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

An article ‘Predicting Employment through Machine Learning‘ by Linsey S. Hugo on the National Association of Colleges and Employers web site,confirms some of my worries about the use of machine learning in education.

The article presents a scenario which it is said “illustrates the role that machine learning, a form of predictive analytics, can play in supporting student career outcomes.” It is based on a recent study at Ohio University (OHIO) which  leveraged machine learning to forecast successful job offers before graduation with 87 percent accuracy. “The study used data from first-destination surveys and registrar reports for undergraduate business school graduates from the 2016-2017 and 2017-2018 academic years. The study included data from 846 students for which outcomes were known; these data were then used in predicting outcomes for 212 students.”

A key step in the project was “identifying employability signals” based on the idea that “it is well-recognized that employers desire particular skills from undergraduate students, such as a strong work ethic, critical thinking, adept communication, and teamwork.” These signals were adapted as proxies for the “well recognised”skills.

The data were used to develop numerous machine learning models, from commonly recognized methodologies, such as logistic regression, to advanced, non-linear models, such as a support-vector machine. Following the development of the models, new student data points were added to determine if the model could predict those students’ employment status at graduation. It correctly predicted that 107 students would be employed at graduation and 78 students would not be employed at graduation—185 correct predictions out of 212 student records, an 87 percent accuracy rate.

Additionally, this research assessed sensitivity, identifying which input variables were most predictive. In this study, internships were the most predictive variable, followed by specific majors and then co-curricular activities.

As in many learning analytics applications the data could then be used as a basis for intervention to support students employability on gradation. If they has not already undertaken a summer internship then they could be supported in this and so on.

Now on the one hand this is an impressive development of learning analytics to support over worked careers advisers and to improve the chances of graduates finding a job. Also the detailed testing of different machine learning and AI approaches is both exemplary and unusually well documented.

However I still find myself uneasy with the project. Firstly it reduces the purpose of degree level education to employment. Secondly it accepts that employers call the shots through proxies based on unquestioned and unchallenged “well recognised skills” demanded by employers. It may be “well recognised” that employers are biased against certain social groups or have a preference for upper class students. Should this be incorporated in the algorithm. Thirdly it places responsibility for employability on the individual students, rather than looking more closely at societal factors in employment. It is also noted that participation in unpaid interneships is also an increasing factor in employment in the UK: fairly obviously the financial ability to undertake such unpaid work is the preserve of the more wealthy. And suppose that all students are assisted in achieving the “predictive input variable”. Does that mean they would all achieve employment on graduation? Graduate unemployment is not only predicated on individual student achievement (whatever variables are taken into account) but also on the availability of graduate jobs. In teh UK  many graduates are employed in what are classified as non graduate jobs (the classification system is something I will return to in another blog). But is this because they fail to develop their employability signals or simply because there simply are not enough jobs?

Having said all this, I remain optimistic about the role of learning analytics and AI in education and in careers guidance. But there are many issues to be discussed and pitfalls to overcome.

 

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

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.

Transferable skills and the future of work

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.

Developing a skills taxonomy

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

Four domains of learning

four development domaninspng

I came upon this text today when I was seeking to extend on an article I was writing that included the idea of learning in four domains. It was produced, I think, for the EmployID MOOC on the Changing World of Work and was probably written by Alan Brown and Jenny Bimrose.Sadly, I was so tied up with producing my own materials for the MOOC and didn’t get to read all of the other peoples. But at a time when there is a growing need to question to division between humanities and technical subjects, I think this offers a good way forward.

Relational development – learning with and from interacting with other people

A major route for relational development is learning through interactions at work, learning with and from others (in multiple contexts) and learning as participation in communities of practice (and communities of interest) while working with others. Socialisation at work, peer learning and identity work all contribute to individuals’ relational development. Many processes of relational development occur alongside other activities but more complex relationships requiring the use of influencing skills, engaging people for particular purposes, supporting the learning of others and exercising supervision, management or (team) leadership responsibilities may benefit from support through explicit education, training or development activities.

Jack from the UK had switched career and now who worked as a carer. From the outset Jack learned much about his work from engaging with residents in the care home as well as learning from other staff. He had received letters from residents expressing their gratitude, which had boosted his confidence. His manager encouraged him to become a trainer in the care home, and although nervous and unsure he delivered the training and his self-efficacy increased.

Cognitive development – acquiring knowledge and thinking skills

A major work-related route for cognitive development involves learning through mastery of an appropriate knowledge base and any subsequent technical updating. This form of development makes use of learning by acquisition and highlights the importance of subject or disciplinary knowledge and/or craft and technical knowledge, and it will be concerned with developing particular cognitive abilities, such as critical thinking; evaluating; synthesising etc.

Bernard, a Czech automotive worker, participated in a short internal company technical training programme which positively surprised him in terms of practical outcomes and motivated him to actively work on his vocational development. ‘You had to know your stuff, the trainer was extremely competent, he knew his field very well, but sometimes I had difficulties to follow him. Anyway, it was really done by professionals who knew their stuff, and I appreciated it very much. I was very satisfied. I learned lots of things that were later very useful for my work […] It was very interesting to meet people from a completely different and a rather specialised area. I learned a lot of things and I was proud of it. I think this was the moment that made me change my attitude towards learning. I became much more curious.’

Practical development – learning by doing, by experience, by taking on challenges

For practical development the major developmental route is often learning on the job, particularly learning through challenging work. Learning a practice is also about relationships, identity and cognitive development but there is value in drawing attention to this idea, even if conceptually it is a different order to the other forms of development highlighted in this representation of learning as a process of identity development. Practical development can encompass the importance of critical inquiry, innovation, new ideas, changing ways of working and (critical) reflection on practice. It may be facilitated by learning through experience, project work and/or by use of particular approaches to practice, such as planning and preparation, implementation (including problem-solving) and evaluation. The ultimate goal may be vocational mastery, with progressive inculcation into particular ways of thinking and practising, including acceptance of appropriate standards, ethics and values, and the development of particular skill sets and capabilities associated with developing expertise.

Davide, an Italian carpenter, saw learning as a practice-based process driven by curiosity, a spirit of observation, and trial and error. A major role was played by his passion for the transformation of matter, which he perceived as an almost sacred event: ‘It really struck me to see that from a piece of wood one can create a piece of furniture’.

Emotional development – making sense of your own feelings and how others feel 

For emotional development, the major developmental routes are learning through engagement,  reflexiveness that leads to greater self-understanding, and the development of particular personal qualities. Much emotional development may occur outside work, but the search for meaning in work, developing particular mind-sets, and mindfulness may be components of an individual’s emotional development. Particular avenues of development could include understanding the perspectives of others, respect for the views of others, empathy, anticipating the impact of your own words and actions, and a general reflexiveness, which includes exploring feelings. Identity development at work may also be influenced by changing ideas individuals have about their own well-being and changing definitions of career success (Brown & Bimrose 2014).

Henrik from Denmark switched career, moving into caring and developed a new relationship with his work, which he found much more emotionally engaging. While studying for his skilled worker qualification, Henrik immersed himself in individual assignments of his own choice. In one assignment, he developed a ‘product’ to help improve a pupil’s ability to communicate, an ability which was being lost due to a rare disease. When Henrik talked about the assignment he was very engaged and showed insight into the syndrome. Because the assignment was closely related to his experience and practice, he saw meaning in undertaking it: ‘It was as though there was a circle I could complete on my own.’ He received a top grade for the assignment, and it is evident that positive learning experiences and the perception of entering into learning processes that are meaningful to his life and work situation are strong motivating factors in his engagement in further learning.