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.

 

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.