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The UK operates a central university admissions service, called UCAS. Today they have released their analysis of institutional and subject admissions for 2020. In an article in the online Higher Education newspaper, WONKHE, Sander Kristel, Chief Operations Officer at UCAS, points out some of  the more striking features of the data.

He reports that Artificial Intelligence degrees have grown by more than 400 per cent in the past decade – from just 65 acceptances in 2011 to 355 acceptances in 2020.

As he says:

This growth will be music to the ears of employers according to research from the Industrial Strategy Council, which highlighted the adoption of automation as the biggest driver of a shift in skills and estimated that 39 per cent of the activities that people are paid to do in the UK today could be automated by 2030, with current technology creating demand in technology-related occupations such as software development.

Less welcome news, however, is that although the ratio of UK male acceptances to UK female acceptances across all Science, Technology, Engineering and Maths subjects has shrunk from 1.34 in to 1.06 over the last decade, there has been little progress made in closing the gap for computer science (6.2 in 2011, relative to 5.7 in 2020), perhaps related to the significant amount of growth in this subject overall.

As many of you will know, FutureLearn is the UK Open Universities MOOC arm, run in conjunction with an international consortium of universities. But, I guess like everyone else, FutureLearn is under pressure to make some money. Their first go was offering paid for certificates for course completion. Another attempt has been to persuade people to sign up for an annual subscription, keeping courses open for a year if they pay.

The latest is to partner with industries for courses providing micro accreditation, in some cases industry recognised. So in December Future Learning is launching “Artificial Intelligence on Microsoft Azure: Machine Learning and Python Basics‘, created by CloudSwft and inc conjunction with Microsoft. “On this microcredential”, they say ” you’ll address this challenge by developing key AI skills that can serve as the first steps towards becoming an AI engineer, business analyst, or AI professional.” And, “Yes. If you successfully complete this microcredential, you’ll receive a voucher to sit a separate exam to earn the Microsoft Azure AI Fundamentals (AI-900) and Microsoft Azure AI Engineer Associate (AI-100) certification.”

Why would FutureLearn be giving away vouchers for sitting Microsoft exams. It could be because the 15 week course costs 584 Euros to enroll.  Much as I like microcredentially, this seems a long way from FutureLearn’s past MOOCs free for participation. And if as the course information claims, “artificial intelligence skills are frequently listed among the most in-demand workplace skills in the current and future job market, as organisations seek to harness AI to revolutionise their operations” and “employers are faced with a shortfall of qualified candidates” surely this is an area where public education and trainings services should be providing online course, rather than restricting access to those who can afford to pay for learning new skills.

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There seems to be a growing awareness of the use and problems with algorithms – at least in the UK with what Boris Johnson called “a rogue algorithm” caused chaos in students exam results. It is becoming very apparent that there needs to be far more transparency in what algorithms are being designed to do.

Writing in Social Europe Christina Colclough says “Algorithmic systems are a new front line for unions as well as a challenge to workers’ rights to autonomy.” She draws attention to the increasing surveillance and monitoring of workers at home or in the workplace. She says strong trade union responses are immediately required to balance out the power asymmetry between bosses and workers and to safeguard workers’ privacy and human rights. She also says that improvements to collective agreements as well as to regulatory environments are urgently needed.

Perhaps her most important argument is about the use of algorithms:

Shop stewards must be party to the ex-ante and, importantly, the ex-post evaluations of an algorithmic system. Is it fulfilling its purpose? Is it biased? If so, how can the parties mitigate this bias? What are the negotiated trade-offs? Is the system in compliance with laws and regulations? Both the predicted and realised outcomes must be logged for future reference. This model will serve to hold management accountable for the use of algorithmic systems and the steps they will take to reduce or, better, eradicate bias and discrimination.

Christina Colclough believes the governance of algorithmic systems will require new structures, union capacity-building and management transparency.I can’t disagree with that. But also what is needed is a greater understanding of the use of AI and algorithms – for good and for bad. This means an education campaign – in trade unions but also for the wider public to ensure that developments are for the good and not just another step in the progress of Surveillance Capitalism.

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Miapetra Kumpula-Natri is the rapporteur for the industry committe for European Parliament’s own-initiative  on data strategy and  a standing rapporteur on the World Trade Organization e-commerce negotiations in the European Parliament’s international trade committee.

Writing in Social Europe she says:

Building a human-centric data economy and human-centric artificial intelligence starts from the user. First, we need trust. We need to demystify the data economy and AI: people tend to avoid, resist or even fear developments they do not fully understand.

Education plays a crucial role in shaping this understanding and in making digitalisation inclusive. Although better services—such as services used remotely—make life easier also outside cities, the benefits of digitalisation have so far mostly accrued to an educated fragment of citizens in urban metropoles and one of the biggest obstacles to the digital shift is lack of awareness of new possibilities and skills.

Kampula-Natri draws attention to the Finnish-developed, free online course, ‘Elements of AI’. This started as a course for students in the University of Helsinki but has extended  its reach to over 1 per cent of Finnish citizens.

Kampula-Natri points out that in the Nordic countries, the majority of participants on the ‘Elements of AI’ course are female and in the rest of the world the proportion exceeds 40 per cent—more than three times as high as the average ratio of women working in the technology sector. She says that after the course had been running in Finland for a while, the number of women applying to study computer science in the University of Helsinki increased by 80 per cent.

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There is an interesting article by Allison Dulin Salisbury in the Forbes magazine this morning. The article says that the Covid 19 pandemic is speeding the digital transformation of business, driven by AI and automation and quotes MIT Economist David Autor calling it an “automation forcing event.”

The combined forces of automation and dramatically altered demand are giving rise to a labor market “riptide” in which some sectors of the economy are seeing mass layoffs while others, like healthcare and tech, are still desperate for talent. Against that backdrop, education and training systems are underfunded and ill equipped to meet the demands of a more complex labor market and the shifting demographics of students.

And from the evidence of the last recession, it appears likely that it will be lower paid and lower skilled workers with jobs most at risk.

However, if the analysis of the problem is correct the answers proposed leave room for doubt. The article says: “The past few years have seen a flourishing of high-quality, low-cost training and education programs, many of them online. They are laser-focused on the needs of working learners.” Maybe so in the USA, but in Europe I am yet to see the emergence of flourishing laser focused online learning programmes. And there is plenty of evidence to suggest that online programmes such as MOOcs have more often been focused on the needs of skilled and higher paid workers.

Neither is the appeal to stakeholder capitalism and for the involvement of employers in the provision of training likely to result in big change. More interesting is the call for “investment in practices that help workers identify what career they want before they start an education program,” and to “align training to the competencies required to land a good first job.” This, the article says “means a focus on both discrete skills and broader “human skills,” like communication and problem-solving, that actually become more marketable amid automation.”

Despite reservations, the argument is moving in the right direction. Put simply the Corona virus has on its own caused massive unemployment, with the effect likely to be magnified by a speed up in automation and the use of AI. This requires the development of large scale training programmes, both for unemployed young people and lower skilled workers whose jobs are threatened. Fairly obviously, the use of technology can help in providing such programmes. Nesta in the UK is already looking at developments in this direction. It will be interesting to see what national governments and the European Union will do now to boost training as a response to the crisis.

geralt (CC0), Pixabay

The Centre for London, a ‘think tank’ for the English capital, has released an interesting new report on further education in London.

The report finds that further education in London is hampered because:

• It is underfunded: there are more learners in Further Education than in Higher Education in London, but spending on adult education, apprenticeships and other work-based learning for over 18s has fallen by 37 per cent since 2009/10.
• There are not enough learners: the proportion of working age Londoners in Further Education has fallen by over 40 per cent since 2014 – only one in 13 Londoners were in further education in 2019.
• Funding can be restrictive: grants for learners and colleges have been reduced or replaced with loans, and providers continue to be funded by annual contracts based on the number of learners in the previous year.
• Making savings impacts teaching: As of February 2019, 29 per cent of London’s colleges were Ofsted rated as requiring improvement or inadequate, compared to just six per cent of London’s schools.
• Courses are not advanced enough: 99 per cent of learners are taking courses at level 3 or below (equivalent to A-Level) and three quarters at level 2 (equivalent to GCSE) or below.
• There are not enough new apprentices: Despite government investment in apprenticeships, London has half as many apprenticeship starts as the rest of the UK, and many of these new starters are not new to the labour market.
• It has not responded to employers’ needs: the number of learners and apprentices in areas with skills shortages has barely changed since 2014/15.

The fall in the number of learners is worrying, but only to be expected given the sharp fall in funding for FE. Nevertheless a better understanding of what exactly is going on would be further data regarding how many people in London are participating in learning. It is possible that part of the fall is due to people pursuing online programmes, although I doubt that this accounts for all of the shortfall.

I am not convinced by the finding that FE has not responded to employers needs – in the long time I have been involved with vocation education and training employers have always said that (although I suppose it is possible that VET provision has never met employers needs).

The point about courses not being advanced enough is one that I have heard in other parts of the UK. I wonder if it is because it is more expensive to provide more advanced courses, or simply that many learners are not equipped to start on more advanced provision.

Much of my work at the moment is focused in two different areas – the training and professional development of teachers and trainers for the use of technology for teaching and learning and the use and understanding of labour market data for careers counseling, guidance and advice. However as data increasingly enters the world of education, the two areas are beginning to overlap.

This morning I received an email from the European Network on Regional Labour Market Monitoring. Although the title may seem a little obscure, the network, which has been active over some time, organises serious research at a pan European level. Each year it selects a theme for research, publications and for its annual conference. Over the last year it has focused on informal employment. Next year’s theme is Small and Medium Enterprises (SMEs) which they point out can be viewed as perhaps the most vibrant and innovative area of the European economy. However, when it comes to researching and understanding SMEs it is not so easy

A number of European or national statistics exist to analyse SMEs’ but they generally use the same categories as for large firms and are, in general, constructed from a large firm perspective or in any case not from a framework based on SME characteristics. Many academic papers focusing on SMEs show that they cannot fully be understood using the same categories as with large firms. The general idea is that firstly, SMEs are same as large ones, just smaller. Secondly, the assumption that they will grow up to become Midcaps, then large firms, is incorrect. Torres and Julien (2005) start their article explaining that “Most, if not all, researchers in small business have accepted the idea that small business is specific (the preponderant role of the owner-manager, low level of functional breakdown, intuitive strategy, etc.)”. A 2019 French publication directed by Bentabet and Gadille tackles the issue of SMEs focussing on their specific “social worlds”, their “action models and logics”, while elsewhere the influences of institutional logics and multi-rationalities of SMEs have been considered. The entry of social worlds highlights the great diversity of micro-enterprises and SMEs, which often makes it difficult to analyse them. As a counterpoint, specific knowledge of these companies is required because they are at the heart of the debates on flexibility, labour market dynamics, skilled labour shortage and disruptions in the vocational training system.

SMEs will be the focus for the next Annual Meeting of the Regional Labour Market Monitoring to be held in September 2020 in Sardinia

An article ‘Predicting Employment through Machine Learning‘ by Linsey S. Hugo on the National Association of Colleges and Employers web site,confirms some of my worries about the use of machine learning in education.

The article presents a scenario which it is said “illustrates the role that machine learning, a form of predictive analytics, can play in supporting student career outcomes.” It is based on a recent study at Ohio University (OHIO) which  leveraged machine learning to forecast successful job offers before graduation with 87 percent accuracy. “The study used data from first-destination surveys and registrar reports for undergraduate business school graduates from the 2016-2017 and 2017-2018 academic years. The study included data from 846 students for which outcomes were known; these data were then used in predicting outcomes for 212 students.”

A key step in the project was “identifying employability signals” based on the idea that “it is well-recognized that employers desire particular skills from undergraduate students, such as a strong work ethic, critical thinking, adept communication, and teamwork.” These signals were adapted as proxies for the “well recognised”skills.

The data were used to develop numerous machine learning models, from commonly recognized methodologies, such as logistic regression, to advanced, non-linear models, such as a support-vector machine. Following the development of the models, new student data points were added to determine if the model could predict those students’ employment status at graduation. It correctly predicted that 107 students would be employed at graduation and 78 students would not be employed at graduation—185 correct predictions out of 212 student records, an 87 percent accuracy rate.

Additionally, this research assessed sensitivity, identifying which input variables were most predictive. In this study, internships were the most predictive variable, followed by specific majors and then co-curricular activities.

As in many learning analytics applications the data could then be used as a basis for intervention to support students employability on gradation. If they has not already undertaken a summer internship then they could be supported in this and so on.

Now on the one hand this is an impressive development of learning analytics to support over worked careers advisers and to improve the chances of graduates finding a job. Also the detailed testing of different machine learning and AI approaches is both exemplary and unusually well documented.

However I still find myself uneasy with the project. Firstly it reduces the purpose of degree level education to employment. Secondly it accepts that employers call the shots through proxies based on unquestioned and unchallenged “well recognised skills” demanded by employers. It may be “well recognised” that employers are biased against certain social groups or have a preference for upper class students. Should this be incorporated in the algorithm. Thirdly it places responsibility for employability on the individual students, rather than looking more closely at societal factors in employment. It is also noted that participation in unpaid interneships is also an increasing factor in employment in the UK: fairly obviously the financial ability to undertake such unpaid work is the preserve of the more wealthy. And suppose that all students are assisted in achieving the “predictive input variable”. Does that mean they would all achieve employment on graduation? Graduate unemployment is not only predicated on individual student achievement (whatever variables are taken into account) but also on the availability of graduate jobs. In teh UK  many graduates are employed in what are classified as non graduate jobs (the classification system is something I will return to in another blog). But is this because they fail to develop their employability signals or simply because there simply are not enough jobs?

Having said all this, I remain optimistic about the role of learning analytics and AI in education and in careers guidance. But there are many issues to be discussed and pitfalls to overcome.

The momentum for the circular economy has never been stronger. Global issues, such as climate change and natural resource consumption levels, urgently require a change in our lifestyles and a transformation in our ways of thinking and acting. To achieve this change, we need new skills, new values and new behaviours that lead to more sustainable societies. But is it even possible to find a shared definition of circular economy (CE) education?

As part of the Erasmus+ CYCLE project, in which Pontydysgu are a partner, on 19 February 2019, ACR+, in partnership with Zero Waste Scotland, organised a workshop entitled “Circular Economy Competencies. Making the Case for Lifelong Learning”.  brought together local authorities, experts and practitioners in the field of environmental and sustainability education to discuss this topic. The speakers of the workshop shared stories of vocational training and green jobs, sustainable consumption education and system thinking, of pedagogical models capable of empowering learners and urging institutions to include the principles of sustainability in their management structures. I introduced the project at the workshop and have contributed to the publication.

What this publication is about

This publication aims to make those experiences a shared treasure by sharing them with educators, policymakers and managers of NGOs and training organisations that intend to promote the development of local loops of circular economy through educational tools. The three chapters of this booklet are structured to cover different areas of the lifelong learning landscape:

• Circular thinking in education. Educational designers will find useful insights on: the promotion of circular holistic approach in schools; a bird’s-eye view on how tertiary education is integrating the circular economy into its educational offer; the creation of attractive learning pathways in adult training;
• Upskilling waste, repair & reuse industry. Policy makers and professionals in the field of vocational training will find useful references on the development of professional standards and competence profiles for 3R’s industries;
• Facilitating the transition towards circular economy. The last chapter contains an analysis of the links between Industry 4.0 and circular economy in Italy and the case history of a network of municipalities that have developed training courses to equip local authorities’ staff for the circular transition. In conclusion, a final article analyses the possible positive correlations between entrepreneurial education and circular economy.

You can download the publication here.

There continues to be a flurry of newspaper articles and studies of teh effect of automation and Artificial Intelligence on employment and jobs. There are different predictions about the scale of the change and particularly about the numbers of jobs which are at risk. One cause of the difference is disagreements about how many new jobs will be created, another is the speed of change. This may in part depend on whether employers choose to invest in new technologies: in teh UK productivity has remained persistently low, probably due to low wage rates.

What we do know is that organisations will need to cope with many of the changes associated with changes in the skill mix required of their employees  through learning through challenging work, training and continuing professional development etc. We also know that the changes mean it is difficult to imagine exactly how the labour market will look in say ten years but understanding the labour market can help people make sense of the context in which they are working or are seeking to work

At the same time we do not know the exact skill demands associated with unforeseen changes in the labour market, but we do know that new technical skills will be required, individuals and firms may need to specialise more to compete in global markets, and that demand will grow for ‘soft skills’ which are very difficult to automate, including complex social skills, cultural and contextual understanding, critical thinking, etc.

Yet this debate is not new. In the 1990s there were similar debates around teh move towards the ‘knowledge society’. At that time it was being predicted that low skilled work was set to rapidly decline, a prediction that pre-dated the rapid expansion in low skilled (or at any rate low paid) employment in the service sector. the answer at that time was seen to be promoting transversal skills and competences, variously called core skills, core competences etc. These emhpasised teh important of literacy and numeracy as well as communication skills and Information Technology. The problem was that such skills and competences were, in general abstracted from the curriculum as stand aone areas of learning, rather than being integrated within occupational learning. Of course, the other tendency n many Euroepan countries was to increase the number of young people going to university, at the expense of vocational educati0on and training.

What was needed then as now was to develop technical skills coupled with soft skills. Mastery of a technical skill is itself be a transferable skill whereby other technical skills can be developed more quickly as they are required . Developing latest industry-integrated technical skills is easiers if an underpinning technical knowledge base has been developed through more traditional educational provision. Retraining while in-work is very much easier than getting redundant people back into work.

Germany by Gerald Heidegger and Felix Rauner who looked at occupational profiles. Occupational profiles are in effect groups of competencies based on individual occupations. In Germany there are over 360 officially recognised occupations.

As long ago as 1996, Gerald Heidegger and Felix Rauner from the University of Bremen were commissioned by the Government of Rhineland Westphalia to write a Gutachten (policy advice) on the future reform and modernisation of the German Dual System for apprenticeship training.

They recommended less and broader occupational profiles and the idea of wandering occupational profiles. By this term they were looking to map the boundaries between different occupations and to recognise where competences from one occupation overlapped with that of another. Such overlaps could form the basis for boundary crossing and for moving from one occupation to another.

Heidegger and Rauner’s work was grounded in an understanding of the interplay between education, work organisation and technology. They were particularly focused on the idea of work process knowledge –  applied and practice based knowledge in the workplace. This was once more predicated on an idea of competence in which the worker would make conscious choices of the best actions to undertake in any particular situation (rather than the approach to competences in the UK which assumes there is always a ‘right way’ to do something).

Per Erik Ellstroem from Sweden put forward the idea of Developmental Competence – the capacity of the individual to acquire and demonstrate the capacity to act on a task  and the wider work environment in order to adapt, act and shape (design) it.

This is based on the pedagogic idea of sense making and meaning making through exploring, questioning and transcending traditional work structures and procedures. Rauner talked about holistic work tasks, based on the idea that a worker should understand the totality of the work process they are involved in.

In this respect it is interesting to see the results of recent research by Burning Glass, a company using AI and big data techniques to analyse labour market information. They say that in examine the role of Receptionist in Burning Glass Technologies’ labor market analysis tool, Labor Insight, “we can see that receptionists have a variety of related jobs they can do based on their transferable skills. Transferable skills are types of skills that a worker can use across many jobs, allowing them to more easily transition into a new role. A receptionist has many transferable skills such as administrative support, customer service, scheduling, data entry, and more. These transferable skills will allow a receptionist to move into related jobs such as Legal Secretary, Executive Assistant, or File Clerk.

According to Labor Insight, a Receptionist can transition into a Medical Secretary role which offers a higher average salary and is projected to grow by 22.5% in the next 10 years. This also offers an opportunity for the receptionist to venture into a new industry, allowing them to explore new health care roles such as Nursing Assistant, Emergency Room Technician, or Patient Service Representative.

The transferable skills that Burning Glass talk of are very similar to Rauner and Heidegger’s wandering occuaptional profiles. Rather than. as some commentators have suggested (see for example Faisal Hoque), a return to humanities based subjects in providing abstracted knowledge as the basis for future qualifications, the need is to improve vocational education and training which allows workers to understand the potentials of integrating automation and AI in the workplace. Creativity is indeed important, but creativity was always a key aspect of many jobs: creativity is part of the work process, not an external skill.