3. Innovating apprenticeships in Scotland
This chapter explores the policy responses to support innovation in apprenticeships in Scotland (United Kingdom). It presents emerging approaches to apprenticeship delivery and learning and teaching methods, as well as ways to attract apprentices, assess their skills and monitor outcomes using innovative technology. Finally, the chapter discusses the importance of providing strategic guidance and practical support for innovation in apprenticeships.
The importance of innovation and technology in the apprenticeship system
When it comes to apprenticeships, technology can play an important role in reducing the transaction costs of matching apprentices and employers, facilitating teaching and learning, and enabling better communication among relevant stakeholders. More broadly, innovation and technology can help to increase the number of apprenticeships; improve the quality of teaching, training and learning; and enable closer monitoring of the system. The COVID-19 pandemic has shown the benefits of using innovative technology for teaching and learning, especially in the context of school closures (OECD, 2020[1]; OECD, 2021[2]), but also highlighted the challenges faced by the vocational education and training (VET) sector where practice-oriented learning in workplaces, workshops and simulated work environments is the norm.
Technologies such as online learning, virtual/augmented/mixed reality and simulators, all provide opportunities to make training more accessible, flexible, safe, and efficient (Box 3.1 gives an overview of the different types of technology that could be used in vocational education). The use of technology in apprenticeships is expanding in several countries, including in Scotland, complementing or (partially) replacing more traditional forms of delivery and assessment along the apprenticeship journey. Nonetheless, there are opportunities for Scotland to accelerate the adoption of technology in education and apprenticeships. For example, although some Scottish learning providers benefit from well-established information and communications technology (ICT) infrastructure and systems for remote learning delivery, not all of them had invested sufficiently in their ICT infrastructure prior to the pandemic. Many colleges reported a range of issues in relation to connectivity, particularly in rural areas (Education Scotland, 2021[3]). In addition, the pandemic also highlighted the complexities of supplementing or replacing work-based learning opportunities.
To overcome barriers like these, strategic guidance and practical support for the effective use of technology will be crucial, as well as the involvement of both the private and public sector – employers, training providers, colleges, universities, research institutions and government. Those who provide teaching and training in classrooms and workplaces should play a role in ensuring that low-quality digital learning tools do not displace valuable instructional activities that could be better done without digital devices (OECD, 2021[4]). Drawing on international examples, this chapter focuses on how Scotland could use technology to expand and strengthen its apprenticeship system.
Artificial intelligence (AI) with sensors and learning management systems
When combined with sensors and learning management systems, AI can give teachers a sense of how their different students are learning, and where they are making progress or getting stuck. AI can help teachers, especially novice ones, read the classroom better and adjust the speed of teaching or stimulate students with techniques such as pop quiz questions. AI can also help with assessments, especially integrating learning and assessment using real-time data and feedback.
Virtual or augmented reality (VR/AR)
VR/AR supports the provision of practical training in a scalable, flexible and safe manner. It may supplement or replace existing training, as it improves the efficiency and quality of training. In the energy sector, for example, apprentices use virtual power plants, designed following industry standards, to simulate electrical failures and solve technical issues. VR applications in the aerospace industry allow industrial maintenance students more time to learn how to use physical equipment where a limited supply of equipment for training would otherwise limit their time. VR can also be used to develop soft skills, for example using realistic immersive scenarios that allow users to improve their communication and collaboration skills while interacting with colleagues, leading a team or selling a product to a client.
Learning analytics
Learning analytics can help teachers plan teaching and training better based on the real-time learning processes and results of students.
Robots
Robots can assist with a wide range of teaching and learning tasks. They can assist students with disabilities or different mother tongues than the instructional language. For example, vocational teachers use welding robots to introduce students to automatic welding. They show how welding robotic arms can be programmed using specialised software. They also demonstrate how car parts, metallic structures or industrial equipment can be welded using this technology. Automated welding can be more efficient than manual welding for repetitive tasks. In automated contexts the welder’s role involves handling some of the parts to be welded; programming, operating and troubleshooting the welding robot; and inspecting the quality of the final product.
Simulators
Simulators allow students to develop their ability to confront real-life challenging scenarios. In engineering, for example, students on the operation and control of engine rooms use simulation software. In the logistics and transportation sector, apprentices use simulators to learn how to drive a truck or operate a loader vehicle facing real-life issues. The maritime sector also widely uses simulators to train apprentices in the navigation and operation of vessels, both at sea and in ports.
Blockchain
Although no applications have yet been realised, blockchain has great potential to bring skills and qualifications together in a reliable, user-friendly credential system. Reskilling and upskilling could be facilitated by blockchain-verified qualifications.
Source: OECD (2021[4]), OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots, https://dx.doi.org/10.1787/589b283f-en; OECD (2021[5]), Teachers and Leaders in Vocational Education and Training, https://dx.doi.org/10.1787/59d4fbb1-en.
3.1.1. Innovative ways to identify and reach potential beneficiaries
Using skills intelligence to align apprenticeship provision to labour market needs
Aligning apprenticeship provision to labour market needs is one of the most difficult but important tasks in an agile apprenticeship system. Technology can help by providing relevant and timely data, including big data about labour market needs and data analytics (ETF, 2020[6]). Such datasets provide many opportunities and are increasingly being used, but also need to be used with caution (Box 3.2). Combining big data with complementary sources of skills intelligence will be key to generating statistically robust, detailed and policy-relevant evidence (Cedefop; European Commission; ETF; ILO; OECD; UNESCO, 2021[7]).
Scotland is doing well in assessing and anticipating skills needs. For example, Skills Development Scotland (SDS) systemically produces sectoral and regional skills assessments, focusing on employment, vacancies and new job openings. SDS already combines evidence from multiple qualitative and quantitative sources with real-time data to offer estimates of current and future skills needs. They are used to inform apprenticeship offers and other skill matters (SDS, 2021[8]).
However, stakeholder discussion in preparation for this OECD review highlighted that employers in Scotland still feel that there is a need to identify and address local and regional skills shortages in a more agile and responsive manner. Data on the match between the apprenticeships on offer and labour market demand are limited. It would be necessary to gather evidence to show whether apprenticeship starts are well aligned with the needs of the labour market (see also Chapter 2) and whether employers make forward-looking skills investments. For example, data on how well apprenticeship starts reflect current labour market needs such as in fast-growing sectors and occupations or strategic priorities for the future – ideally disaggregated by level of skills, sector and other targeted characteristics – could be useful for designing and guiding apprenticeship provision.
More could also be done to help employers and learning providers to access and understand the results of skills intelligence and to effectively use them for their skills planning and training provision – both in the short and long term. Skills intelligence could benefit not just those working on curriculum design and delivery, but also those working in areas such as career guidance and counselling, budget allocations for training programmes, human resources, and migrant workers. Scotland is currently building “skills and technology route maps” to help stakeholders use skills intelligence at the skills planning stage.
Real-time, online labour market data have great potential to improve the understanding of trends in skills needs. The advantages of web-based big data over conventional approaches include timeliness and granularity. However, such data tend to need more effort to prepare them for analysis than those collected using conventional approaches. The unstructured information provided often suffers from statistical, selection and conceptual biases. Despite advances in information and natural language processing and cloud computing, setting up a stable and well-functioning system for gathering, processing and analysing big data remains challenging. Developing such a system is complicated and resource intensive, but it can be beneficial in the long run.
Web-based big data cannot and should not replace existing skills intelligence methods and sources. Exploiting the complementarities between big data and other sources of skills intelligence is key in generating statistically robust, detailed, and policy-relevant evidence. It is the combination of artificial and human intelligence that will be key to further developing big data’s role in shaping effective vocational education and training and skills policies.
Source: Cedefop; European Commission; ETF; ILO; OECD; UNESCO (2021[7]). Perspectives on Policy and Practice: Tapping into the Potential of Big Data for Skills Policy, http://data.europa.eu/doi/10.2801/25160.
Matching and profiling potential apprentices, employers and learning providers
Technology cannot only be used to reduce mismatch between the apprenticeship offer and labour market needs overall, but also to better match learners, learning providers and employers. Technology can help connect them, by profiling individuals’ skills and aspirations, employers’ skills needs, and learning providers’ training offers, and finding the best matches between them. This can help reduce searching and matching costs and increase the effectiveness of matching skill needs and training.
Scotland has set up a comprehensive online apprenticeship portal called apprenticeships.scot. Among its many functions, the portal offers services to connect apprentices and employers, and provides information on apprenticeship jobs and funding opportunities, and general guidance and advice on apprenticeships. Although the majority of apprentices are not recruited via this portal – in practice many apprentices have been working for their employer for some months or years before they start their apprenticeship – hard-to-fill vacancies such as in the childcare sector are regularly advertised through it. In order to make this function more effective, the platform could use profiling technology. For example, data analytics and statistical profiling could help to target, identify and reach out to potential apprentices and employers. Statistical models can help governments identify target groups based on their policy goals as well as economic needs. Scotland already has various databases, including the records in SDS’s Customer Support System, which could be further exploited to that end.
Public employment services in various countries profile individuals and match them to services or interventions based on factors commonly associated with long-term unemployment and at-risk groups. Rather than relying on traditional one-to-one matching, more innovative profiling models are likely to become more prominent as big data become more widely available. Combined with new machine learning techniques, the models can use the data to improve their predictive power and target results more precisely, ideally in conjunction with a continuous dialogue between data analysts, policy makers and practitioners (Landeghem, Desiere and Struyven, 2021[9]). With more detailed data collection on students, apprentices and graduates, as well as learning providers and employers, profiling models could be used to identify individuals who could benefit from an apprenticeship, including early school leavers and job seekers. Flanders (Belgium) provides a useful example of a deep learning model using real-time labour market data and job seekers’ skills data to support people in taking up vocational training or finding jobs (Box 3.3). Likewise, on the employer side, data on employees’ skills and employers’ training systems can show which enterprises could benefit from offering apprenticeships and/or need support to engage in apprenticeship provision. Box 3.4 describes Germany’s self-assessment tool for companies to use to identify how far they have progressed towards Industry 4.0, as well as to analyse their employees’ skills, their own efforts to develop skills and their training practices.
The Flemish Employment and Vocational Training Service (VDAB) helps residents of Flanders find jobs and take vocational training, by using machine learning (ML). VDAB recently partnered with Radix.ai, a Belgian ML startup, to make the matching process more effective and efficient. This initiative uses the VDAB data contained in CVs and job postings and applies ML to provide better-targeted matches for VDAB users. Deep learning, a subset of ML, enables machines to mimic human behaviour, and in order to train the deep learning model, VDAB regularly uploads new vacancies and CVs to their storage engine. With each new dataset, the engine learns how the job market evolves, noting changes in job demand and how trends shift over time.
The deep learning model also learns how jobs are spoken of and what the changing interplay of words means. For example, “data scientist” is a relatively new job, related to the roles of machine learning engineer, data analyst and even AI architect. The model allows machines to learn the meaning of words and continue to improve matching quality. Based on word relationships and the interests and behaviour of the users, job matches are more closely aligned to the aptitudes, talents and preferences of job seekers.
Source: Amazon Web Services (2021[10]), AWS Partner Story: VDAB & Radix.ai, https://aws.amazon.com/partners/success/vdab-radix-ai/
These identification and matching tools could be combined with existing tools for raising awareness about and the attractiveness of apprenticeships and should not replace more traditional efforts to promote and strengthen the image of apprenticeships.
Germany’s self-check for businesses helps small and medium-sized enterprises (SMEs) check how prepared they are for Industry 4.0 (see Box 3.10 for information on Germany’s Industry 4.0 initiative). This benchmarking and assessment tool uses a survey on business characteristics and practices, as well as key performance metrics, to identify how far the business is progressing towards Industry 4.0. Businesses input details on their structure and organisation and receive detailed feedback. The tool is well-known among mechanical engineering and manufacturing industries as the sector was heavily involved in its development.
The questionnaire contains two parts: basic information about the firm, including its sector, revenue, and number of employees, primarily to ensure the representativeness and projections, and details about business practices related to industry 4.0. These questions identify a set of indicators across the six modules of the Industry 4.0 Readiness Model, including analysing employee skills in various areas and company’s efforts to acquire new skill sets and training practices. The tool then provides a scorecard and rating of the business’ level of Industry 4.0 readiness.
The benchmarking tool was launched at the German Mechanical Engineering Summit in Berlin in 2015. Nearly 7 000 firms completed the check between 2017 and 2019. About 45% of these respondents were assessed at Levels 2 (beginner) and 3 (intermediate). Only 1.6% of firms achieved Level 4 (expert) and none reached Level 5 (top performer). The average level mechanical and plant engineering was 1.4 and the average in manufacturing was 1.3. Overall, the lowest levels were achieved in the Smart Factory and Data-driven services dimensions at 0.8 and 1.1 respectively. The dimension with the highest assessment score was Employees at 1.7.
Source: OECD (2020[11]), "Digital business diagnostic tools for SMEs and entrepreneurship: A review of international policy experiences", https://doi.org/10.1787/516bdf9c-en.
3.1.2. Promoting and supporting technology-enabled apprenticeships
The benefits of using technology in apprenticeship provision
The COVID-19 pandemic provided an impetus for innovation by forcing education and training providers to use technology to ensure continuity of learning during school closures, including in vocational education. Many countries have increased the share of online vocational education programmes (Briggs, López and Anderson, 2021[12]). The pandemic gave many workers and students a chance to work and study remotely – as work goes virtual, so too must apprenticeships. Learners in Scotland have since highlighted how much they appreciate, and have benefited from, increased and flexible online access to support functions. However, the pandemic also highlighted some challenges, including around digital skills and connectivity issues – which are not specific to apprenticeships but could have an impact given the increasing use of online courses relevant to apprenticeships. According to Education Scotland (2021[3]), a number of colleges in Scotland identified digital skills gaps among individual learners and staff, as well as issues with access to digital equipment and connectivity. In most colleges, learning resources and activities were also made available as physical resource packs, delivered directly to learners, to support those facing barriers to accessing remote learning such as poor connectivity or digital poverty (Education Scotland, 2021[3]).
The benefits of more and better technology use in education go beyond the pandemic, and this is also the case for apprenticeships. Technology can help increase the accessibility of apprenticeships (Box 3.5). Online and virtual learning can improve access for learners in remote areas by providing remote connections to learning providers and employers – as long as Internet connectivity is up to standard. For example, the Open University in Scotland offers flexible distance-learning opportunities, including Graduate Apprenticeships in software development and cyber security. It reaches out to employee-apprentices working in remote areas such as the Shetland Islands, far off the northern Scottish coast. Another example is e-Sgoil, a Scottish initiative that promotes online learning to improve equity and access across the Western Isles, supporting Foundation Apprenticeships (e-Sgoil, 2021[13]). In Norway, apprentices in rural settings have successfully completed certain programme requirements through e-learning platforms (OECD/ILO, 2017[14]). Other types of technology can also make apprenticeships more accessible to students with disabilities that may have prevented them from following certain pathways in the past. For example, AI systems can help students overcome obstacles, such as through text-to-speech or speech-to-text applications or wearables to help visually impaired students read books (Vincent-Lancrin and van der Vlies, 2020[15]).
More advanced technology can help to diversify training options, by overcoming material shortages that might otherwise limit what governments and learning providers can offer to students and how students can progress. For example, virtual or augmented reality (VR/AR) and simulators, can enable students to develop vocational skills by performing specific tasks like operating heavy machinery, learning how to repair a car engine, or testing chemical products in a laboratory (OECD, 2021[4]). In such cases, it may be cheaper and safer to use simulators or VR/AR than traditional laboratories that are expensive to set up, maintain and update.
Technology can also increase the effectiveness of learning in terms of learning progress and outcomes. According to a World Bank study, VR training is more effective on average than traditional training in developing technical, practical and socio-emotional skills; it is particularly promising in fields of health and safety, engineering and technical education. Students who had VR training used inputs and time more efficiently and/or were better at avoiding performance errors than students receiving traditional training. For each additional hour of VR training, students scored 3% higher in technical learning assessments than those exposed to the same content delivered through traditional methods (Angel-Urdinola, Castillo-Castro and Hoyos, 2021[16]).
Another potential benefit of technology is that it can be used to provide personalised support to learners and teachers. Learner tracking systems, where teachers and trainers have detailed information on learners, can improve the quality of training provision, similar to the player-level analytics available to a professional sports coaching staff. Such systems can provide teachers and trainers with information that they may have neglected during lessons due to their workload or other systemic, technical, or institutional issues. Data analytics and statistical profiling models can be also used to identify students at risk of dropping out, using the administrative micro-data that are increasingly being collected by education systems and organisations. While identifying a good set of early warning indicators remains difficult, a few systems have shown a high level of accuracy and enriched thinking about the reasons students drop out (OECD, 2021[4]). These techniques may help prevent students dropping out, detect potential problems, and provide opportunities to intervene earlier. For example, in the United Kingdom, predictive analytics tools can be used to identify high-risk programmes and learners by measuring trends in learner engagement and motivation (Box 3.5).
Finally, technology can also be used to reduce administrative and repetitive tasks involved in apprenticeship management, such as managing admissions and school allocations, assessment reports, proctoring systems, and resource allocation and planning (OECD, 2021[4]). The Scottish apprenticeship system is already making use of such technologies to some extent.
While there are many potential benefits to the use of technology, it also poses challenges and traditional face-to-face learning will in certain cases remain the preferred option. Increasing the use of technology should be a means to an end and not an end in itself. Hence, technology and innovation should be carefully embedded into curricula and pedagogy with the aim of achieving a higher level of performance. Any new approach by training employers and providers should be supported by research and development, and supplemented by learner performance analytics on progression. Although World Bank research (Angel-Urdinola, Castillo-Castro and Hoyos, 2021[16]) found that VR training can be as effective as traditional training methods, its effectiveness differs across sectors and subjects.
Scotland (UK): Online apprenticeships, blockchain technology, virtual learning and e-portfolios
The Open University (OU) works with employers to offer online Graduate Apprenticeships in cyber security and software development. Learning material includes online activities, eBooks, video and audio resources, OU Anywhere App, and face-to-face and online tutorials. The OU provides apprentices with a support network: an apprenticeship programme manager, practice tutor, line manager/mentor, academic tutor and student support team. It also provides pre-programme learning and induction. As part of the QualiChain project, the OU’s Knowledge Media Institute is planning to use blockchain technology to allow learners to manage and verify their educational and employment qualifications. The project aims to develop an open-source platform to capture learners’ data and a decentralised approach to archiving, managing and verifying them.
Another example is BAE Systems, a global defence, aerospace and security company, which provides apprenticeships in Scotland and was graded excellent in SDS’s quality assurance assessment in 2019-20. The company has well-equipped training rooms that provide a modern training environment. A high-tech Virtual Experience room enables apprentices to "walk round" a ship while still in the training suite. The Learning Assistant e-portfolio option is available to all apprentices to complete and upload evidence, which supports them to progress their portfolio and complete their qualification. Emails and messaging facilities in the E-portfolio system provide helpful communication channels between apprentices and the skills and training team.
Likewise, Ident Training Ltd is a vocational learning provider for dental nurses, which was graded excellent in SDS’s quality assurance assessment in 2019-20. It meets businesses and learners’ needs by providing hybrid learning: e.g. Saturday morning classes and evening Skype calls together with online learning platforms to provide flexible training options depending on location and work patterns. The provider also uses an e-portfolio system to enable employers and apprentices to access information on the learning progress of apprentices and also developed an online resource site for staff, apprentices and employers (e.g., classes are saved and uploaded on the resource site). The provider’s classroom facilities include a fully equipped IT suite with interactive smartboard.
Canada: Flexibility and Innovation in Apprenticeship Technical Training (FIATT) project
The FIATT project funded ten pilots that experimented with alternative delivery approaches such as a combination of online learning with classroom learning, mobile training units, instructor support, and simulators. Courses were created for the trades of carpenter, construction electrician, gasfitter, heavy-duty equipment technician, mobile and tower crane operators, plumber, refrigeration and air conditioning technicians, steamfitter/pipefitter and welder.
According to an evaluation (2015-18) by the Canadian Apprenticeship Forum, more FIATT apprentices relocated and incurred fewer costs when relocating, compared to non-FIAAT apprentices. FIATT apprentices were more likely to agree that their training was flexible. They missed fewer hours of work and reported fewer lost earnings. The following defining success factors were identified: engaging employers to be aligned with the latest industry standards and workplace practices, involving faculty in programme development, connecting trades instructors and IT specialists to make engaging online learning content, and structuring online courses to offer reminders about assignments to support apprentice progression. However, connectivity and capacity in remote and rural communities were challenging.
The United States: Micro-industry engagement
In Louisiana, hands-on teacher support is combined with technology to connect rural students with employers. A major component of this micro-industry engagement is a strategic partnership with Nepris, a company that virtually connects schools, teachers and students with workplace experts and professional mentors. Through this partnership, teachers have engaged industry experts to conduct interviews with students, provide feedback on a capstone or other project, or judge student competitions. The micro-industry engagement is intended to be a series of cumulatively structured engagements and is designed around four key tenets: 1) virtual access to workplace experts in every industry sector; 2) teachers are empowered with the technologies and curated instructional resources; 3) schools offer virtual and in-school exercises akin to onsite workplace-based learning; and 4) students prepare with workplace experts, mastering sophisticated communication skills.
Singapore: Virtual and augmented reality
The Institute of Technical Education (ITE), a principal provider of vocational education and training (VET) in Singapore, introduced two types of immersive VET technologies: 3D virtual reality (VR) and augmented reality (AR).
3D VR technology facilitates access to real-world work sites. For example, students enrolled in the Marine and Offshore Technology course used a multi-wall 3D VR system to practise their skills on a simulated oil rig platform. Using this technology, students were able to safely train for adverse weather conditions and learned to adjust to a variety of environmental conditions and associated job hazards.
3D AR applications enable students to interact with real world environment using real time data, contextualising knowledge for just-in-time learning. For example, students on the Aerospace Technology course were able to load 3D aircraft engine models into their mobile devices and watch simulations of these engine parts in the AR viewer. These 3D simulations helped them to visualise details of complex systems and the operational flow inside the equipment. The schools worked closely with technology solution providers to design relevant learning activities for students, based on the curriculum requirements.
The United Kingdom: Predictive analytics to indicate dropout risks and learning progress
Predictive analytics (PA) can identify the various profiles or combinations of factors which might indicate, for example, the likelihood that an apprentice will drop out. Computer algorithms can use big data to find the patterns which predict an outcome, for example the responsiveness of tutors, determining how quickly work is assessed and returned to apprentices. PA has the potential to match tutors with individual learners and provide high-level reporting of retention risks across groups of apprentices, by measuring trends in learner engagement and motivation. PA can also be used to support tutors in managing their workloads.
In the United Kingdom, one-stop apprenticeship management apps such as Aptem offer these services with built-in machine learning functionality. Aptem identifies issues among programmes or learners through its early warning system and manages the apprenticeship programme from start to finish, generating rich data to assess the pace and progress of personalised learning.
Source: Open Univ; CAF-FCA (2019[17]), https://caf-fca.org/wp-content/uploads/2019/01/CAF-FIATT-Report_EN_F.pdf;
The Open University (2022[18]), New grant to develop blockchain technology for learning, www.open.ac.uk/research/news/new-grant-develop-blockchain-technology-learning#:~:text=The%20OU's%20Knowledge%20Media%20Institute,their%20educational%20and%20employment%20qualifications; SDS (2020[19]), Quality Assurance Assessment 2019-20: BAE Systems Ltd, https://www.skillsdevelopmentscotland.co.uk/media/47146/bae-systems-qa-report-may19.pdf; SDS (2020[20]), Quality Assurance Assessment 2019-20: Ident Training Ltd, https://www.skillsdevelopmentscotland.co.uk/media/47162/ident-training-ltd-qa-report-dec19.pdf; UNESCO (2017[21]), Beyond Access: ICT-enhanced Innovative Pedagogy in TVET in the Asia-Pacific, https://bangkok.unesco.org/sites/default/files/assets/article/ICT%20in%20Education/TVET/TVET%20pub.PDF; Advance CTE (2020[22]), CTE Distance Learning in Rural Communities, https://cte.careertech.org/sites/default/files/documents/factsheets/CTE_Distance_Learning_Rural_Fact_Sheet_2020.pdf.
ILO (2020[23]), ILO Toolkit for Quality Apprenticeships. Volume 2 Guide for Practitioners: Innovations and strategies in apprenticeships, https://www.ilo.org/wcmsp5/groups/public/---ed_emp/---ifp_skills/documents/publication/wcms_751118.pdf;
Abrahams (2018[24]), Patron Think Piece - Aptem: Smart machines can transform apprenticeships, https://www.aelp.org.uk/news/news/think-pieces/patron-think-piece-aptem-smart-machines-can-transform-apprenticeships.
Assessing the current status of advanced technology in the apprenticeships system
The use of technology and innovation for apprenticeship teaching and training is growing in Scotland but more could be done to fully take advantage of the benefits described above. The Enterprise and Skills Strategic Board sub-group recommended supporting the expanded use of technology in apprenticeships, at least for off-the-job training, within the context of the COVID-19 pandemic (ESSB, 2020[25]). A key first step would be to assess the status of technology use in Scottish apprenticeships and identify which parts of the system could make more and better use of it. For example, the Netherlands undertook a survey to identify how different technologies are used for different training objectives as well as the barriers to the use of innovative technology in apprenticeships (Box 3.6)
In the Netherlands, the use of digital tools and innovative technologies for teaching is widespread among VET institutions. According to a recent survey (2019), around half of the programmes used online courses, digital portfolios or digital learning materials and environments (DLM/E) and adaptive personalised DLM/E, but few institutions, programmes or teachers used AI, digital badges1 gamification and VR. The main barriers to the use of innovative technology were vocational teachers’ lack of ICT skills, time and ownership, and institutions’ lack of vision and objectives. This survey was run with 307 individuals in 53 VET institutions, representing 83% of all Dutch upper secondary VET institutions.
Note: “All institutions” refers to the 53 participating VET institutions (i.e. 83% of all Dutch VET institutions). Restricting the sample to those institutions that have at least 5 respondents in the survey reduces the number of institutions covered to 23.
1. A digital badge is an online-based visual representation that uses detailed metadata to signify learners’ specific achievements and credentials in a variety of subjects across K-12 classrooms, higher education, and workplace learning (Muilenburg and Berge, 2016[26]).
Source: ECBO (2019[27]), Onderwijsinnovaties met moderne ICT in het mbo, https://ecbo.nl/wp-content/uploads/sites/3/Rapport-Onderwijsinnovaties-met-moderne-ICT.pdf.
The European Commission’s SELFIE (Self-reflection on Effective Learning by Fostering the Use of Innovative Educational Technologies)1 is a free online tool designed to help education and learning providers in Europe and beyond embed digital technology into teaching, learning and assessment. SELFIE anonymously gathers the views of students, teachers, in-company trainers and school leaders on how technology is used in their school. Their input can be used by schools to formulate an action plan and set priorities to implement changes to support teaching, learning and student assessment (Hippe, Brolpito and Broek, 2021[28]).
The results from such an assessment can be used to develop an innovation plan for the apprenticeship system. Implementing innovation requires strong leadership within training institutions, well-trained vocational teachers and trainers, strong co-ordination with employers and technology-solution developers, and of course adequate financial resources (OECD, 2021[5]). The example of the Netherlands (Box 3.6) shows that technology use in apprenticeships depends on the capacity of employers and training providers to put it to effective use. Ultimately, the use of technology in apprenticeships is only possible if training employers and providers are both interested and have the capacity, resources and tools to implement the innovation (see Section 2.3).
Enabling vocational teachers and trainers to make the most of technology
Vocational teachers and in-company trainers play a role in finding the right balance between in-person teaching and remote, online, or other use of technology (OECD, 2021[4]). They need to carefully balance the pros and cons of technology use and its effectiveness, but also the costs and benefits of using technology versus traditional methods. Training providers and employer may require additional support to be able to make informed decisions.
The extent to which teachers and in-company trainers will effectively integrate new technology into their activities depends strongly on their digital and pedagogical skills. However, the evidence from OECD countries is that a significant share of VET teachers do not use digital technologies for teaching or do not feel confident in doing so. Around one in four VET teachers using the SELFIE tool in OECD countries do not feel confident using digital technologies in classroom teaching, or when providing feedback to their students. Confidence using technology is lower among older VET teachers: around 82% of VET teachers aged 30 to 39 feel confident preparing lessons using digital technology, and 76% of them feel confident using technology in class teaching, but those figures fall to 59% and 53%, respectively, for teachers over 60 (OECD, 2021[5]). Likewise, younger vocational teachers are more likely to use digital resources than their older peers (Figure 3.2). For instance, almost 70% of teachers under the age of 30 use digital technology as part of their teaching to engage their students or to foster their creativity, compared with only slightly more than 50% of those over 60 (OECD, 2021[5]).
Teachers and trainers in apprenticeship programmes can make more effective use of innovative technologies in the delivery of apprenticeships if they have the interest and capacity to do so and the right resources and tools. Therefore, teachers and trainers should be supported, and able to participate in professional development in this area (e.g., data and teacher tracking system can offer teacher feedback for self-regulation). It is important to ensure that older teachers and trainers do not get left behind in this transition.
Note: All percentages refer to the share of high responses (i.e. 4 and 5 on a 5-point-scale). Participation in SELFIE is anonymous and voluntary, thus the data are not representative, see disclaimer in the source.
Source: OECD (2021[5]), OECD Reviews of Vocational Education and Training, https://dx.doi.org/10.1787/59d4fbb1-en (using data extracted from the SELFIE database in the period October 2018-December 2020).
Recommendations and implementation for Using innovation and technology to further expand and promote apprenticeships
To expand and promote apprenticeships, Scotland should take further steps to make the most of the opportunities offered by technology and innovative approaches.
Make better use of labour market intelligence using automated data analytics and forecasts to align apprenticeship provision with labour market demand. Smarter use of matching platforms could help connect potential apprentices and employers and reduce search and matching costs. Scotland is doing well in these areas but could do more to help employers and learning providers to easily access and understand the results of skills intelligence and use them in their skills planning and training provision. These data should be also used to better inform apprentices and employers about the benefits of and need for apprenticeships.
Develop statistical profiling and modelling techniques in the apprenticeship system to help identify and reach out to potential apprentices and employers. The use of such techniques is currently limited but is likely to become more prominent with the increasing availability of big data, combined with new machine learning techniques. Scotland could consider investing in this area, building on its work monitoring those completing their apprenticeships (see Section 3.2).
Help employers to evaluate their skills and training system and the need to provide apprenticeships, building on existing technology such as business diagnostic tools. Such diagnostic tools would need to be developed in collaboration with relevant experts to ensure their relevance and effectiveness.
Based on an assessment of which approaches are most likely to increase accessibility for both employers and apprentices, improve quality, and address physical and resource challenges, the sector will need support to put technology to effective use.
Promote the benefits of using technology in apprenticeships, including online, VR/AR and simulators, learner tracking systems and apprenticeship management systems to all relevant stakeholders. Implementing innovation requires strong leadership within training institutions, well-trained vocational teachers and trainers, strong co-ordination with employers, and adequate financial resources and quality assurance mechanisms.
Set appropriate guidelines for learning providers and in-company trainers on the right balance between in-person teaching and remote, online or other use of technology that takes the place of human interaction. These guidelines should be supported by research and reviewed and updated based on analysis of learner performance and progression.
Provide professional development opportunities to teachers and trainers to support them in making effective use of technology in their practice. Particular attention should be paid to ensuring that older teachers and trainers do not get left behind in this transition. This will require strategic guidance and practical support (see Section 2.3).
Assessment in apprenticeships – and in technical education and training more broadly – can be complex for learning providers, employers and assessors, especially when it involves authentic work tasks, synoptic assessment and grading (Field, 2021[29]). Technology can provide more time- and cost-effective ways of assessing practical skills, and can help streamline the process.
Similarly, technology can improve the monitoring of quality and outcomes in the apprenticeship system, by providing more accurate, timely and detailed information on the labour market outcomes of apprentices. Monitoring can be more rigorously and effectively conducted using advanced analytical approaches and technologies that facilitate data collection and analysis. This information can help to make the apprenticeship system more responsive (Chapter 2) and inclusive (Chapter 4).
3.2.1. Using innovative approaches for the assessment of apprenticeships
Using technology to facilitate apprenticeship assessment
Assessment in apprenticeships in OECD countries often involves evaluation of both theoretical knowledge and practical and technical skills. Assessment of practice-oriented and employability skills in an authentic working environment is often not straightforward, due in part to material and equipment costs (Kis and Windisch, 2018[30]). In addition, certification and qualification processes may involve collecting many pieces of evidence about the competences achieved, which can be administratively burdensome and complex as standards and frameworks evolve. This is particularly the case when different stakeholders are involved in assessing and providing evidence at different phases of an apprenticeship.
Technology can create innovative, cost-effective and predictable ways to assess practical skills and collect the necessary evidence. Digital and smart technology is increasingly being used in the assessment of education and training outcomes, including in apprenticeships, and in compiling e-portfolios of skills. For example:
Multi-functional e-platforms that facilitate apprenticeship processes including assessment and certification, can reduce administration and communication costs and facilitate communication and data collection, as in the case of Norway (Box 3.7). Mobile logbooks allow apprentices to record and demonstrate their learning and training progress, including details such as hours worked, tasks performed and equipment used. Likewise, (self-) assessment platforms can be used to assess and broaden apprentices’ competences and prepare them for summative assessments or examinations (ILO, 2020[23]). As hinted in Box 3.5, apprenticeship providers in Scotland also use e-portfolios or equivalent online platforms to undertake these tasks.
Online exam tools or online assessment platforms can reduce the work of assessors by creating exam questions to test competence-oriented tasks and compile them into an overall exam. Germany is developing a prototype for such a tool (Winther, 2021[31]) (Box 3.7).
Smart technologies and smart data analysis techniques enable assessments to be broadened to take in skills that cannot be easily measured by conventional tests. For example, game-based tests can measure higher-order skills (e.g. creativity) or emotional and behavioural skills (e.g. collaboration, behavioural strategy), and analyse eye-tracking data and audio recordings, and process natural language and information such as time-on-task (OECD, 2021[4]). In Scotland, the Thales group already uses online game-based assessment when recruiting apprentices.
Blockchain technology can open new avenues for credentialing in apprenticeships as a form of “verification infrastructure”. It enables claims about an individual or institution, including their characteristics and qualifications, to be verified instantly and with a very high level of certainty. This helps eliminate diploma and other record fraud; facilitates the movement of learners and workers between training institutions, workplaces and jurisdictions; and empowers individuals by giving them increased control over their own data. Many blockchain initiatives are underway across the world, which may transform how apprenticeship systems – as well as entire skills systems – manage degrees and qualifications (OECD, 2021[4]). Several European countries are advancing in this area, including Scotland (see Box 3.5). Micro-credentials such as digital badges are becoming increasingly common. Several sector bodies in Scotland offer modularised training courses and micro-credentials to certify professional skills acquired in those courses (e.g. Scottish Social Services Council), which may be suitable for integration into apprenticeship courses, assessments and certification. There is also work being undertaken by QAA Scotland via a sector-wide project entitled “Understanding Micro-credentials and Small Qualifications in Scotland” to explore and understand the potential of micro credentials and digital badging (QAA Scotland, 2021[32]).
Maximising the use of technology in assessment
Despite its potential, the use of technology for assessments is relatively uncommon compared to aspects of apprenticeships. For example, only 56% of VET teachers using the SELFIE tool in OECD countries use digital technologies for assessment purposes (Figure 3.2) (OECD, 2021[5]). Evidence is scarce in Scotland, but while online assessment has already been used sporadically, there is room to explore more advanced uses of technology-enabled assessment and certification. For example, the Federation for Industry Sector Skills and Standards (FISS) in the United Kingdom manages an apprenticeship certification system to reduce the cost and bureaucracy associated with certification. This kind of process could be integrated with an apprenticeship management system to better link the learning process and outcome assessment. Caution is needed when expanding these kinds of innovative approaches. For example, it is important to be transparent about how accurate technological systems are at measuring, diagnosing or assessing and to consider data protection requirements. At the same time, the limits of technologies should be fairly measured against the limits of human beings performing similar tasks.
Close collaboration between employers, trainers and assessors – as well as strategic support from government – is a fundamental step prior to making any decision about implement technology solutions for assessment.
Apprentice learning progress management systems
Online logbooks can help track apprentices’ progress in real time through tools such as an interactive dashboard. One example, log.work, has an integrated messaging platform allowing real time collaboration to enable easy communication between apprentices, work placement supervisors and assessors. The logbooks can also be customised to suit a specific qualification structure. Dashboards function for apprenticeship management offer digital portfolios (or e-portfolios) that store documents; collect the evidence needed for assessment; and track apprentices’ knowledge, skills, behaviours and off-the-job hours. Apprentices can keep their logbook up to date from their mobile devices or computers and can add photographic evidence of completed work. Employers can minimise time spent managing apprentices and manage all apprentice placements from one screen. Assessors can easily sort log entries by relevant unit and use a simple rating system. It can also inform about on-the-job and off-the-job training and facilitate the information exchange between training providers and employers, and update the individual learner plan upon progress.
Germany: technology-based assessment in vocational training (ASCOT)
The ASCOT (Technology-based Assessment of Skills and Competences in Vocational Education and Training) research initiative was launched by the Federal Ministry of Education and Research in 2011. It aimed to develop valid methods for the technology-based assessment of vocational skills and competences at the end of VET. The initiative involved several co-operative projects between experts in the fields of science and practice. It focused on five occupations: 1) motor vehicle mechatronics technician; 2) electronics technician for automation technology; 3) industrial clerk; 4) elderly care; and 5) medical assistant. The projects developed assessment instruments based on real-life situations. For instance, apprentices in mechatronics had to diagnose engine defects in computer-based simulations. Apprentices training to be medical assistants were confronted with a virtual doctor’s office simulating real-life scenarios and interactions with patients.
The ASCOT instruments proved highly suitable for the assessment of large areas of occupational competence such as technical and professional competences, occupation-specific social and communication skills, and occupationally relevant literacy and numeracy skills. The instruments also increased the objectivity of assessments, improved the test motivation of examinees thanks to the use of multimedia and interactive content, and were more efficient than traditional assessment instruments. This could offer both time and cost savings in the long term. Since 2018, the follow-up initiative ASCOT+ has been developing digital measuring instruments for professional and cross-professional competences in three professional fields and testing them as teaching and learning media and in examinations. This development took place in close co-operation with social partners, the relevant authorities, bodies that develop examinations, companies and vocational schools.
Norway: OLKWEB, an e-platform
In Norway, apprentices are able to complete their training requirements, provide documents and access government assistance through specialised e-platforms. One popular system known as OLKWEB has been optimised for use by training offices, who are able to follow up on their apprentices and generate reports that document the apprentice’s activities and outputs. Learning providers are able to perform a number of key functions, including:
Analyse and monitor the apprentice’s progress through curriculum goals provided through traditional means or through the use of films, images and mobile apps.
Apprentices are also able to interact with each other through the system, and can use the interface to record meetings and receive information. The employer is also able to monitor the apprentice’s progress in off-the-job training. In the extremely rural area of Nordland, the customised apprentice interface allows apprentices to fulfil their training requirements without travelling vast distances. E-platforms also remove administrative burdens and allows young people to complete their apprenticeship requirements flexibly.
Italy: Learning support system (Atlas of Work and Qualifications)
Italy’s learning support system, Atlas of Work and Qualifications, helps in the planning of training offers and the evaluation of offers in relation to labour market skills needs. It is a support tool for employability and lifelong learning services and can be used for the analysis of the organisational and productive evolution of a sector; planning support training; assessment; the recognition of educational credits; the Identification, Validation and Certification (IVC) processes acquired in non-formal and informal learning contexts; and job orientation, including profiling services and skills assessment. It was developed in collaboration with employers and trade unions, bilateral representatives, professional associations, sectoral experts and stakeholders in the work-learning system.
Source: Cedefop (2021[33]), Italy: guidelines for the certification of competences, www.cedefop.europa.eu/en/news-and-press/news/italy-guidelines-certification-competences?src=email&freq=weekly; INAPP (2019[34]), Atlas del lavoro e delle qualificazioni, https://atlantelavoro.inapp.org; BMBF (2021[35]), ASCOT, https://www.ascot-vet.net/ascot/de/ascot-projekte/ascot-projekte_node.html;jsessionid=169A2384E7D2E2A2733580B40B471F25.live092; Ștefӑnicӑ et al (2016[36]), Technical and Vocational Education and Training: Issues, Concerns and Prospects, Competence-based Vocational and Professional Education; OECD (2017[14]), Engaging Employers in Apprenticeship Opportunities: Making It Happen Locally, https://dx.doi.org/10.1787/9789264266681-en.
3.2.2. Using data to monitor the outcomes of apprenticeships
Scotland has made a significant progress in using data to monitor both the education and labour market outcomes of apprentices, and costs and benefits to employers. The Education and Skills Impact Framework (ESIF) initiative was implemented in 2019, building on SDS’s Apprenticeship Long Term Outcome (ALTO) Framework, which followed the 2016 OECD recommendations for apprenticeship evaluation (Bajgar and Criscuolo, 2016[37]). ESIF will provide estimates of the economic and social impact of investment in post-school education and skills for individuals, employers and the public purse (Box 3.8).
Prior to the initiative, Scotland published the Modern Apprenticeships (MA) Outcomes Reports (2012, 2016) based on telephone surveys of apprentices, which asked about outcomes such as skills, career progression and employment, and the MA Employer Survey 2015. These self-report studies can be unreliable measures of impact because respondents might have consciously or unconsciously adjusted their answers to what they expected the evaluator hoped to hear, and also because it is difficult for respondents to judge what the outcome would have been without participation (Bajgar and Criscuolo, 2016[37]) . In addition, the surveys contacted apprentices within six months of completing their training and so did not provide information on longer-term outcomes. The OECD recommendations advised that these surveys should instead provide information on the operation, costs and form of training provided (Box 3.8). More recent surveys, including the Apprenticeship Employer Survey (SDS, 2020[38]) and Modern Apprentice in Training (SDS, 2019[39]) are moving in this direction and continue to provide useful information.
The new ESIF evaluation results, which are based on administrative data linkages, are expected to provide additional and more robust insight than self-reported impacts, with a focus on longer term outcomes (up to seven years after completion of the apprenticeship). Scotland’s next step should be to use these results to refine its apprenticeship instruments and continue to update the studies as new cohorts of apprenticeship data and other linked administrative data become available.
The ESIF also includes a cost-benefit analysis which could be used to benchmark against other countries. For example, in Germany, BIBB conducts a representative survey on the costs and benefits of apprenticeships every five years. The latest survey found that on average the net cost to companies was EUR 6 478 per apprentice for the 2017/18 training year (totalling EUR 8.4 billion), without considering savings from recruitment costs and other long-term benefits. Around 28% of trainees generate net income for employers during their training period while the remaining trainees were only worthwhile to employers in the medium or long term. Switzerland also produces cost-benefit analysis at the economy, programme and firm level, using intensive data inputs and monitoring mechanisms (SERI, 2020[40]). Korea also occasionally monitors the cost-benefit of apprenticeships, with the most recent results showing that the net present value for Korean society was estimated at approximately USD 2.3 billion for the period of 2016-20 (KRIVET, 2019[41]). Such analyses can help engage employers and learners in the apprenticeship system.
SDS worked with the OECD to develop the evaluation framework and began to implement it through Apprenticeship Long Term Outcome (ALTO) Framework, which largely forms the basis of the post-school Education and Skills Impact Framework (ESIF).
2016 OECD recommendations for Modern Apprenticeship evaluation:
Monitoring inputs and outputs: Input data include public funding contributions, SDS costs attributable to administering MAs, training and administrative costs paid by employers and the amount of time apprentices spend in training rather than in productive work. Output data include the number of MA starts, leavers and apprentices in training, and MA completion rates. Additional outputs which SDS should consider monitoring are apprenticeship duration and form of training (on-the-job, off-the-job and learning by doing). Reported statistics should be disaggregated by level, framework or framework group and key apprentice characteristics.
Evaluation of process and participant satisfaction: Process evaluation could be based on a combination of interview- and survey-based evidence. The MA Employer Survey and MA Outcomes Survey should move away from the self-reporting of impacts and towards feedback on the operational aspects of MAs, information on the form of training and information on employers’ administrative costs.
Evaluation of impact on individuals and employers:
Individual-level evaluation should primarily rely on linked administrative and Office of National Statistics (ONS) data and investigate the effect on labour market and education outcomes. To provide the most robust results, it should compare individuals who complete MAs with two different control groups: those who never started an MA (never-starters); and who started but did not complete an MA (non-completers).
Employer-level evaluation should make use of existing firm data (e.g. the Inter-Departmental Business Register and the Annual Business Survey) which can be linked to records held by SDS. It should examine the effect of MAs on productivity. It is important to distinguish between the effect of apprentices during their training (which may be negative) and after the training is completed. Estimates should be strengthened by comparing firms employing apprentices to other firms with similar characteristics.
Cost-benefit analysis: These should produce estimates of the value that MAs generate for the Scottish economy each year. The benefit should include employment and productivity increases due to MAs and, if feasible, indirect benefits of MAs such as reducing skill shortages in the economy. The cost should include the direct training costs paid by SDS and employers, the indirect costs due to time spent in training rather than productive work and administrative costs.
Source: Bajgar and Criscuolo (2016[37]), "OECD Evaluation Framework for Modern Apprenticeships in Scotland", https://doi.org/10.1787/59084781-en; Bajgar and Criscuolo (2018[42]), “Designing evaluation of Modern Apprenticeships in Scotland”, https://doi.org/10.1007/978-3-319-78461-8_18.
ESIF measurement framework
Building upon the 2016 OECD recommendations and the subsequent ALTO framework, Scotland is working on ESIF to provide estimates of the economic and social impact of investment in education and skills for individuals, employers and the public purse. This is the first initiative to gather comprehensive, consistent evidence on impact and return on investment (ROI) in the post-school education and skills system in Scotland. This focuses on SCQF level 4 and above, including apprenticeships.
Recommendations and implementation for innovating assessment and monitoring of performance and outcomes
Digital technology brings opportunities to facilitate apprenticeship assessments and certification. Scotland can take advantage of these opportunities to reduce the costs of communication, administration, and data collection borne by practitioners in the process of apprenticeship assessments:
Maximise the effective use of existing technology in assessment and certification. These could include multi-functional e-platforms, and online assessment tools to test competence-oriented tasks or capture evidence of skilled performance as part of workplace learning. Mobile logbooks could help facilitate the monitoring of training and recording apprentices’ progress, and blockchain technology could be used to validate qualifications. Micro-credentials facilitate not only flexible learning but also the certification of modularised qualifications. Apprenticeship certificates could be integrated into an apprenticeship management system that links learning processes and outcome assessments. When adopting such systems, close collaboration with relevant stakeholders is fundamental. The performance of the system, in particularly its cost-effectiveness, should be monitored in order to continuously improve the system and reduce costs and risks.
To improve its monitoring of the outcomes and performance of apprenticeships, Scotland should:
Scotland has developed several strategies that could foster the digitalisation of the apprenticeship system, and the Scottish economy more broadly. These include the Education and Skills Review (including on innovation) (Scottish Government, 2017[43]), Digital Strategy and Roadmap for Innovations in Industry, and Scotland’s Artificial Intelligence Strategy (Digital Scotland, 2021[44]). Scotland is committed to promoting innovation across sectors, and this is expected to have a spillover effect on apprenticeships. In addition, Scotland’s dual goal of economic growth and net zero emissions, gives it an interest in maximising the use of digital technology and ecological solutions at every corner.
Ensuring that the Scottish apprenticeship system is fully geared towards these economy-wide goals will require strategic guidance and practical support on applying its innovation strategies to the apprenticeship system. Guidelines and support for the actual implementation at the institutional level can will help actors in the apprenticeship system to adopt and effectively use rapidly advancing technology.
3.3.1. Providing guidance and assistance and encouraging partnerships for innovation
Supporting the adoption and use of technology
Given the opportunities that newly available technologies offer for innovation, learning providers and employers should be given guidance and assistance as well as incentives to integrate them into their training. This requires systemic efforts, including equipping learning providers with new devices and software, and teaching teachers and trainers how to use them in their practice, as well as substantial investment and prioritisation for doing so. OECD countries generally have a high level of access to devices, but practical support and guidance from educational institutions or national-level bodies is often missing.
Data from the SELFIE tool (discussed above) show that vocational teachers do not always get the support they need for using digital technologies in the classroom (Hippe, Pokropek and Costa, 2021[45]) (also see the Netherlands example in Box 3.6). For example, across OECD countries with available data, only 52% of vocational teachers using the SELFIE tool reported receiving support from their institutional leaders when trying out new ways of teaching with digital technology, and 45% said that their institutional leaders discuss their professional development needs with them for teaching with digital technology. Only 31% of the teachers agreed that they had time to explore how to improve their teaching with digital technology, and 51% that their institutional leaders support them in sharing experiences within their institution about teaching with digital technology.
A survey of staff in Dutch VET schools (Box 3.6) showed that the most important enabling factors for the successful adoption of digital technology in teaching and learning were having a strategic vision and goals for digital technology adoption (ECBO, 2019[27]). A well-designed strategy for the digital transformation of training institutions, including training companies, starts with an assessment of the current use of technology, the support measures available and any identified challenges. This can be done using a tool like Switzerland’s Digi-Check (Box 3.9). Digital transformation plans and guidance should have concrete goals and targets for the implementation of new technologies. Many VET providers do not carry out systematic reviews of progress on the use of digital technology: only 37% of vocational teachers in OECD countries using the SELFIE tool agreed that progress in teaching and learning with digital technology is reviewed in their school (OECD, 2021[5]).
On-site technical support in the use of technology as well as relevant professional development will also facilitate developing the practice of VET teachers and trainers. In Scotland, apprenticeship providers and SDS (and the SFC with its new responsibilities on Graduate and Foundation Apprenticeships) should ensure that VET teachers and trainers have sufficient time and resources to experiment, reflect, learn and implement new technology in their teaching and training. One way of achieving this could be by encouraging stakeholder partnerships for the development and use of technology and innovation in apprenticeships, as explored below.
Switzerland: Digi-Check
Digi-Check is a tailor-made assessment for the management and staff in VET institutions to use to self-assess the need for digital transformation, such as the design of media-based teaching or the digital skills of teachers and learners. Led by the Swiss Federal Institute for Vocational Education and Training, The assessment takes place through one-day workshops and coaching, with the following objectives:
Taking stock of digitalisation in the vocational school, from a strategic, organisational and pedagogical point of view.
Determining the potential for change in teaching (teaching and learning process, didactic scenarios, and use of digital media).
Defining measures for the further development of teachers' digital skills: how should teachers be prepared and supported to use digital media in their lessons?
Source: OECD (2021[5]), Teachers and Leaders in Vocational Education and Training, https://dx.doi.org/10.1787/59d4fbb1-en.
Developing the use of technology and innovation through stakeholder partnerships
Training providers, employers and apprentices can play a key role in ensuring that new technologies are relevant for the apprenticeship system. In recent years, the education technology (EdTech) market has grown, and many EdTech companies have started producing applications tailored to the need of vocational teachers, trainers and learners, including apprenticeship management systems, simulators and VR/AR. VET teachers, trainers and industry experts are often involved in the design of new applications, making a significant contribution towards developing materials that are relevant and easy to use in vocational training (OECD, 2021[5]). Several countries encourage such collaborations by establishing formal partnerships between the VET sector, industry, EdTech companies and research and development institutions to foster innovation and the use of technology in VET (Box 3.10).
Financial resources and guidance are often provided to help support the establishment of these partnerships. Since 2012, the Innovation Centre Programme in Scotland has encouraged researchers and businesses to forge new collaborations across sectors, with some of the initiatives involving vocational training. Box 3.10 also provides international examples of such support initiatives: Germany’s smart learning factories in VET schools, funded by regional governments, and Denmark’s Knowledge Centres in VET funded by the Danish government.
Another example is Scotland’s Digital Skills Partnership (2017-20), funded by SDS and the SFC. The partnership connected industry with colleges and universities, with the goal of addressing the rapidly growing and changing skills needs of the digital economy. The partnership offered a range of events to help college and university lecturers teaching computing skills to connect with industry employers. The partnership also trialled a collaborative software development project which enabled college and university teachers and students to work together on live industry projects in real workplaces, using industry tools.
Building on these positive experiences, Scotland could further encourage EdTech companies to co-create digital tools and materials with vocational teachers, students and employers that are relevant, affordable, scalable, inter-operational and easy to use in apprenticeships. Unless these important stakeholders are part of the design and use of those tools and materials, the technology is unlikely to be effective for learning and training (OECD, 2021[4]).
Germany: Smart learning factories in VET schools (Baden-Württemberg State)
Preparing for future changes in industrial manufacturing, driven by Industry 4.0 and the Internet of Things, Baden-Württemberg (BW) in Germany established smart learning factories in VET schools, with support from the district government.
In the learning factory, original Industry 4.0 components were installed such as manufacturing execution system computers, Quick Response (QR) code scanners, marking lasers and collaborative robots. Its basic laboratory includes workstations where students work in pairs on training modules. Students can program training modules with programmable logic controllers. They can analyse the functionality of each individual component and evaluate their suitability for Industry 4.0 manufacturing processes. All training modules are mounted on mobile units that can be connected to the IT network, the power supply and the pneumatic system at each of the stationary workstations. The learning factory encourages collaboration with industry and training for teachers and trainers. For example, a learning factory team collaborated with a company to produce model cars by using automation, using real industrial components. Teachers and industry professionals have access to the learning factory facilities and receive training on how it works.
The Netherlands: A funding scheme for innovation in education and training
The Dutch Ministry of Education, Culture and Science has set up the Teachers Development Fund (LerarenOntwikkelFonds). The fund provides teachers, including upper secondary vocational teachers with a budget and guidance to implement their own project for innovation in their school. Teachers are at the heart of managing this funding scheme: they assess project applications, coach and guide teachers, examine the results of the projects, and organise learning and network meetings.
Denmark: Knowledge Centres to promote and facilitate technology use in VET
Denmark has several initiatives to foster the use of technology in VET. The Danish Ministry of Education funded ten Knowledge Centres (KCs) in areas including automation and robotics, IT, welfare technology, and process technology. The KC for IT promotes the use of digital technology in VET. The centre provides professional development opportunities for teachers in VET, including theoretical and practical elements to support teaching and learning, and has also established networks of pedagogical staff and leaders to share their knowledge, creating new solutions to common challenges.
The KC for Automation and Robot Technology promotes innovation in VET using advanced technology such as universal robots, collaborative robots or VR applications for VET teaching. Each centre works with more than a dozen VET schools in the areas of industrial automation, mechanics, electronics, welding, data and communication, and education. The centres provide VET teachers with teaching material, such as teaching tutorials or short courses on Industry 4.0, VR equipment and robots. The centres provide demonstrations and face-to-face technical support to teachers and students on how to use robots in the workplace and lend VR headsets and/or robots to VET teachers.
Source: Lernfabrik Bietigheim-Bissingen (2020[46]), Programmiert auf Lernerfolg!, www.lernfabrik-bietigheim.de/anlage/; (OECD, 2021[5]).: About LOF, https://www.lerarenontwikkelfonds.nl/over-lof; OECD (2021[5]), Teachers and Leaders in Vocational Education and Training, https://dx.doi.org/10.1787/59d4fbb1-en.
Recommendations and implementation for providing strategic guidance and practical support for innovation
In order to further expand and strengthen apprenticeships by using technology and innovation, Scotland should provide strategic guidance and practical support in the development, adoption and use of technology in apprenticeships. In particular:
Do more to translate existing national-level strategies into practical guidance and assistance at institutional levels (local authorities, training institutions and companies etc.). This could include: planning and investment to equip learning providers with new devices and software, training teachers and trainers how to use them in their practice, and assessing needs and identifying barriers to implementing the use of technology in apprenticeship provision. SAAB can play a key role here in providing this practical guidance and assistance, which individual providers could then expand in more detail.
Encourage learning providers, together with employers, to develop plans to use technology to improve and innovate apprenticeships. These should start with an assessment of the current use of technology and related challenges. VET institutions and national-level skills bodies should ensure that VET teachers and trainers have sufficient time and resources to experiment, reflect, learn and implement new technology in their teaching and training. SDS can further support and strengthen the monitoring of this element, for example by including it in the quality assurance assessment.
Encourage collaboration among VET sector, EdTech companies, training employers and research institutions to co-create digital tools and materials that are relevant, affordable, scalable, inter-operational and easy to use within apprenticeships. Scotland could build on existing partnerships or establish new ones specifically for the use of technology and innovation for apprenticeships.
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Note
← 1. It was developed based on the European Commission Framework for Digitally-Competent Educational Organisations (DigCompOrg). The tool was developed with a team of experts from schools, education ministries and research institutes across Europe.