Since the DS4Skills kicked off in October 2022, the project consortium has worked on one of the foundational pillars – delivering a methodology for the categorisation and assessment of existing initiatives in skills and educational data. This lays the first foundation for shaping the future data space for skills and education. Through an online survey, 108 initiatives were collected and analysed so far.
Accordingly, a methodology is specified to categorise and assess the existing data sharing initiatives in the skills and educational domain. It supports the overview of the current fragmented landscape of existing platforms, services, apps, data spaces, and other initiatives. Also, it provides a baseline to identify the most promising initiatives for further interviews and analysis.
The categorisation of initiatives follows the method of qualitative classification of data. A set of relevant parameters is established with clear definitions to classify and assess different kinds of initiatives. For example, how mature the initiative is, what type of stakeholders it represents, what level of interoperability is achieved, what needs it addresses, etc. In total, the methodology consists of 19 parameters.
A subset of these parameters is used to classify initiatives as in or out of scope for DS4Skills, as follows:
- Type of data: DS4Skills looks for initiatives that are skills data or educational data specific – generic data initiatives are considered out of scope.
- Actuality: DS4Skills looks for initiatives that are active – initiatives with no visible activity in the past 2 years are considered out of scope.
- Maturity: DS4Skills looks for initiatives that are well documented – initiatives with no or very limited documentation are considered out of scope.
- Geographical scope: DS4Skills looks for initiatives with a broader geographical scope – very regional initiatives are considered out of scope.
Of specific interest of DS4Skills, and therefore always considered in scope following the methodology, are initiatives that already have developed a data space, a data ecosystem or data standards on skills or educational data.
The methodology also provides guidelines to evaluate an initiative as highly relevant, somewhat relevant and not relevant to take into account for the design of a data space for skills. The parameters defined in the methodology are divided into three groups for the assessment:
- Diversity parameters, that ensure that the market scan comes up with a diverse set of initiatives.
- Comparison parameters, that are used to mutually compare and assess the relevance of the initiatives.
- Descriptive parameters, that document information of an initiative, not taken into account for the assessment.
Based on the categorisation and assessment methodology, the next outputs that DS4Skills will work on are a functional online inventory of existing initiatives and a blueprint of the data space for skills.