Archive for the ‘Career Guidance’ Category

Case study. The Ada chatbot: personalised, AI-driven assistant for each student.

March 31st, 2020 by Graham Attwell

As part of the AI and vocational education and training project funded through the EU Erasmus plus project we are producing a series of case studies of the use of AI in VET in five European countries. Here is my first case study – the Ada chatbot developed at Bolton College.

About Bolton College

Bolton College is one of the leading vocational education and training providers in the North West of England, specialising in delivering training – locally, regionally and nationally – to school leavers, adults and employers. The college employs over 550 staff members who teach over 14,500 full and part time students across a range of centres around Bolton. The college’s Learning Technology Team has a proven reputation for the use of learning analytics, machine learning and adaptive learning to support students as they progress with their studies.

The Ada Chatbot

The Learning Technology Team has developed a digital assistant called Ada which went live in April 2017. Ada, which uses the IBM Watson AI engine, can respond to a wide range of student inquiries across multiple domains. The college’s Learning Technology Lead, Aftab Hussain, says “It transforms the way students get information and insights that support them with their studies.” He explains: “It can be hard to find information on the campus. We have an information overload. We have lots of data but it is hard to manage. We don’t have the tools to manage it – this includes teachers, managers and students.” Ada was first developed to overcome the complexity of accessing information and data.

Student questions

Ada is able to respond to student questions including:

  1. General inquiries from students about the college (for example: semester dates, library opening hours, exam office locations, campus activities, deadline for applying for university and more);
  2. Specific questions from students about their studies (for example: What lessons do I have today/this afternoon/tomorrow? Who are my teachers? What’s my attendance like? When is my next exam? When and where is my work placement? What qualifications do I have? What courses am I enrolled in? etc.)
  3. Subject specific inquiries from students. Bolton College is teaching Ada to respond to questions relating to GCSE Maths, GCSE English and the employability curriculum.

Personalised and contextualised learning

Aftab Hussein explains: “We are connecting all campus data sets. Ada can reply to questions contextually. She recognises who you are and is personalised according to who you are and where you are in the student life cycle. The home page uses Natural Language Processing and the Watson AI engine. It can reply to 25000 questions around issues such as mental health or library opening times etc. It also includes subject specific enquiries including around English, Mathematics and business and employability. All teachers have been invited to submit the top 20 queries they receive. Machine learning can recognise the questions. The technical process is easy.” However, he acknowledges that inputting data into the system can be time consuming and they are looking at ways of automatically reading course documentation and presentations.

All the technical development has been undertaken in house. As well as being accessible through the web, Ada, has both IOS and Android apps and can also be queried though smart speakers.

The system also links to the college Moodle installation and can provide access to assignments, college information services and curriculum materials. The system is increasingly being used in online tutorials providing both questions for participants and access to learning materials for instance videos including for health and social care.

It is personalised for individuals and contextualised according to what they are doing or want to find out. Aftab says: “We are looking at the transactional distance – the system provides immediate feedback reducing the transactional distance. “

Digital assessment

Work is also being undertaken in developing the use of the bot for assessment. This is initially being used for the evaluation of work experience, where students need to provide short examples of how they are meeting objectives – for example in collaboration or problem solving. Answers can uploaded, evaluated by the AI and feedback returned instantly.

Nudging

Since March 2019, the Ada service has provided nudges to students with timely and contextualised information, advice and guidance (IAG) to support their studies. The service nudges students about forthcoming exams, their work placement feedback and more. In the following example, a student receives feedback regarding his work placement from his career coach and employer.

The College is currently implementing ProMonitor, a service which will offer teachers and tutors with a scalable solution for managing and supporting the progress made by their students. Once ProMonitor is in place, Ada will be in a position to nudge students about forthcoming assignments and the grades awarded for those assignments. She will also offer students advice and guidance about staying on track with their studies. Likewise, Ada will nudge teachers and student support teams to inform them about student progress; allowing for timely support to be put in place for students across the College.

A personal lifelong learning companion

For Aftab Hussein the persona of the digital agent is important.

For Aftab Hussein the persona of the digital agent is important. He  thinks that in the future that chatbot will morph into a personal cognitive assistant that supports students throughout their entire educational life, from nursery school to university and beyond.

“The personal assistant will learn from each student throughout their life and adapt according to what they like, while guiding them through studies. It could remind when homework is due, book appointments with tutors, and point towards services and events that might support studies, for example.”

 

 

 

Discussion: Learning and Training anywhere

March 30th, 2020 by Graham Attwell

The International Labour Organization (ILO) have launched a E-Discussion on Continuing online learning and skills development in times of the COVID-19 crisis. The discussion started on 27 March and runs to 9 April.

The ILO say “the virtual discussion provides an opportunity to explore the concept of “learning and training anywhere, anytime”, an idea central to the concept of lifelong learning. This, in turn, requires examination of a range of issues such as how technically prepared we are to support new ways of working in the face of disruptors like a pandemic, and how quickly we can organize digital education and training and mobilize teachers and trainers to maintain services to learners.”

You can join the discussion at the following addresses

 

Careers identities in the Lockdown

March 30th, 2020 by Graham Attwell

Graham Attwell will be speaking at an online webinar – LiveCareerChat@Lockdown on 6 April. The webinar, organised by DMH Associates will focus on the future challenges for careers identities and careers advice and guidance

Deirdre Hughes says “During these turbulent times, we all have an opportunity for reflection, sharing ideas and offering practical advice on how best to manage career identity and changing work practices. This webinar is designed to bring people together and to listen and/or share experiences of careers support mechanisms at a time of crisis. ”

Graham Attwell will talk about the changing international labour markets and the challenges of new technologies, including AI and automation.

The webinar takes at 1630 – 1730 CEST on Monday 6 April and is free. You can register at https://dmhassociates.easywebinar.live/event-registration-3

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.

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    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)


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