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.

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.

The debate around Learning Analytics seems to be opening up. And although there is little sign of agreement over future directions, the terms of discussion seem both broader and more nuanced than previously. I think some of this is in response to the disillusionment of early researchers and adopters.

In yesterdays OLDaily, Stephen Downes pointed to an excellent article by Bodong Chen. Bodong points to the surge of interest in Learning Analytics but cautions that: “The surge of this nascent field rests on a promise–and also a premise–that digital traces of learning could be turned into actionable knowledge to promote learning and teaching.

He suggests that: “One approach to understanding learning analytics is to recognize what are not learning analytics” including academic analytics and educational data mining. Instead, he says “learning analytics is more directly concerned with teachers and learners by attending to micro-patterns of learning.”

Bodong draws attention to a tension between learning and analytics “as two pivotal concepts of the field” He points out that “learning analytics deals with educational phenomena at multiple levels”. As an example he says: “collaborative knowledge building as a group phenomenon depends on contributions from individuals, but cannot be reliably inferred from individual learning.”

Understanding and accepting that “the meaning of learning analytics as a term is plural and multifaceted” is an important basis for future research. Within the only just emerging field of workplace Learning Analytics, not only is there the issue of individual and collaborative learning and knowledge development but also issues around proxies for learning. Whilst performance in practice might be seen as a possible proxy, performance may also be seen to involve a wider range of factors, including the working environment, the division of work and opportunities for practice. And the already established field of Performance Analytics seems at considerable tension to learning.

I found yesterdays protests about the BBC plans to archive their recipe site fascinating. After over 120000 people signed a petition protesting against the move and after the government culture minister (somewhat disingenuously) distanced himself from the plan, the BBC backed down and said they would move the recipes to their commercial web site. Now those into conspiracy theory might suggest this was what the BBC were after all the time and others point to huge protests from the middle class over the potential restriction on access to the Great British Bake off etc. whilst cutbacks to welfare quietly proceed. But I think this misses the point.

The major pressures for the BBC to restrict access to free recipes was that they are competing with private businesses including paid for newspapers, subscription websites, commercial publishers and so on. And that public funding should not be allowed to so this. People didn’t buy in to that argument, largely because of a conciousness that the BBC is a publicly owned organisation and that we have teh right to free content paid for by a license fee (ie taxes). I seem to remember the same argument coming from publishers in the early days – some ten or twelve years ago – against Open Educational Resources. Resources created by university staff, so they said, were paid for by public funding and that was unfair competition.  Today despite the government’s same disdain for publicly funded education as for the BBC, Open Educational Resources have become seen as a Good Thing. And the debate over OERs has extended into a wider discussion on the meaning of open. In the same way the protests over the proposed archiving of a publicly owned archive of recipes could well extend into the meaning of open content in wider areas of the web and to an open digital infrastructure The battle for open goes on.

Another in this mini series on Learning Analytics. When looking at Work based learning, Double Loop Learning becomes particularly important. Double-loop learning is used when it is necessary to change the mental model on which a decision depends. Unlike single loops, this model includes a shift in understanding, from simple and static to broader and more dynamic, such as taking into account the changes in the surroundings and the need for expression changes in mental models.⁽Mildeova, S., Vojtko V. ,2003).

To remind readers again, in the EmployID European project we are aiming to support scalable and cost-effective facilitation of professional identity transformation in public employment services. And I would argue such identity transformation is based on refection on learning, on Double Loop Learning. Identity transformation necessarily involves the development of new metal models and new ways of looking at work based behaviours and practices.

So where does Learning Analytics fit into this? Learning analytics aims to understand and improve learning and the learning environment. This does not necessarily involve Double Loop Learning. For students feedback about their present performance may be enough. But if we aim for identity transformation and wish to improve the learning environment then we need a deeper interpretation of data. This has a number of implications in terms of designing Learning Analytics.

Firstly we have to have a very clear focus on what the purpose of the Learning Analytics is. Is it to find out more for example about informal learning in organisations or to inform L and D department staff about the Learning environment. Is it to help learners understand about their interactions with other staff or to examine their own dispositions for learning – and so on? Secondly – and crucially who is that data presented to users – be it learners or trainers. The existing parading for Learning Analytics presentations appears to be the dashboard. Yet in the LAk16 pre conference workshops there were a whole series of presentations where presenters invited participants to say what the graphics meant. And often we couldn’t. If LA professionals cannot interpret data visualisations then a leaner has little hope of making their own meanings. I am a little puzzled as to why dashboards have become the norm. And one of my major concern is that often it is difficult to understand the visualisation out of the context in which the learning exchange has happened. If Double Loop learning is to happen, then learners need to reflect in order to make meanings. And refection occurs best, I think, in the context in which it takes place.

Image: Ralph Klamma – http://www.slideshare.net/klamma/technical-challenges-for-realizing-learning-analytics

There are alternatives to the dashboard. For instance with EmployID we are developing real time discourse analysis and are also looking at providing dynamic prompts for reflection.>One final point. If we are aiming at using Learning Analytics for Double Loop Learning we need to find out what works and what does not. That means that any measure for Learning Analytics needs to be accompanied by well designed evaluation measures. All too often because LA collects data, it presumes to cover evaluation. Whilst both LA and evaluation may share data, they aim at different things.

The debate over the future of work, long running in research circles but kicked into public consciousness amongst others a Oxford University study titled ‘The Future of Employment: How susceptible are jobs to computerisation’ suggesting over 40 per cent of jobs are at threat in the next 11 years due to technology, continues. In truth there is little agreement from economists and labour market specialists. Some claim techn0logy is leading to more jobs, some that it is destroying jobs and still other that it is neutral. Some claim technology is leading to jobs being deskilled, others the reverse.

I like a recent blog post entitled ‘More on digitalisation and skills: What happens within occupations?’, by Guillermo Montt on the OECD Skills and Work web site. The article says that “as technology enters the workplace, the tasks related to a job and an occupation change” citing  Alexandra Spitz-Oener (2006) who found that in Germany, occupations in the 2000s require more complex skills than in 1979 and that this change is more pronounced in occupations that adopted computers. Although something of a simplification, that finding is largely born out in analysis of the USA O*NET data. The article also draws attention to research by James Bessen published in his recent book ‘Learning by Doing: The Real Connection between Innovation, Wages and Wealth‘. “He follows the evolution of occupations over time and claims that accelerated technological change has implications for inequality within occupations with more and more occupations becoming winner-take-all markets.” Essentially, as new technology is introduced pay and opportunities in occupations bifurcate with a few taking high high, pay levels and more taking home lower pay. “In occupations requiring above-median computer use, the 90th to 50th percentile wage ratio has risen by 0.2% per year but has remained stagnant in occupations with below-median computer use. Workers who stay ahead of the curve, those who learn by doing, reap the wage benefits of technological change.”

This has major implication for training and continuing professional development. CPD has traditionally been organised through courses. But as we have already found in in the EmployID project working with employees in European Public Employment Services, traditional course delivery is both too slow to respond to change and even more problematic is unable to deliver the volume of training required. The approach adopted in EmployID is both to look at using new technologies for learning and for promoting informal learning in the workplace but also to center on changing occupational identities. For instance there is a very different occupational identity associated with a print graphic designer than todays web designer. But the ability to change occupational identities may be shaped by previous learning experiences and by motivation as well as the ability to reflect on both individual and group learning. Within EmployID we are exploring how Learning Analytics can bets be deployed to assets people in reflection (Reflection Analytics) and to assist in transforming identities to deal with such change. I am presenting this work next week at a LAKs pre conference workshop in Glasgow and will publish by slides on this blog.

Predicting the future of labour markets is not easy at the best of times. And this is not the best of times. The problems include the long lasting effects of the financial crash, the impact of government austerity policies (and non impact of qualitative easing) as well as rapid changes in the way we work and in the technologies we are using.

Essentially future labour markets are modelled using existing labour markets, with the proviso of different scenarios according to disruption. At the moment disruptions are seen to be overriding the base model, resulting in much uncertainty.This is a big issue for young people setting out on a career or indeed for those thinking of changing jobs or of entering education  and training.

The real problem with modelling is that there is no consensus on what is happening with today’s labour markets. Lately  this debate has spilled out from more academic and economic journals into the popular press, with predictions of a severe squeeze on middle skilled work, especially office work, due to the introduction of robots, machine learning and artificial intelligence. Yet a new  study by Dr Andrea Salvatori of the Institute for Social and Economic Research calls such concerns into doubt.

Although she recognises a bifurcation of labour markets with a decline of middle skilled jobs, rather than robots, the cause, she suggests, is the expansion in university education, “which has led to a tripling in the share of graduates among employees, accounting for the entire growth in top-skilled occupations, as well as a third of the decline in middling occupations.”

“In parallel, the relative performance of wages in high-skill occupations has deteriorated relative to mid-skill ones, indicating that the supply of workers for these jobs outpaced demand and contributed to the continuing shift from the middle to the top. These facts are highly suggestive that the improvement in the education of the workforce has contributed significantly to the reallocation of employment from mid- to high-skill occupations.”

Andrea Salvatori says that far from being threatened by technology the wages of middle skilled occupations have risen in line with high skilled professions, which she suggests may be due to the increased use of technology.

This debate is important. It suggests that rather than the disruption by technology (which it is always presumed as inevitable) it is government policies over education and training that are responsible for the shrinkage in middle skilled jobs. It could also be suggested that that lack of such jobs may in part be to blame fo the persistently low rate of increase in productivity in the UK, especially when compared with Germany which has continued to train for middle skilled jobs through its apprenticeship system.

I like this blog post by Robert Peal entitled ‘A Myth for Teachers: Jobs That Don’t Exist Yet’. The article looks at the origins of the idea that the top 10 in-demand jobs in 2010 didn’t exist in 2004 and its later variant that 60 per cent of the jobs for children in school today have not been invented. In both cases he found it impossible to track these statement in any reliable research. Of course these are myths. But often such myths can be tracked back to quite prosaic political objectives.

For a long time, the European Union has pushed the idea of the knowledge society. And whilst there are many learned papers describing in different ways what such a society might look like or why such a society will emerge there is little evidence of its supposed impact on labour markets. Most common is the disappearance of low and unskilled jobs, linked to growing skill shortages in high skilled employment. Yet in the UK most recent growth in employment has been in low skills, low paid jobs in the retail sector. I remember too in the late 1990s when the European industry lobby group for computers were preaching dire emergencies over the shortage of programmers, with almost apocalyptic predictions of what would happen with the year 200 bug if there were not major efforts to train newcomers to the industry. Of course that never happened either and predictions of skills shortages in software engineering persist despite the fact the UK government statistics show programmers pay falling in the last few years.

I’ve been invited to do several talks in the last year on the future of work. It is not easy. There are two lengthy reports on future skills for the UK – ‘Working Futures 2012- 2022’ and ‘The future of work: jobs and skills in 2030’, published by the UK Commission for Skills and Industry. Both are based on statistical modelling and scenario planning. As one of the reports says (I cannot remember which) “all models are wrong – it is just that some of more useful than others. Some things are relatively clear. There will be a big upturn in (mainly semi skilled) work in healthcare to deal with demographic changes in the age of the population. There will also be plenty of demand for new skilled and semi skilled workers in construction and engineering. Both are major employment sectors and replacement demand alone will result in new job openings even if they do not expand in overall numbers (many commentators seem to forget about replacement demand when looking at future employment).

But then it all starts getting difficult. Chief perhaps amongst this is possible disruptions which can waylay any amount of economic modelling. The following diagram above taken from ‘The future of work: jobs and skills in 2030’ shows possible future disruptions to the UK economy and to future jobs. One of these is the introduction of robots. With various dire reports that up to 40 per cent of jobs may disappear to robots in the next few years, I suspect we are creating another myth. Yes, robots will change patterns of employment in some industries, and web technologies enable disruptions in other areas of the economy. Yet much of the problems with such predictions lay with technological determinism – the idea that technology somehow has some life of its own and that we cannot have any says over it. At the end of the day, despite all the new technologies and the effects of globalization, there are massive policy decisions which will influence what kind of jobs there will be in the future. These include policies for education and training, inter-governmental treaties, labour market and tax policies, employment rights and so on. And such considerations should include what jobs we want to have, how they are organised, where they are and the quality of work. At the moment we seem to be involved in a race to the bottom – using the excuse of austerity – which is a conscious policy – to degrade both pay and work conditions. But it doesn’t need to be like this. Indeed, the excuses for austerity may be the biggest myth of all.