Category Archives: 21stCenturySkills

European Union, AI and data strategy

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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.

Vocational courses not advanced enough

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training, education, vocational training

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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.

 

 

SMEs are not the same as large firms

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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

Is this the right way to use machine learning in education?

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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.

 

Circular Economy and Lifelong Learning: Scenarios – Methodologies – In action

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2019 ACR ZWS Circular Economy Lifelong learning Cover
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.