Archive for the ‘Career Guidance’ Category

The development of Labour Market Information systems

August 29th, 2018 by Graham Attwell

Over the past few years, part of my work has been involved in the design and development of Labour Market Information Systems. But just as with any facet of using new technologies, there is a socio-technical background to the emergence and use of new systems.

Most countries today have a more or less elaborated Labour Market Information system. In general, we can trace three phases in the development of these systems (Markowitch, 2017). Until the 1990s, Labour Market Information systems, and their attendant classification systems, mainly provided statistics for macroeconomic analysis, policy and planning. Between the 1990s and 2005 they were extended to provide data around the structuring and functioning of the Labour markets.

Mangozho (2003) attributes the change as a move from an industrial society to a post-industrial society (and the move to transition economies in Eastern Europe). Such a definition may be contentious, but he usefully charts changes in Labor market structures which give rise to different information needs. “While previously, the economic situation (especially the job structure) was relatively stable, in the latter phase the need for LMI increases because the demand for skills and qualifications changes fundamentally; the demand for skills / qualifications changes constantly, and because of these changes, Vocational Education and Training (VET) system has to be managed more flexibly (ETF, 1998)’.

He says: “In the industrial/pre-transition periods:

  • The relationship between the education and training system and the Labor market was more direct.
  • Occupational structures changed very slowly and as such, the professional knowledge and skills could easily be transferred.
  • Planning, even for short-term courses, could be done well in advance, and there was no need to make any projections about the future demands of occupations
  • The types of subjects and the vocational content required for specific jobs were easily identifiable.
  • There was little need for flexibility or to design tailor-made courses.
  • The education system concentrated on abstract and theoretical knowledge as opposed to practical knowledge.
  • Steady economic growth made it possible for enterprises to invest in on the job training.
  • There was less necessity to assess the relevance and adequacy of the VET system because it was deemed as adequate.
  • A shortage of skills could easily be translated into an increase of the number of related training institutions or student enrolments without necessarily considering the cost effectiveness of such measures. (Sparreboom, T, 1999).
  • Immediate employment was generally available for those who graduated from the education and training systems.”

Changes in the structure and functioning of Labour markets and the VET systems led to a greater need for comprehensive LMI to aid in the process of interpreting these structural shifts and designing effective HRD policies and programs, which provide for more linkages between the education and training systems and the Labor market.

At the same time, the reduction in the role of the state as a major employment provider and the development of market economies gave impetus to the need for a different approach to manpower planning, where the results of Labor market analysis as well as market based signals of supply and demand for skills are made available to the various economic agents responsible for the formulation and implementation of manpower and employment policies and programmes.

This led to the establishment of formal institutions to co-ordinate the generation of LMI, for instance internet based Labour Market Information Systems and the setting up of Labour Market Observatories and the development of more tangible LMI products, which provide a broad up, dated knowledge of the developments on the Labour market for different users.

Since 2005, Labour Market Information systems have been once more extended to incorporate both matching of jobs to job seekers and matching of supply and demand within Labour markets, particularly related to skills.

Data and the future of universities

August 2nd, 2018 by Graham Attwell

I’ve been doing quite a lot of thinking about how we use data in education. In the last few years two things have combined – the computing ability to collect and analyse large datasets, allied to the movement by many governments and administrative bodies towards open data.

Yet despite all the excitement and hype about the potential of using such data in education, it isn’t as easy as it sounds. I have written before about issues with Learning Analytics – in particular that is tends to be used for student management rather than for improving learning.

With others I have been working on how to use data in careers advice, guidance and counselling. I don’t envy young people today in trying to choose and  university or college course and career. Things got pretty tricky with the great recession of 2009. I think just before the banks collapsed we had been putting out data showing how banking was one of the fastest growing jobs in the UK. Add to the unstable economies and labour markets, the increasing impact of new technologies such as AI and robotics on future employment and it is very difficult for anyone to predict the jobs of the future. And the main impact may well be nots o much in new emerging occupations,or occupations disappearing but in the changing skills and knowledge required n different jobs.

One reaction to this from many governments including the UK has been to push the idea of employability. To make their point, they have tried to measure the outcomes of university education. But once more, just as student attainment is used as a proxy for learning in many learning analytics applications, pay is being used as a proxy for employability. Thus the Longitudinal Education Outcomes (LEO) survey, an experimental survey in the UK, users administrative data to measure the pay of graduates after 3, 5 and 0 years, per broad subject grouping per university. The trouble is that the survey does not record the places where graduates are working. And once thing we know for a certainty is that pay in most occupations in the UK is very different in different regions. The LEO survey present a wealth of data. But it is pretty hard to make any sense of it. A few things stand out. First is that UK labour markets look pretty chaotic. Secondly there are consistent gender disparities for graduates of the same subject group form individual universities. The third point is that prior attainment before entering university seems a pretty good predictor of future pay, post graduation. And we already know that prior attainment is closely related to social class.

A lot of this data is excellent for research purposes and it is great that it is being made available. But the collection and release of different data sets may also be ideologically determined in what we want potential students to be able to find out. In the same way by collecting particular data, this is designed to give a strong steer to the directions universities take in planning for the future. It may well be that a broader curriculum and more emphasis on process and learning would most benefits students. Yet the steer towards employability could be seen to encourage a narrower focus on the particular skills and knowledge employers say they want in the short term and inhibit the wider debates we should be having around learning and social inclusion.

 

Learning about Careers: Open data and Labour Market Intelligence

August 1st, 2018 by Graham Attwell

I’ve spent a lot of the last two months writing papers. I am not really sure why – other than people keep asking me to and I really do have a built up load of things which I haven’t written about. But one bad consequence of all this is I seem to have abandoned this blog. So,  time to start catching up here.

This paper – Learning about Careers: Open data and Labour Market Intelligence – is co-written with Deirdre Hughes. It is a preprint and wil be published in RIED – Revista Iboeroamericana de Educación a Distancia (The Iberoamerican Review of Digital Education) some time soon.

The full paper can be found on Research Gate or alternatively you can download it here. The abstract is as follows:

“Decisions about learning and work have to be placed in a particular spatial, labour market and socio-cultural context – individuals are taking decisions within particular ‘opportunity structures’ and their decisions and aspirations are further framed by their understanding of such structures. This article examines ways in which learning about careers using open data and labour market intelligence can be applied. An illustrative case study of the LMI for All project in the UK shows the technical feasibility of designing and developing such systems and a model for dissemination and impact. The movement towards Open Data and increasingly powerful applications for processing and querying data has gathered momentum. This combined with the need for labour market information for decision making in increasingly unstable labour markets have led to the development and piloting of new LMI systems, involving multiple user groups. Universal challenges exist given the increasing use of LMI, especially in job matching and the rapidly expanding use of open source data in differing education and employment settings. We highlight at least six emergent issues that have to be addressed so that open data and labour market intelligence can be applied effectively in differing contexts and settings. We conclude by reflecting on the urgent need to extend the body of research and to develop new methods of co-constructing in innovative collaborative partnerships.”

 

Are job algorithms good enough?

February 22nd, 2018 by Graham Attwell

We’ve all made jokes about the jobs that various ‘professional’ social networks recommend for us.  This morning I had a message from ResearchGate:

LinkedIn is no better. Here are tworesearchgate jobs it recently found for me:

linkedin

Goodness knows how they  vaguely thought I was qualified for these jobs. But never mind – it is only the usual nonsense form free social networks, we think. But it does matter. These reconsiderations come through algorithms. And nearly every Public Employment Service I have talked to is either trialling or considering trialling software which matches applicants to jobs. OK, the algorithms may be better written. And probably the employment services have more data on both applicants and jobs that has the likes of ReseachGate and LinkedIn. But in seeking to provide a better service at less cost through the use of technology the employment services are ignoring that many people need guidance and support when seeking employment form qualified professionals. Taking a job is not like ticking a like on a social website.  It involves serious decisions which can affect peoples futures and the future of their family.  And, at the moment, Artifical Intelligence is not enough for helping in those decisions.

Why is there such a big gender difference in graduate employment

June 16th, 2017 by Graham Attwell

salaries grad

In our work on Labour Market Information Systems, we frequently talk about the differences between labour market information and labour market intelligence in terms of making sense and meanings from statistical data. The graph above is a case in point. It is one of the outcomes of a survey on Graduate Employment, undertaken by the UK Higher Education Statistics Agency (HESA).

Like many such studies, the data is not complete. Yet, looking at the pay by gender reveals what WONKHE call “a shocking picture of the extent of the pay gap even straight out of university, and how different subject areas result in a diverse range of pay differences.”

Understanding why there is such a gap is harder. One reason could be that even with equal pay legislation, employers simply prefer to employ male staff for higher paid and more senior jobs. Also, the graph shows the subject in which the students graduated, not the occupational area in which they are employed. Thus the strikingly higher pay for mean who undertook nursing degrees may be due to them gaining highly paid jobs outside nursing. Another probable factor in explaining some of the pay gap is that the figures include both full and part time workers. Nationally far more women are employed part time, than men. However, that explanation itself raises new questions.

The data from HESA shows the value of data and at the same time the limitations of just statistical information. The job now is to find out why there is such a stark gender pay gap and what can be done about it. Such ‘intelligence’ will require qualitative research to go beyond the bald figures.

Jobs in cyber security

February 7th, 2017 by Graham Attwell

In a new fact sheet the Tech Partnership reveals that UK cyber workforce has grown by 160% in the five years to 2016. 58,000 people now work in cyber security, up from 22,000 in 2011, and they command an average salary of over £57,000 a year – 15% higher than tech specialists as a whole, and up 7% on last year. Just under half of the cyber workforce is employed in the digital industries, while banking accounts for one in five, and the public sector for 12%.

Productivity and vocational education and training

October 25th, 2016 by Graham Attwell

apprenticesInterest in Vocational Education and Training (VET) seems to go in cycles. Its always around but some times it is much more to the forefront than others as a debate over policy and practice. Given the pervasively high levels of youth unemployment, at least in south Europe, and the growing fears over future jobs, it is perhaps not surprising that the debate around VET is once more in the ascendancy. And the debates over how VET is structured, the relation of VET to higher education, the development of new curricula, the uses of technology for learning, the fostering of informal learning, relations between companies and VET schools, the provision of high quality careers counselling and guidance, training the trainers – I could go on – are always welcome.

Whilst in some countries like the UK deregulation seems to have created many jobs, most of these are low paid and insecure.

Higher productivity requires innovation and innovation is in turn dependent on the skills and knowledge of the workforce. But in a time of deregulation there is little incentive for employers to invest in workforce training.

There are signs that some companies are beginning to realise they have a problem. There has been a notable interest from a number of large companies in supporting new apprenticeship programmes and not just in the German speaking countries. In Spain the recently launched Alliance for FP Dual is making slow but steady progress in persuading companies to support the FP Dual alternance or apprenticeship programme. There remain many obstacles, not least the continuing austerity programme, political instability and the perilous financial position of many small and medium enterprises. I will talk more about some of these issues in forthcoming articles on this web site, coming out of the findings of a  small research project in Valencia sponsored by the International Network on Innovative Apprenticeship (INAP).

But to be successful initiatives like the Spanish FP Dual and the wider EU backed Alliance for Apprenticeships have to be linked to wider programmes to promote innovation. Without some degree of labour market regulation this is going to be hard to achieve.

Jobs of the Future

August 22nd, 2016 by Graham Attwell

There is a lot of speculation at the moment as to the jobs of the future. On the one hand, it is said that we are educating young people for jobs which do not yet exist; on the other hand there are dire predictions that up to of existing 55 per cent of jobs may disappear to automation in the next five years.

If it is hard as a researcher who works with labour market data to make sense of all this, imagine what it is like for young people trying to plan a career (and if doing a degree in the UK, running up major debt).

However, there is beginning to appear some more nuanced research on the future of jobs. Michael Chui, James Manyika, and Mehdi Miremadi have just published the initial report on a research project looking at how automation will affect future employment. The report, entitled ‘Where machines could replace humans—and where they can’t (yet)’, is based on detailed analysis of 2,000-plus work activities for more than 800 occupations. Using data from the US Bureau of Labor Statistics and O*Net, they have quantified both the amount of time spent on these activities across the economy of the United States and the technical feasibility of automating each of them.

Their overall finding is that while automation will eliminate very few occupations entirely in the next decade, it will affect portions of almost all jobs to a greater or lesser degree, depending on the type of work they entail.
automation
Each whole occupation is made up of multiple types of activities, each with varying degrees of technical feasibility. In practice, they explain, automation will depend on more than just technical feasibility. Five factors are involved: technical feasibility, costs to automate, the relative scarcity, skills and costs of workers who might otherwise do the activity, benefits (e.g. superior performance) of automation beyond labour costs substitution and regulatory and social acceptance considerations.
The likelihood and ease of automation depends on the types of activities organised on a continuum of less susceptible to automation to more susceptible to automation: managing others, applying expertise,  stakeholder interactions, unpredictable physical work, data collection, processing data, predictable physical work. Thus occupations like accommodation, food service and manufacturing which include a large amount of predictable physical work are likely to be automated, similarly work in finance and insurance which involves much processing of data. On the other hand jobs in construction and in agriculture which comprise predominantly unpredictable physical work are unlikely to be automated, at least at present. And there is good news for teachers: “the importance of human interaction is evident in two sectors that, so far, have a relatively low technical potential for automation: healthcare and education.”

Discourses of Love and Labour

August 8th, 2016 by Graham Attwell

Like very much this announcement in the Media, Communications and Cultural Studies Association (MeCCSA) list server:

Dr Ergin Bulut is an assistant professor at Koc University in Turkey. He will start his IAS fellowship on 1 June 2017. It will focus on the analysis of labour conditions in the video game industry. Dr Bulut comments on the importance of studying the digital game industry: “There is much hype regarding the potentials of creative economy and creative production. Young people tend to regard video game development as a dream job. Our society also preaches that young people should do what they love and be ready to work for free if they really want to have a job in the video game industry or other creative industries. An inquiry of the game industry enables us to understand both the pleasures and pains of game development and interrogate the politics of this ‘dream job’ discourse”.

It seems every event about start up businesses I go to focuses on computers and Information Technology as the golden answer for young people and work opportunities in the future. I don’t have the figures to hand but I read in an EU report that the average (mean, I think) wage for those developing mobile apps in the UK is something like £12,000 a year and it is little more in most EU countries.

There seems a fairly wide disjunction between young people’s perceptions and the reality of opportunities and employment in different jobs.About five years ago I ran a focus group in a careers centre in Kent in England. I asked the young people in the panel how they found out about possible careers. They looked at me as if I was stupid – its obvious sir, they said. We look it up on Google. Research suggests most people rarely go beyond the first page of listings on any Google search. And then, as now, queries about how much pay you can hope to make frequently push sites to the top which are merely trying to gather data and give wildly improbable results based on very little data. It was this experience which led us to become involved in the UKCES LMI for All project which seeks to provide access to a range of quality labour Market Information and can be used to develop a variety of different applications. But good though LMI for All is research like that outlined above in a range of different occupations would be invaluable in helping to understand why young (and not so young) people choose their careers. (And I love the title!).

Making sense of data about education and jobs

June 6th, 2016 by Graham Attwell

restorer
High or low skills? Graduate job or not?

For a number years now I have been working on projects developing the use of open data for careers counselling, advice and guidance. This work has been driven both by the increasing access to open data but also by the realisation of the importance of Labour Market Information (LMI) for those thinking about future education and / or jobs. And of course with high levels of job insecurity, such thinking becomes more urgent and in an unstable economy and labuor market, more tricky.

Yet even if we clean the data, add it to a database, provide and open API for access and develop tools for data visualisation, interpretation is still not easy. Here is one case, taken from this mornings Guardian newspaper.

employment graph

 

Although the article is using the chart to show the rapid growth in knowledge intense occupations, I am not sure it does. Assuming that these are percentage change based on the original job totals, it probably show growth in low skilled jobs is far outstripping high skilled work, especially in the last 12 months. And that is taking into account that (once again probably) most job loss due to technology is focused din low skilled areas – e.g the quoted 70,00 jobs lost in supermarket check outs due to automation.

I am also interested to see from wonkhe that “The Higher Education Statistics Agency (HESA) who have been running the Destination of Leavers Survey (DLHE) and its predecessors for 21 years, are now consulting widely on the future of assessing graduate outcomes.” For some time now there has been disquiet about the numbers of graduates working in ‘non graduate’ jobs. And that raises questions – just like the graph above focusing on high skills occupations – on just what a graduate job is. André Spicer, professor of organisational behaviour at the Cass Business School, City University London has cited “studies suggesting that the jobs which require degree-educated employees have peaked in 2000 and may be going down” and notes that many people apparently employed for their high-level specialist skills end up doing sales and marketing or fairly routine generalist work.

All this of course is highly subversive. Officially we are moving towards a high skilled economy needing more graduates and requiring higher level apprenticeships. My feeling in country slick Spain with high youth unemployment is what we need are apprenticeships in areas like construction and hospitality – both because they are sectors which can provide employment and also where higher skills are desperately needed to improve quality and productivity. Yet for governments there is an awful temptation to launch programmes in new ‘sexy’ areas  like games technologies despite the scarcity of jobs in these fields.

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