Aside from encouraging awarding bodies to produce better credentials, policy and decision-makers should incentivise credential issuers to make skills in micro-credentials visible using a common taxonomy developed by actors across the credential supply and demand sides.

A promising technological approach is the use of Artificial Intelligence to extract skills data from natural text, and to use this to better understand a person's learning accomplishments. Such tools are being increasingly integrated into applicant tracking software as well as common CV builders and platforms. Another approach is for recognising authorities to create mappings between employment opportunities, desired skills and micro-credentials that teach those skills.

One example of this in action is the European Skills, Competences, Qualifications and Occupations (ESCO). ESCO represents a European classification of skills, competences, qualifications and occupations available in 28 languages (besides official EU languages, Icelandic, Norwegian, Arabic and Ukrainian). It encompasses 3008 occupations (including hierarchical relationships between them and mappings to International Standard Classification of Occupations – ISCO and 13,890 skills) as of January 2023.

Another example is the Rich Skill Descriptors developed by the Open Skills Network. Using this system, an employer can publish a job vacancy that lists not only the skills required, but also link it to a list of courses that impart those skills. This can be used to screen potential candidates by skills or by learning background, increasing the potential pathways to an interview.

Finally, employers as well as education and training organisations can develop application templates which encourage users to describe the skills they possess, rather than solely relying on descriptions of work experience and credentials earned. This allows a person to describe their learning outcomes to a recognising body, even where the body has no prior knowledge of the micro-credential.

 

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