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

    Does AI mean we no longer need subject knowledge?

    January 15th, 2020 by Graham Attwell

    I am a little bemused by the approach of many of those writing about Artificial Intelligence in education to knowledge. The recently released Open University Innovation Report, Innovating Pedagogy, is typical in that respect.

    “Helping students learn how to live effectively in a world increasingly impacted by AI also requires a pedagogy”, they say, “that, rather than focusing on what computers are good at (e.g. knowledge acquisition), puts more emphasis on the skills that make humans uniquely human (e.g. critical thinking, communication, collaboration and creativity) – skills in which computers remain weak.”

    I have nothing against critical thinking, collaboration or creativity, although I think these are hard subjects to teach. But I find it curious that knowledge is being downplayed on the grounds that computers are good at it. Books have become very good at knowledge over the years but it doesn’t mean that humans have abandoned it to the books. What is striking though is the failure to distinguish between abstracted and applied knowledge. Computers are very good at producing (and using) information and data. But they are not nearly as good at applying that knowledge in real world interactions. Computers (in the form of robots) will struggle to open a door. Computers may know all about the latest hair styles but I very much doubt that we will be trusting them to cut our hair in the near future. But of course, the skills I am talking about here are vocational skills – not the skills that universities are used to teaching.

    As opposed to the emergent Anglo Saxon discourse around “the skills that make humans uniquely human” in Germany the focus on Industry 4.0 is leading to an alternative idea. They are seeing AI and automation as requiring new and higher levels of vocational knowledge and skills in areas like, for example, the preventative maintenance of automated production machinery. This seems to me to be a far more promising area of development. The problem I suspect for education researchers in the UK is that they have to start thinking about education outside the sometimes rarified world of the university.

    Equally I do not agree with the reports assertion that most AI applications for education are student-facing and are designed to replace some existing teacher tasks. “If this continues”, they say “while in the short run it might relieve some teacher burdens, it will inevitably lead to teachers becoming side-lined or deprofessionalised. In this possible AI-driven future, teachers will only be in classrooms to facilitate the AI to do the ‘actual’ teaching.”

    The reality is that there are an increasing number of AI applications which assist tecahers rather than replace them – and that allow teachers to get on with their real job of teaching and supporting learning, rather than undertaking an onerous workload of admin. There is no evidence of the inevitability of teachers being either sidelined or deprofessionaised. And those experiments from Silicon Valley trying to ‘disrupy’ education by a move to purely online and algorithm driven learning have generally been a big failure.

     

     

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    Artificial, Intelligence, ethics and education

    January 2nd, 2020 by Graham Attwell

    I guess we are going to be hearing a lot about AI in education in the next year. As regular readers will know, I am working on a European Commission Erasmus Plus project on Artificial Intelligence and Vocational Education and Training. One subject which is constantly appearing is the issue of ethics. Apart from the UK universities requirements for ethical approval of research projects (more about this in a future post), the issue of ethics rarely appears in education as a focus for debate. Yet it is all over the discussion of AI and how we can or should use AI in education.

    There is an interesting and (long) blog post – ‘The Invention of “Ethical AI“‘ recently published by Rodrigo Ochigame on the Intercept web site.

    Orchigame worked as a graduate student researcher in the former director of the MIT Media Lab, Joichi Ito’s group on AI ethics at the Media Lab. He left in August last year , immediately after Ito published his initial “apology” regarding his ties to Epstein, in which he acknowledged accepting money from the disgraced financier both for the Media Lab and for Ito’s outside venture funds.

    The quotes below provide an outline of his argument although for anyone interested in this field the article merits a full read. the

    The emergence of this field is a recent phenomenon, as past AI researchers had been largely uninterested in the study of ethics

    The discourse of “ethical AI,” championed substantially by Ito, was aligned strategically with a Silicon Valley effort seeking to avoid legally enforceable restrictions of controversial technologies.

    This included working on

    the U.S. Department of Defense’s “AI Ethics Principles” for warfare, which embraced “permissibly biased” algorithms and which avoided using the word “fairness” because the Pentagon believes “that fights should not be fair.”

    corporations have tried to shift the discussion to focus on voluntary “ethical principles,” “responsible practices,” and technical adjustments or “safeguards” framed in terms of “bias” and “fairness” (e.g., requiring or encouraging police to adopt “unbiased” or “fair” facial recognition).

    it is helpful to distinguish between three kinds of regulatory possibilities for a given technology: (1) no legal regulation at all, leaving “ethical principles” and “responsible practices” as merely voluntary; (2) moderate legal regulation encouraging or requiring technical adjustments that do not conflict significantly with profits; or (3) restrictive legal regulation curbing or banning deployment of the technology. Unsurprisingly, the tech industry tends to support the first two and oppose the last. The corporate-sponsored discourse of “ethical AI” enables precisely this position.

    the corporate lobby’s effort to shape academic research was extremely successful. There is now an enormous amount of work under the rubric of “AI ethics.” To be fair, some of the research is useful and nuanced, especially in the humanities and social sciences. But the majority of well-funded work on “ethical AI” is aligned with the tech lobby’s agenda: to voluntarily or moderately adjust, rather than legally restrict, the deployment of controversial technologies.

    I am not opposed to the emphasis being placed on ethics in AI and education and the debate and practice son Learning Analytics show the need to think clearly about how we use technology. But we have to be careful that we firstly do not just end up paying lip service to ethics and secondly that academic research does not become a cover for teh practices of the Ed tech industry. Moreover, I think we need a clearer understanding of just what we mean when we talk about ethics in the educational context. For me the two biggest ethical issues are the failure of provide education for all and the gross inequalities in educational provision based on things like class and gender.

     

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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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    Understanding the gender pay gap

    December 5th, 2019 by Graham Attwell

    We have written before about the gender pay gap in the UK. According to the Office for National Statistics the average hourly (gross, excluding overtime) gender pay gap in the UK for all employees fell from 17.8 per cent in 2018 to 17.3 per cent in 2019. However, nee research has revealed cross-national gaps vary from as much as -5 per cent in Wigan to 32 per cent in Slough suggesting that only focusing on a national perspective might be overly simplistic.

    The Centre for Cities has found that 7 of the 10 cities with the highest gender pay gap are located either in the South East or East of England. They say that “as cities in these regions tend to perform economically better than cities in the North of England, economic performance seems to influence the gender pay gap in cities. In general, cities with higher average weekly earnings (e.g. Cambridge, London, Reading, Crawley, Slough) tend to have a higher gender pay gap.”

    Another factor the Centre for Cities things is driving higher gender pay gaps in the south of England is the bigger difference between men and women holding a managerial position. While 5.2 of men and 3.2 per cent of women in the north east hold such a position, 8.1 per cent of managers in the south east are men while only 4.4 per cent are women (data is not available below regional level).”

    Six out of the ten cities with the smallest gender pay gap are located in the North of England: Wigan, Burnley, Warrington, Sunderland, Blackburn and Middlesbrough. These cities have weaker economies and lower rates of employment

    The Centre for Cities has looked at the industrial composition of the labour market in Warrington and Wigan, finding that both cities have a higher share of jobs in education, human and health activities and social work than cities with higher gender pay gaps such as Slough and Crawley.

    The composition of sectors in and around cities is seen as important and since women are more likely to be employed in the public sector, for instance, as teachers, social workers and nurses, the gender pay gap tends to be lower in cities with a higher proportion of public sector jobs such as in Middlesbrough, Blackburn, Swansea and Glasgow.

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    The Circular Economy for Youth

    December 3rd, 2019 by Graham Attwell

    These are my slides from the recent online kick off meeting for the European Erasmus Plus Circular Economy for Youth project. The project will last two years and is coordinated by Pontydysgu. Other partners are from Greece, North Macedonia, Italy, Belgium and France.

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    Readings on AI and Education

    November 18th, 2019 by Graham Attwell

    In an early activity in our new project on Artificial Intelligence in Vocational Education and Training, we are undertaking a literature review. Although there seems to be little about AI and VET, the issue of AI in education is thsi years hot trend. Of course there seems to be more talk than actual practice. Any way, here is a quick summary (just notes really) of things I stumbled on last week.

    Perhaps most interesting was an online webinar organised by the European Distance Education Network (EDEN) as part of European Distance Learning Week.  According to the online platform there were 49 of us present and four presentations. Sadly the recording is not yet available but I will link to it once it is online. What was most interesting was that almost everyone who spoke, and I recognised quite a few prominent researchers in the contributions, were pretty much opposed to AI. Too dangerous, no benefit, just hype, developers with no idea about learning etc. Really only one speaker, Alexandra Cristea from Durham University could see potential.

    I found teh follwing publiscation by her. Demographic Indicators Influencing Learning Activities in MOOCs: Learning Analytics of Future Learn Courses (PDF) by Alexandra I. Cristea  and Lei Shi from the University of from Liverpool University  looks at pre-course survey data and online learner interaction data collected from two MOOCs, delivered by the University of Warwick,in 2015, 2016,and 2017. The data is used  to explore how learner demographic indicators may influence learner activities.Recommendations for educational information system development and instructional design, especially when a course attracts a diverse group of learners, are provided.

    Meanwhile in the UK, NESTA are continuing to promote AI. However, they too emphasis ethical issues with the use of the technology. In ‘Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges’ they say

    Although challenges for the ethical and responsible use of artificial intelligence and the sharing of data are common to many sectors, schools and colleges present a distinct combination of properties and considerations. The sharing of data needs to be governed in a manner that realises benefit for the public, and AIEd must be used ethically and responsibly.

    AIEd’s potential and risks is reflected in the views of parents. 61% of parents anticipate that AI will be fairly or very important to the classroom of the near future. However, many are fairly or very concerned about consequences of determinism (77%), accountability (77%) and privacy and security (73%).

    Finally, I had a look at the X5GON project website. X5GON is a large scale European research programme project, bringing togther a number of leading European Universities. It appears to be developing AI driven tools. particarrly focused on Open educational Resources. The project website says:

    This new AI-driven platform will deliver OER content from everywhere, for the students’ need at the right time and place. This learning and development solution will use the following solutions to accomplish this goal:

    • Aggregation: It will gather relevant content in one place, from the projects case studies as well as external providers and other preferred resources.
    • Curation: AI and machine learning will be key to curate relevant and contextual content and external students at the right time and point of need.
    • Personalization: It will make increasingly personalized recommendations for learning content to suit students’ needs, based on the analysis of relevant factors.
    • Creation: Large, small and medium-sized universities have tacit knowledge that can be unlocked and re-used. This approach will allow any organization to release and build their own content libraries quickly and conveniently to share with the world and vice versa.

    I’ll keep writing up my findings, in the form of notes on this site. And if anyone has any recommendations of what else I should be reading please add in the comments below.

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