Projects to support Careers Advice, Guidance and Counselling

About the Projects to Suppport Careers Advice, Guidance and Counselling

Pontydysgu are involved in a number of project support Careers Advice, Guidance and Counselling. In the UK, Careers advice for young people aged 13-19 is provided through the regionally based Connexions service. The Connexions service was established in 2001 with the aim of providing a comprehensive service to meet young people’s needs for information, advice and support.

The aim of  Connexions is to provide high-quality, impartial, information, advice and guidance (including careers advice and guidance), together with access to personal-development opportunities to help remove barriers to learning and progression and ensure young people make a smooth transition to adulthood and working life.

Pontydysgu has been involved in a series of projects in conjunction with our partners, the University of Warwick and Raycom, and local Connexions services.

These projects include:

  • the development of an ePortfolio for use in Careers Guidance, Advice and counselling in schools
  • the development of tools for the visualisation of labour market data
  • the development of a web site for continuing professional development for Careers Personal Advisors
  • the development of web based tools and games for young people to help them explore particular careers
  • research into the potential of Web 2.0, social software and mobile devices for careers advice, guidance and counselling
  • the potential of on-line e-learning for continuing professional development for Careers Personal Advisors

This work is led for Pontydysgu by Graham Attwell and Dirk Stieglitz. If you are interested in any of these project or would like to know more about our work in this are please contact Graham Attwell – graham10 [at] mac [dot] com

Posts about projects to support Careers Advice, Guidance and Counselling

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.

 

 

 

 

 

 

 

 

 

 

 

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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

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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.

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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.

 

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

July 31st, 2019 by Graham Attwell

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.

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Skills for Green Jobs

June 14th, 2019 by Graham Attwell


Addressing climate change and setting economies and societies more firmly onto a path towards a sustainable, low-carbon future is one of the defining challenges of our time. Such shift will entail far-reaching transformations of our economies, changing the ways we consume and produce, shifting energy sources, and leveraging new technologies.

The European Centre for Vocational Education and Training, Cedefop, has released a new report on Skills for Green Jobs. The report is based on country studies undertaken in collaboration with the International Labour Organization (ILO) in six countries (Denmark, Germany, Estonia, Spain, France and the UK) since 2010.

A key outcome, says CEDEFOP, is that countries vary in their approach to defining, classifying and collecting data on green jobs and skills. However, they have observed increased efforts are observed on data collection on developments in the ‘green economy’.

Since 2010, green employment trends have tended to parallel general economic trends. Carbon reduction targets and associated incentives and subsidies have been especially influential on green jobs and skills; other green policies, such as legislation to protect the environment, have also been important.

Although few countries have a strategy on skills for green jobs, “the updating of qualifications and VET programmes has soared, reflecting increased demand for green jobs and skills since 2010.” Updates mainly concern adding ‘green’ components to existing qualifications/programmes, since changes in skill demands are perceived more pertinent to including new green skills within existing occupations rather than the creation of new green ones.

 

More information is available in the CEDEFOP magazine promoting learning for work, Skillset and Match.

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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.

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Transferable skills and the future of work

April 9th, 2019 by Graham Attwell

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.

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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.

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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.

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Project List

Current Projects

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    TACCLE 4 CPD

    CYCLE

    OurTown

    TACCLE 5 VET

    LMIforAll

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    B-Learning4All

    EmployID

    Euronet-PBL

    EuroTrainer II

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    RadioActive Europe

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    Seele

    Show your own Gold

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    Support Careers Guidance

    TACCLE

    TACCLE 2

    TACCLE 3 Coding

    TTplus

    WebQuest for HRM

    WLT

    YETI

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