Best practices

Incorporating automated tools and analysis in master's programme design

Users: Training Providers (Public) | Theme: Skills Data | Action: Framework/Methodology | Beneficiaries: Training Providers (Public)

MERIT

digitalmerit.eu

Simona Ramanauskaite, Full professor and senior researcher, Vilnius Gediminas
Technical University 

MERIT is developing master’s degrees and short-term with the participation of NGOs, non-profits, research organisations, companies and SMEs. These are delivered by technical universities across Estonia, Lithuania, Latvia, Italy and Spain.
 
Courses in domains such as AI, cybersecurity and IoT become quickly outdated and projects like MERIT need resilient methods for easily building new content. Establishing an intangible infrastructure which allows for this is key to the project’s sustainability.

The challenge?

MERIT had to strike a balance between specialisation and flexibility to ensure it meets both national market demands and student preferences. It should be adaptable to evolving industry needs while maintaining a structured curriculum that provides essential digital competencies.

 
Designing courses based on arbitrary data and anecdotal knowledge would pose risks to the programme’s attractiveness and market match. Additionally, performing manual analysis of available data and indications for each new course would have been time consuming and inefficient.
 
Questions that MERIT needed to answer include:
  • How to monitor the market needs and existing innovations?
  • How to assure the need for updates of existing courses?
  • How to identify common topics for 5 study programmes with more than 15 courses?
  • How to monitor students’ progress and identify potential issues in their development pathway?

Our solution

MERIT developed automated tools for systematic analysis and monitoring to enhance data-driven decision-making in education and workforce development.

It minimised the resources required for data analysis by employing summarisation tools that aggregate and interpret large datasets, providing a clearer understanding of key insights. 

A competency-oriented tool was developed to monitor skill acquisition, track competency development, and predict future training needs. 

Outcomes

3x

Faster mapping than before

due to the automatic

 mapping tool and simplified syllabus

90%

Tool accuracy

for automated course

similarity estimation

 

50+

Courses mapped

simplifying the mapping between different study programmes

Key takeaways

  1. Data scraping from multiple sources is essential: Leveraging data to collect real-time information on industry trends, skill demands, and educational programmes is an effective method to keep up to date with changing market demands and ongoing innovation.
  2. AI and NLP as tools to identify similarities, cluster data, and map trends across various educational programmes: allows for monitoring of necessary tweaks for existing courses.
  3. Implementing learning environment log data to identify potential study experience challenges is essential. This will allow you to access their root causes will enable proactive interventions.
Learn more here

Case Summary–

Context: Fit4internet is a non-profit initiative aimed at enhancing digital literacy across Austria. By using the Digital Competence Framework (DigComp AT), which identifies key components of digital competencies and aligns with European standards, Fit4internet helps individuals showcase their digital skills. The Fit4internet platform offers a way to track and prove digital competencies, helping with employability and ensuring that the Austrian workforce has the necessary ICT skills to meet market demands. Through the digital skills profile platform, the initiative supports both individual growth and the development of a digitally capable society. 

Challenge: The challenge faced is managing proof of competencies acquired through various learning methods, including formal education, non-formal training, and informal certifications, which can lead to inconsistent records.  Securing sustainable funding to support the continuous development of new digital competencies, ensuring that programs remain up-to-date with evolving market needs. The second main challenge was fostering broad participation and engagement, especially among groups who may lack access to digital tools or education.

Identified Approach / Solution: The Fit4internet platform provides a centralised, user-friendly system to manage and showcase digital competencies, aligning with the European Qualifications Framework (EQF). Through this platform, individuals can track and showcase their digital skills, making it easier for employers, educators, and organisations to assess their talent.

The platform allows users to create an e-portfolio that showcases their digital skills, with the option to share it through a link or as a PDF. This portfolio highlights the digital competencies that a user has acquired, linked to their certificates. These certificates can be formal, non-formal, or informal.

The platform also supports the generation of a digital skills profile that showcases the user’s acquired skills. Users earn digital badges as a visual representation of their skills. This allows them to continue with their learning journey, while showing off their competencies to others.

Outcomes:

 

 

 

Key takeaways:

Fit4internet engage multiple stakeholders from academia, industry, and policy to develop holistic and relevant digital competency frameworks. They also use widely applied frameworks to measure skills gaps, enabling better planning for individuals and institutions. By doing this they improve reinsertion rates into the labor force by addressing individuals digital skill gaps and aligning them with market needs. This benefits companies by providing clear insights into the skillsets that are being developed and that are available in the workforce.