BACKGROUND & CHALLENGES Machine Learning (ML) – a subset of Artificial Intelligence behind a series of important technological breakthroughs (automated translation systems, medical image analysis, virtual assistants) – is profoundly transforming “traditional” business models & processes across sectors, by optimising the processing of massive data volumes and automating core tasks. The fast-paced expansion of ML uses, especially in data-driven industries (financial services, health care, retail), is rapidly pushing forward the demand for skilled ICT workers in the EU. Whereas the demand for ML skills is steadily growing, employers are facing a shortfall of suitable candidates, which is leaving thousands of positions unfilled (an estimated 769,000), threatening productivity, efficiency & future growth. Besides this rapidly increasing demand, the prevalent mismatch can also be attributed to formal education and training providers’ slow and fragmented responsiveness to new workplace developments, especially when those are cutting-edge. The strengthening of both initial and continuous VET provision in the field is therefore essential so that the European ICT workforce can acquire and develop the mix of ML technical (data modelling, software engineering), non-technical (governance, business management) and meta (sense of initiative and entrepreneurship) skills required to deliver and support the uptake of tailor-made ML enabled solutions in the market. OBJECTIVES The project forms a Strategic Partnership to strengthen the key digital competences in VET provision for ICT workers, and address existing occupational skills needs & mismatches. MACHINA goals are to: 1. Design a joint VET curriculum in ML, to empower ICT workers with sought-after technical, non-technical, meta (soft) skills. 2. Introduce flexible training delivery methods and innovative open-access pedagogical resources to support VET provision and ML skills acquisition. 3. Foster the recognition and integration of ML skills requirements into sectoral competence frameworks & certification schemes. 4. Improve ML labour market & skills intelligence at the EU level. PARTNERSHIP The consortium comprises 5 organisations (and 6 associated partners such as Volkswagen Group) with high capacity/qualifications from 5 countries (FR, DE, GR, IT, RO), representing the worlds of VET, Policy Making, Research, Learning Innovation. UCBL, L3S are at the forefront of ML related research & technological development, being also pioneers in the design/delivery of ICT courses on data science and intelligent systems. ACADEMY, as a school specialised in ML and AI, brings expertise in the design/delivery of VET courses, covering the entire spectrum of ML technical, non-technical skills. ANC, as a National Coordination Point for the European Qualification Framework, contributes systemic knowledge & regulatory insights regarding qualifications, skills. EXELIA is an expert in R&D and the delivery of innovative ICT-based learning methodologies. ACTIVITIES -Extended labour market and skills intelligence gathering activities, leading to the development of ML learning outcomes -Development of the structure of a joint VET curriculum on ML -Creation of corresponding pedagogical materials to be offered as Open Education Resources -Development, testing, delivery of Vocational Open Online Course (VOOC) infrastructures on ML, promoting the uptake of innovative and flexible practices in VET -Creation of a blueprint (specs) for the establishment of an EU-wide ML qualification for ICT workers -Involvement of key policy makers & stakeholders for the social recognition of MACHINA learning outcomes as well for supporting the integration of ML skills into sectoral competence frameworks & occupational standards -Sharing of outputs with multiplier events, inviting target groups to uptake MACHINA results and to act as further multipliers RESULTS -Learning outcomes for training provision in ML for ICT workers -Learning units (curriculum structure), trainers’ toolkit and VET integration guidelines -Open Educational Resources for ML in 6 languages, including VOOC infrastructures & content -The outline of an EU-wide VET qualification in ML for ICT workers, to be eventually recognised, embedded into national certification schemes -A blueprint with recommendations for the integration of ML skills requirements into sectoral competence frameworks (e-CF) & classification systems (ESCO) -5 national information days to promote project results and set an open discussion on increasing the quality and effectiveness of VET POST-PROJECT SUSTAINABILITY -Uptake of project materials & OERs from relevant E&T providers across the EU -Participation of a growing number of initial and continuous VET learners in courses based on or integrating project outputs -Expansion of the strategic partnership towards an EU ML skills network of collaborating VET providers, sector representatives, social partners, ICT companies.
The project forms a Strategic Partnership to strengthen the key digital competences in VET provision for ICT workers, and address existing occupational skills needs & mismatches. MACHINA goals are to: 1. Design a joint VET curriculum in ML, to empower ICT workers with sought-after technical, non-technical, meta (soft) skills. 2. Introduce flexible training delivery methods and innovative open-access pedagogical resources to support VET provision and ML skills acquisition. 3. Foster the capitalisation of MACHINA results in VET practice through the recognition and integration of relevant occupational requirements into competence frameworks & certification schemes. 4. Improve ML labour market & skills intelligence at the EU level.
Machine Learning (ML) – a subset of Artificial Intelligence behind a technological breakthroughs (automated translation systems, medical image analysis, virtual assistants) – is transforming “traditional” business models & processes across sectors, by optimising the processing of massive data volumes and automating core tasks. The fast-paced expansion of ML uses, especially in data-driven industries (financial services, health care, retail), is rapidly pushing forward the demand for skilled ICT workers in the EU. Whereas the demand for ML skills is steadily growing, employers are facing a shortfall of suitable candidates, which is leaving thousands of positions unfilled (an estimated 769,000), threatening productivity, efficiency & future growth. The prevalent mismatch can also be attributed to formal education and training providers’ slow and fragmented responsiveness to new workplace developments, especially when those are cutting-edge. The strengthening of both initial and continuous VET provision in the field is therefore essential so that the European ICT workforce can acquire and develop the mix of ML technical, non-technical (governance, business management) and meta (sense of initiative, entrepreneurship) skills required.