Background

The fish farming industry in Europe employs approximately 85,000 staff and a further 120,000 work in the support services. The EU seafood market sources 10% of its fish from EU fish farming, 25% from fisheries and 65% from imports (SEC 2011, p 883). Norway, our largest producer, provides 36 million salmon meals daily.

Fish represent 50% of all consumed protein and levels are anticipated to rise to 65% by 2030, reflecting a growth rate globally of 6.6% per annum. Conversely, despite ambitious national growth targets the industry is stagnant. The lack of personnel with the correct skills and qualifications is becoming widely recognized as one of the main obstacles to sustainable growth in production. The industry is becoming increasingly sophisticated leading to specialization within the workforce. However, the lack of accessible specialist training in operating the more advanced equipment and technologies has led to inefficiency and fish losses.

At the farm level, throughout Europe, workforce development challenges are exacerbated by the remote rural location of many facilities. Consequently, the reliance on local recruits is growing, many of whom have knowledge and skills gaps and remain unqualified following a significant period of initial employment. This is typified by Norway, where only 50% of their salmon husbandry staff has completed any relevant education leading to qualifications. In some countries, such as Scotland and Norway, where migrant labor is prevalent, the language and culture barriers to learning are intensifying, necessitating a more individualized approach to learning

Initially, alternative RPL definitions and approaches and their application within national qualification delivery systems will be explored by partners to identify any existing good practice to be shared. Subsequently, the methods will be demonstrated to VET teachers in partner countries, ultimately training some to apply RPL to the delivery of their courses. The RPL apply feedback loops to determine each individual learners existing knowledge, skills and understanding and identify the topics and concepts that they find most challenging. Subsequently, this allows teachers to precisely target their teaching to satisfy each individual learner’s learning needs. Ultimately, the methods and tools can accept and document evidence of a learner’s prior learning for recognition within a qualification or certificate that is respected within the labor market. 

Following initial demonstration, small-scale pilots and the training of VET practitioners, the RPL methodology will be incorporated within workplace learning and courses that address high priority occupations, such as the ‘cage farming operative’. Feedback gathered from learners and the industry to evaluate demand will help to refine and improve the system after the ‘proof of concept’ and before ‘scaling up’. Consequently, the industry’s appetite for innovative ICT enabled learning that can enhance learning and digital skills through the use of modern ICT tools, will be gauged, recognized and nurtured. The general enhancement of digital skills resulting from their involvement with Optimal will also benefit the workforce as they can be transferred to aquaculture applications, such as surveillance, feeding systems and environmental management.

In summary, through its engagement, piloting and evaluation activities, Optimal aims to catalyze a concerted effort, between industry and VET providers, to equip the workforce with the knowledge and skills required to ‘do their job’ competently. This will be evidenced by the achievement of relevant and trusted qualifications built on the acceptance of prior learning and the recognition and documentation of skills developed informally. Consequently, the fish farming industry will be professionalized, raising its profile within the ‘blue economy’, in response to Blue Growth (European Union, 2012).

However, once in employment, most recruits cannot access a relevant skills development programs and suitable qualifications. A flexible provision is required that recognizes their prior learning gained on the job, informing an individual learning plan. However, there is a general lack of flexible and accessible work based VET and to date the industry has been catering for their workforce development needs, relatively unassisted. Company based training schemes, which are not quality assured or certificated and generally do not recognize prior learning, have proliferated. This is both ineffective and inefficient.

Therefore, Optimal proposes to test the application of ICT driven systems and other tools to the recognition of prior learning (RPL) and work based training delivery system, to determine and demonstrate the benefits. It is envisaged that this will lead to wider adoption of the technology and RPL approaches by both the fish farming industry and VET sector. 

This project has been funded with support from the European Commission. This publication reflects the views only of the authors, and the Commission cannot be held responsible for any use, which may be made of the information contained therein.