is an Alliance of the Open Universities in Europe with the aim to create a pan-european Open University. Therefore, the relevance of skills for Open Universities and how to advise lifelong learning students to achieve the labour market skills, is one of the big topics in the Alliance.    

The specific goals to achieve are:

• O1. Add skills data dimension to learning data sets.

The assumption is that by adding skills data, institutions can gain better insights into both quality of learning and learning path taking into account skills goals and needs.

• O2. Improve insights through learning analytics and skills analytics.

The assumption is that by enriching and sharing learning and skills datasets, both education & training institutions and labour market organisations can gain better insights and make more informed decisions.

• O3. Improve the data sets to be FAIR (Findable, Accessible, Interoperable and Reusable) by collaboration with other skills data providers and unifying data formats and procedures.

The assumption is that by unifying data formats and procedures organisations can share analytics techniques, data models and dashboards.