Best practices
Framework for competency-based evaluation of students in higher education
Users: Training Providers (Public) | Theme: Skills Data | Action: Education Programmes/Courses |
Beneficiaries: Training Providers (Private), Training Providers (Public)
MERIT
Simona Ramanauskaite, Full professor and senior researcher, Vilnius Gediminas
Technical University
Designing content in higher education is often associated with a lack of agility. Universities create fixed curricula, following rigid schedules, which do not match market dynamics, but rather limit their adaptability to rapidly evolving digital skills requirements
MERIT has the task of creating an educational ecosystem, spanning across Estonia, Lithuania, Latvia, Italy and Spain to train digital specialists and improve the evaluation of different students or courses by matching study programmes to the skills produced.
The challenge?
MERIT was faced with a lack of standardised frameworks and common terminology for defining digital skill levels and competencies.
This made it difficult to compare and analyse study programmes across different countries and institutions, hindering a reliable programme-skills mapping.
Addressing these issues was essential to ensuring a structured and comparable approach to digital skills education.
Our solution
MERIT implemented a continuous and competency-oriented student and study programme analysis, which is not commonly applied in higher education.
It established its own comprehensive framework for advanced digital skills and supporting competencies, which ensured a structured approach to skills evaluation and programme alignment.
It implemented new training and assessment methodologies focused on competency development, covering both technical and soft skills relevant to industry needs.
It developed tools for continuous analysis and monitoring of student progress and study programmes, based on a taxonomy of topics and their relationships. This facilitated the cross-country comparison and standardisation.
Lastly, it created a structured mapping system that aligned educational programmes with key competency areas, ensuring that skill levels and topics are consistently classified across institutions.
Outcomes
74
Topics and skills
forecasted
for research and academia
139
Technology Priorities
identified
for industry
12
University Priorities
identified
in soft skills and knowledge areas
- Because of the data mapping, students can now be evaluated based on their competencies, and not based on courses that they have completed.
- Alumni are also encouraged to continue with life-long learning by receiving more structured directions on how to improve upon their competencies.
- Industry and alumni matching is available based on competencies / skills matching.
Key takeaways
- Multi-Source Skills Analysis is Essential: The most effective evaluation method which combines multiple data sources, including SME needs, research trends, and summarised reports, to accurately assess hard and soft skills.
- Skills Mapping Should Align with Institutional Priorities: Competency frameworks should be adaptable to each partner university’s focus areas, ensuring alignment with local academic and industry requirements.
- Developing a Detailed Topic Hierarchy is Crucial: Establishing a well-structured and comprehensive skills taxonomy is key to ensuring comparability and adaptability in digital education programmes.