Archive for the ‘Career Guidance’ Category

Good jobs, bad jobs, skills and gender

February 3rd, 2020 by Graham Attwell

I have written before about the issues of interpreting sense making from Labour Market Data and the difference between Labour Market Information and labour Market Intelligence.

This is exposed dramatically in the article in Social Europe by German Bender entitled ‘The myth of job polarisation may fuel populism’. As German explains “It has become conventional wisdom since the turn of the century that labour markets are rapidly becoming polarised in many western countries. The share of medium-skilled jobs is said to be shrinking, while low- and high-skilled jobs are growing in proportion.” But as German points out: “In a research report published last May by the Stockholm-based think tank Arena Idé, Michael Tåhlin, professor of sociology at the Swedish Institute for Social Research, found no job polarisation—rather, a continuous upgrading of the labour market.”

German goes on to explain:

The main reason is that the research, as is to be expected from studies rooted in economics, has used wages as a proxy for skills: low-paying jobs are taken to be low-skilled jobs and so on. But there are direct ways of measuring skill demands in jobs, and Arena Idé’s report is based on a measure commonly used in sociology—educational requirements as classified by the International Labour Organization’s ISCO (International Standard Classification of Occupations) scheme. Using this methodology to analyse the change in skill composition yields strikingly different results for the middle of the skill distribution.

The study found that while jobs relatively low skill demands but relatively high wages—such as factory and warehouse workers, postal staff and truck drivers—have diminished, others with the same or slightly higher skill demands but lower wages—nursing assistants, personal-care workers, cooks and kindergarten teachers—have increased.

The reason is that the former jobs are male dominated whilst the jobs which have grown have a majority of female workers. Research in most countries has shown that women (and jobs in which women are the majority) are lower paid than jobs for men, regardless of skills levels.

“Put simply”, says German: “wages are a problematic way to measure skills, since they clearly reflect the discrimination toward women prevalent in most, if not all, labour markets across the world.”

A further review of two British studies from 2012 and 2013, showed a change in the composition, but not the volume, of intermediate-level jobs. “Perhaps the most important conclusion”, German says “was that ‘the evidence shows that intermediate-level jobs will remain, though they are changing in nature’.”

The implications of this interpretation of the data are profound. If lower and medium skilled jobs are declining there is little incentive to invest in vocational education and training for those occupations. Furthermore, young people may be put off entering such careers and similarly careers advisers may further mislead school leavers.

There has been a trend in many European countries towards higher level apprenticieships, rather than providing training with the skills need to enter such medium skilled jobs. But even a focus on skills, rather than wages, may also be misleading. It is interesting that jobs such as social care and teaching appear more resistant to automation and job replacement from technologies such as Artificial Intelligence. But those who are arguing that we should be teaching so called soft skills such as team building, empathy and communication are talking about the very skills increasingly demanded in the female dominated low and middle skilled occupations. It may be that we need not ony to relook at how we move away from wages as a proxy for skills, but also look at how we measure skills.

German references research by Daniel Oesch and Giorgio Piccitto, who studied occupational change in Germany, Spain, Sweden and the UK from 1992 to 2015, characterising good and bad jobs according to four alternative indicators: earnings, education, prestige and job satisfaction.

They concluded that occupations with high job quality showed by far the strongest job growth, whereas occupations with low job quality showed weak growth regardless of indicator used.

 

 

 

 

 

 

 

 

 

 

 

SMEs are not the same as large firms

December 18th, 2019 by Graham Attwell

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

Career Development: Identity, Innovation and Impact

September 17th, 2019 by Graham Attwell

On Thursday, 10th October 2019 I am delighted to be speaking at the conference on ‘Career Development: Identity, Innovation and Impact’ in Birmingham UK

The conference will focus on career development policies, research and practice for young people and adults. It will explore practical ways of harnessing individuals’ talents, skills and learning experiences in fast changing and uncertain labour markets. Here is the abstract for my presentation:

Graham Attwell, technical lead for the UK ‘LMI for All’ project (funded by the Department of Education and led by the University of Warwick, IER) will explain latest labour market intelligence/information developments applied in career education, guidance and counselling settings. He will reflect on the changing world of work and examine the impact of technology on the future labour market and implications of Automation and Artificial Intelligence (AI) on employment and the jobs of the future. He will consider how can we best advise young people and adults on courses and employment.

The conference, organised by Deirdre Hughes for DMH Associates, will be exploring the changing nature of identities on a lifelong basis, innovative ways of working with young people and adults in education, training, employment and other community settings. In times of austerity and the impact on services users, there becomes an urgent need to provide evidence on the impact of careers work.

Participants will also get the chance to hear about a series of recent international policy and research events and your own ‘Resource Toolkit’. It is, the conference newsletter says, an opportunity to acknowledge and celebrate innovative and impactful careers work.

Deirdre Hughes will be announcing ambitious plans to help inspire others to engage in career development policies, research and practice and saying more about what they are doing with their partners on careers work in primary schools, post-primary schools and colleges (city-wide approaches), youth transitions, evidence and impact approaches and adult learning both within and outside of the workplace. To receive regular copies of their newsletter go to http://eepurl.com/glOP2f.

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

September 2nd, 2019 by Graham Attwell

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.

 

Travel to university time a factor in student performance

August 14th, 2019 by Graham Attwell

My summer morning’s work is settling into a routine. First I spend about half an hour learning Spanish on DuoLingo. Then I read the morning newsletters – OLDaily, WONKHE, The Canary and Times Higher Education (THE).

THE is probably the most boring of them. But this morning they led on an interesting and important research report. In an article entitled ‘Long commutes make students more likely to drop out’, Ana McKie says:

Students who have long commutes to their university may be more likely to drop out of their degrees, a study has found.

Researchers who examined undergraduate travel time and progression rates at six London universities found that duration of commute was a significant predictor of continuation at three institutions, even after other factors such as subject choice and entry qualifications were taken into account.

THE reports that the research., commissioned by London Higher, which represents universities in the city found that “at the six institutions in the study, many students had travel times of between 10 and 20 minutes, while many others traveled for between 40 and 90 minutes. Median travel times varied between 40 and 60 minutes.”

At one university, every additional 10 minutes of commuting reduced the likelihood of progression beyond end-of-first-year assessments by 1.5 per cent. At another, the prospect of continuation declined by 0.63 per cent with each additional 10 minutes of travel.

At yet another institution, a one-minute increase in commute was associated with a 0.6 per cent reduction in the chances of a student’s continuing, although at this university it was only journeys of more than 55 minutes that were particularly problematic for younger students, and this might reflect the area these students were traveling from.

I think there are a number of implications from this study. It is highly probable that those students traveling the longest distance are either living with their parents or cannot afford the increasingly expensive accommodation in central London. Thus this is effectively a barrier to less well off students. But it is also worth noting that much work in Learning Analytics has been focused on predicting students likely to drop out. Most reports suggest it is failing to complete or to success in initial assignments that is the most reliable predicate. Yet it may be that Learning Analytics needs to take a wider look at the social, cultural, environmental and financial context of student study with a view to providing more practical support for students.

I work on the LMI for All project which provides an API and open data for Labour Market Information for mainly use in careers counseling advice and guidance and to help young people choose their future carrers or education. We already provide data on travel to work distances, based on the 2010 UK census. But I am wondering if we should also provide data on housing costs,possibly on a zonal basis around universities (although I am not sure if their is reliable data). If distances (and time) traveling to college is so important in student attainment this may be a factor students need to include in their choice of institution and course.

 

Is manufacturing finished in the UK?

June 12th, 2019 by Graham Attwell

The Guardian newspaper highlights a report by Cambridge University for the Department for Business, Energy and Industrial Strategy (BEIS), showing that Britain’s manufacturing sector is much larger than official figures suggest.

The report argues that official statistics, which estimate that manufacturing output accounts for 9% of national income, are based on “outdated and inaccurate methods of counting” and the figure is much higher.

The report avoids putting a fresh figure on the proportion of GDP accounted for by the sector, but one of its authors said it was nearer 15% once activities tied to the sale of UK-made products, including engineering support and contracted services, were included.

“It is essential that policymakers have accurate information on the size of manufacturing sectors in order to develop an internationally competitive industrial strategy,” said Eoin O’Sullivan, one of the report’s authors.

“In particular, policymakers need to be able to measure manufacturing in a way that better reflects how firms actually organise themselves into value networks.”

While the Guardian news spin on the report focuses on the threat to the manufacturing by tariffs on exports resulting from a no deal Brexit, the report has wider implications. Manufacturing has long been seen as in decline and is accordingly unattractive as a careers option when compared to the growing service sector. Yet the report shows the continuing importance of occupations like engineering.

Automation and the future of work: the Chatbot

April 8th, 2019 by Graham Attwell

According to the Office for National Statistics, around 1.5 million jobs in England are at high risk of some of their duties and tasks being automated in the future.

The ONS analysed the jobs of 20 million people in England in 2017, and has found that 7.4% are at high risk of automation.

Automation involves replacing tasks currently done by workers with technology, which could include computer programs, algorithms, or even robots.

Women, young people, and those who work part-time are most likely to work in roles that are at high risk of automation.

It is important to understand automation as it may have an impact on the labour market, economy and society and on the skills and qualifications young people will need in the future.

The ONS have developed a chatbot for people to find out more about automation. You can try it out below and you can download the data here.

Understanding Labour Market data

April 8th, 2019 by Graham Attwell

The increasing power of processors and the advent of Open Data provides us information in many areas of society including about the Labour Market. Labour Market data has many uses, including for research in understandings society, for economic and social planning and for helping young people and older people in planning and managing their occupation and career.

Yet data on its own is not enough. We have to make sense and meanings from the data and that is often not simple. Gender pay gap figures released by the UK Office of National Statistics last week reveal widespread inequality across British businesses as every industry continues to pay men more on average than women. This video Guardian journalist Leah Green looks at the figures and busts some of the common myths surrounding the gender pay gap.

Empower to Shape Change: Learning and Identities in the Changing World of Work

March 21st, 2019 by Graham Attwell

Empower-to-Shape-Change

As regular readers of this blog will know, Pontydysgu were members of a consortium in a project called EmployID, funded by the European Commission. The project focused on changing work identities in Public Employment services and how technology could be used to support Continuing Professional Development, including both formal learning and informal learning.

All too often such project produce a series of fairly unintelligible reports before they face away. We were determined not to replicate this pattern. Instead of producing a  series of annual reports for the EU based on different project work packages, for three years of the project we produced an an unified annual report in the form of an ebook.

And the EmployId Consultancy Network , formed out of the project has now produced a short book, designed for individuals and organisations interested in organisational transformations, changing identities and learning.

The EmployId Consultancy Network is a network of researchers, practitioners and trainers offering tailored services for solutions around facilitating staff development with the focus on professional identity transformation (among them are myself, Luis Manuel Artiles Martinez, Pablo Franzolini, Deirdre Hughes, Christine Kunzmann, John Marsh, Andreas P. Schmidt, Jordi Fernández Vélez, Ranko Markus, Karin Trier, Katarina Ćurković and Adrijana Derossi).

This is what the book is about:

The world of work is undergoing fundamental transformations.

For example, nurses have mostly chosen their job because they want to care for their patients, but their work now involves, to a large degree, computer-based documentation and quality assurance measures. Practitioners in public employment services turn from administrating unemployment benefits into coaches for their clients. And engineers need to make sense of large scale sensor data and assess the opportunities of artificial intelligence techniques for their companies’ future services.We see technological developments such as digitization and automation in an ever increasing number of sectors and intensity.

Are you embracing and shaping the change or are you being driven by it?

Companies and public sector organisations have to reshape their value creation processes and guide their employees to new job roles, creating an uncertain outlook. Ask yourself are you embracing and shaping change, or are you being driven by it? The ability to utilise modern technologies and methods is simply scratching the surface. Overcoming resistance to change, stressful conflicts, and lack of openness are major road blocks. We also need to look at a deeper level of learning. Employees need to rethink their job roles, their relationship to others, and what a successful working environment means to them.

Employees and Leaders need to take new approaches to match the new responsibilities

This indicates the importance of the professional identity of individuals and occupational groups. Employees are often not given opportunities to engage in reflective learning conversations. There is a need for workers to consider the emotional aspects of their work and identity. It is important that they also acquire the skills needed to work effectively with others to move from a problem focus to a solution focus and help each other in their learning process.

In this short book, we look at strategies to empower and shape change, including the role of technology and identity transformation for learning in the workplace.The contents of this book follow a deliberate path focusing on contemporary themes. It is aimed at practitioners, managers, researchers and policymakers.

You can download a free PDF copy of the book here. Or you can order the paperback version on Amazon for Euro 14.40.

Developing a skills taxonomy

February 6th, 2019 by Graham Attwell

This morning’s mailing from the Marchmont Employment and Skills Observatory reports that NESTA have launched an interesting new Tool – a UK skills taxonomy:

“Skill shortages are costly and can hamper growth, but we don’t currently measure these shortages in a detailed or timely way. To address this challenge, we have developed the first data-driven skills taxonomy for the UK that is publicly available. A skills taxonomy provides a consistent way of measuring the demand and supply of skills. It can also help workers and students learn more about the skills that they need, and the value of those skills.” NESTA

It should help with careers guidance and is ideal for people looking at the return to differing career choices and how you get there. NESTA began with a list of just over 10,500 unique skills that had been mentioned within the descriptions of 41 million UK job adverts, collected between 2012 and 2017 and provided by Burning Glass Technologies. Machine learning was used to hierarchically cluster the skills. The more frequently two skills appeared in the same advert, the more likely it is that they ended up in the same branch of the taxonomy. The taxonomy therefore captures ‘the clusters of skills that we need for our jobs’.

The final taxonomy can be seen here and has a tree-like structure with three layers. The first layer contains 6 broad clusters of skills; these split into 35 groups, and then split once more to give 143 clusters of specific skills. Each of the approximately 10,500 skills lives within one of these 143 skill groups.

The skills taxonomy provide a rich set of data although requiring some work in interpretation. The six broad clusters of skills are:

The ten clusters (at the third layer) containing the most demanded skills are:

  1. Social work and caregiving
  2. General sales
  3. Software development
  4. Office administration
  5. Driving and automotive maintenance
  6. Business management
  7. Accounting and financial management
  8. Business analysis and IT projects
  9. Accounting administration
  10. Retail

The five skill clusters at the third layer with the highest annual median salaries are:

  1. Data engineering
  2. Securities trading
  3. IT security operations
  4. IT security standards
  5. Mainframe programming

The five clusters with the lowest salaries are:

  1. Premises security
  2. Medical administration
  3. Dental assistance
  4. Office administration
  5. Logistics administration

While the taxonomy is based on web data collected between 2012 and 2017, the approach has teh potential to be developed on the basis of real time data. And it is likely to be only one of a number of tools produced in the next two years using machine learning to analyse large data sets. The use of real-time data from web vacancies is receiving a lot of attention right now.

There is also interest in the idea of skills clusters in the ongoing debate over the impact of Artificial Intelligence on jobs and employment. Rather than whole occupations disappearing (and others surviving) it is more likely that the different skills required within occupations may change

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    Erasmus+

    The European Commission has published an annual report of the Erasmus+ programme in 2018. During that time the programme funded more than 23,500 projects and supported the mobility of over 850,00 students, of which 28,247 were involved in UK higher education projects, though only one third of these were UK students studying abroad while the remainder were EU students studying in the UK. The UK also sent 3,439 HE staff to teach or train abroad and received 4,970 staff from elsewhere in the EU.


    Skills Gaps

    A new report by the Learning and Work Institute for the Local Government Association (LGA) finds that by 2030 there could be a deficit of 2.5 million highly-skilled workers. The report, Local Skills Deficits and Spare Capacity, models potential skills gaps in eight English localities, and forecasts an oversupply of low- and intermediate -skilled workers by 2030. The LGA is calling on the government to devolve the various national skills, retraining and employment schemes to local areas. (via WONKHE)


    Innovation is male dominated?

    Times Higher Education reports that in the UK only one in 10 university spin-out companies has a female founder, analysis suggests. And these companies are much less likely to attract investment too, raising concerns that innovation is becoming too male-dominated.


    Open Educational Resources

    BYU researcher John Hilton has published a new study on OER, student efficacy, and user perceptions – a synthesis of research published between 2015 and 2018. Looking at sixteen efficacy and twenty perception studies involving over 120,000 students or faculty, the study’s results suggest that students achieve the same or better learning outcomes when using OER while saving a significant amount of money, and that the majority of faculty and students who’ve used OER had a positive experience and would do so again.


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