Introduction

    Welcome to the Wales Wide Web

    October 25th, 2007 by Dirk Stieglitz

    Wales Wide Web is Graham Attwell’s main blog. Graham Attwell is Director of the Wales based research organisation, Pontydysgu. The blog covers issues like open-source, open-content, open-standards, e-learning and Werder Bremen football team.

    You can reach Graham by email at graham10 [at] mac [dot] com

    Wales Wide Web

    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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    Productivity, innovation, learning and ‘Place’

    September 3rd, 2019 by Graham Attwell

    Fig 7Antiguo-cauce-del-río-Turia-3The UK Centre for Cities has been undertaking a lot of interesting research on the future of cities. In a recent article on their website, they look at ‘why place matters when thinking about productivity. Productivity has been persistently low in the UK and the article discusses “‘Place’, one of the pillars of productivity identified by the Government’s Industrial Strategy” and how it interacts with the other four pillars – ‘People’, ‘Ideas’, ‘Business Environment and ‘Infrastructure’.

    Perhaps not surprisingly they find that. city centres offer inherent advantages to some businesses compared to those offered by rural areas. They also draw on previous research in finding that “broadly speaking, density is good for innovation…. the proximity of researchers to each other through co-location improves quality of output. Our work also finds that jobs in city centres are more productive than their counterparts elsewhere” although this preference is not universal.

    Infrastructure’ , they say, “is the pillar where the impact of ‘place’ is the most obvious. Proliferation of public transport systems is the most efficient solution to get people around in dense city centres where as a private car is the best way to travel in the countryside.”

    However it is the people pillar that I find most interesting and where I disagree with the article. “For the ‘people’ pillar, ‘place’ is indiscriminate – skill levels are the biggest determinant of outcomes everywhere.” The research has been taking place as part of the government drive to develop Local Industrial Strategies in England. Yet I do not think ‘place’ can be reduced to providing skills training courses. Our work in the EU funded CONNECT project suggests that as important, if not more so, is the promotion of opportunities for learning, through networks of different organizations including both the public and private sectors. Such organisations embrace cultural and social activities and adult education as well as formal skills training. And especially in dense cities like Valencia or Athens informal learning taking place in public spaces is critical. Such public spaces are frequently under pressure  from developers and policies need to be developed to preserve and extend such places. Thus any policy which looks at productivity and skills needs to take a wider viewpoint and in relation to cities, consider how public places play a role in sharing knowledge and developing social innovation.

     

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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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    Reading on screen and on paper

    September 1st, 2019 by Graham Attwell

    Do you read books and papers on screen or do you prefer paper. I am conflicted. I used to have an old Kindle but gave it up because I am no fan of Amazon. And I used to read books on firstly an ipad and latterly an Tesco Huddle tablet – both now sadly deceased.

    Like many (at least if the sales figures are to be believed) I have returned to reading books on paper, although I read a lot of papers and such like on my computer, only occasionally being bothered to print them out. But is preferring to physical books a cultural feel good factor or does it really make a difference to comprehension and learning?

    An article in the Hechinger Report reports on research by Virginia Clinton, an Assistant Professor at the University of North Dakota who “compiled results from 33 high-quality studies that tested students’ comprehension after they were randomly assigned to read on a screen or on paper and found that her students might be right.”

    The studies showed that students of all ages, from elementary school to college, tend to absorb more when they’re reading on paper than on screens, particularly when it comes to nonfiction material.

    However the benefit was small – a little more than  a fifth of a standard deviation and there is an important caveat in that the studies that Clinton included in her analysis didn’t allow students to use the add on tools that digital texts can potentially offer.

    My feeling is that this is a case of horses for courses. Work undertaken by Pontydysgu suggested that ebooks had an important motivational aspect for slow to learn readers in primary school. Not only could they look up the meaning fo different words but when they had read for a certain amount of time they were allowed to listen to the rest of teh story on the audio transcription. And there is little doubt that e-books offer a cost effective way of providing access to books for learners.

    But it would be nice to see some further well designed research in this area.

     

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    Learning, education outcomes and socioeconomic class

    August 30th, 2019 by Graham Attwell

    We have long known that educational outcomes are heavily influenced by social class. But little has been done to try to understand how social class affects learning. In that respect the article by Lien Pham on ‘How socioeconomic background makes a difference in education outcomes‘ is very welcome.

    Pham notes that although “PISA publishes its PISA context assessment framework to supplement its regular international PISA testing of reading, maths and science”, ” these are just snapshots rather than an analysis of the impact of students’ background characteristics on their participation in these processes, or whether the educational system, schooling processes and classroom practices may favour certain groups over others” and “they do not help to shed light on how and why some students perform better than others.”

    Pham says “In order to truly understand what is happening with inequality I believe we have to recognise the implicit social relationships and social structures in the schooling processes that position students in different vantage points.”

    Pham goes on to look at what PISA says about students’ family backgrounds, student ethnicity and polices to improve educational inequality, adding his own comments and analysis. His overall conclusion is that reducing inequality neds more than just access to economic resources

    We need to deeply understand students’ “real” opportunities within our systems of education. I believe we need to look more closely at what students can reasonably do (or not do) with those resources given their backgrounds and situations.

    Resources are important, but just because a school has a wide variety of resources doesn’t mean all of its students will benefit from those equally.

    I am arguing that policy attention to improve educational inequality should place student agency and diversity at the forefront, rather than focussing on resources with the assumption that all students will be able to access them in similar ways with similar outcomes.

    You can read more in his paper: Capital and capabilities in education: Re-examining Australia’s 2015 PISA performance and context assessment framework

     

     

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    Back to school

    August 30th, 2019 by Graham Attwell

    It’s the last day of the summer holidays.

    On Sunday I am traveling to Hamburg for the European Conference on Educational Research (ECER). I am not a great fan of conferences – al least the formal part. I have long campaigned for the ‘flipped conference’. All too often conferences just consist of researchers reading out their bullet points from their slides. Their is little chance to interrogate the ideas, less so to have a proper discussion about the work they are presenting. All too often presentations overrun with it being accepted that the ten or so minutes scheduled for discussion at the end of three or four presentations will be eaten up. And it is interesting that people still hark back to the Personal Learning environment conferences where we did at least try to do things differently. In reality the best bit of the conferences are usually in the informal discussions which take place outside the official sessions.

    Having said that I like the ECER conferences. One strength is the priority given to emerging researchers. Another is the international focus for ECER, not just in terms of attracting delegates from all over the world, but in stressing that presentation should focus on at east the European dimension of the research. A third advantage of ECER is that it covers many different areas of education through the 31 or so networks which organise the programme. This year, I am in a privileged position as I have been commissioned by the European Educational Research Association to make a series of short videos, interviewing the network conveners. The idea is that the videos provide a quick and informative way of people understanding the focus of the networks and the activities they are undertaking, including the increasing number of what EERA call ‘season schools’ (formerly summer schools but the changed nomenclature reflecting the fact that most take place outside the summer time). This week we are aiming to record 21 videos. It will be hard work but a lot of fun and for me a great learning opportunity.

    Of course, one of the attractions of conferences is the chance to meet up with old colleagues and friends. I will be in Hamburg all week. If you would like to meet up just drop me a line.

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