Its true Twitter can be a distraction. But it is an unparalleled resource for new ideas and learning about things you didn’t know you wanted to learn about. This morning my attention was drawn by a Tweet linking to a interview in Times Higher Education with Todd Rose entitled “taking on the ‘averagarians’.” Todd Rose believes that “more sophisticated examples of “averagarian” fallacies – making decisions about individuals on the basis of what an idealised average person would do – are causing havoc all round.” The article suggests that this applies to higher education giving the example that “Universities assume that an average student should learn a certain amount of information in a certain amount of time. Those who are much quicker than average on 95 per cent of their modules and slower than average on 5 per cent may struggle to get a degree.”
It seems to me that this is one of the problems with Data Analytics. It may or may not matter that an individual is doing better or worse than the average in a class or that they spend more or less time reading or even worse logged on to the campus VLE. Its not that this data isn’t potentially useful but it is what sense to make of it. I’m currently editing a paper for submission to the workshop on Learning Analytics for Workplace and Professional Learning (LA for Work) at Learning Analytics and Knowledge Conference (LAK 2016) in April (I will post a copy of the paper here on Sunday). And my colleague Andreas Schmidt has contributed what I think is an important paragraph:
Supporting the learning of individuals with learning analytics is not just as designers of learning solutions how to present dashboards, visualizations and other forms of data representation. The biggest challenge of workplace learning analytics (but also learning analytics in general) is to support learners in making sense of the data analysis:
- What does an indicator or a visualization tell about how to improve learning?
- What are the limitations of such indicators?
- How can we move more towards evidence-based interventions
And this is not just a individual task; it requires collaborative reflection and learning processes. The knowledge of how to use learning analytics results for improving learning also needs to evolve through a knowledge maturing process. This corresponds to Argyris & Schön’s double loop learning. Otherwise, if learning analytics is perceived as a top-down approach pushed towards the learner, it will suffer from the same problems as performance management. These pre-defined indicators (through their selection, computation, and visualization) implement a certain preconception which is not evaluated on a continuous basis by those involved in the process. Misinterpretations and a misled confidence in numbers can disempower learners and lead to an overall rejection of analytics-driven approaches.
Labour Market Information (LMI) is not perhaps the most popular subject to talk about. But with the advent of open and linked data, LMI is increasingly being open up to wider audiences and has considerable potential for helping people choose and plan future careers and plan education programmes, as well as for use in research, exploring future skills needs and for social and economic planning.
This is a video version of a presentation by Graham Attwell at the Slovenian ZRSZ Analytical Office conference on “Short-term Skills Anticipations and Mismatch in the Labour Market. Graham Attwell examines ongoing work on mid and long term skills anticipation in the UK. He will bases on work being undertaken by the UK Commission for Employment and Skills and the European EmployID project looking, in the mid term, at future skills needs and in the longer term at the future of work. He explains the motivation for undertaking these studies and their potential uses. He also explains briefly the data sources and statistical background and barriers to the wok on skills projections, whilst emphasising that they are not the only possible futures and can best serve as a a benchmark for debate and reflection that can be used to inform policy development and other choices and decisions. He goes on to look at how open and linked data is opening up more academic research to wider user groups, and presents the work of the UKCES LMI for All project, which has developed an open API allowing the development of applications for different user groups concerned with future jobs and future skills. Finally he briefly discusses the policy implications of this work and the choices and influence of policymakers in influencing different futures.
EmployID is an EU-funded, four-year project which aims to support Public Employment Services staff to develop competences that address the need for integration and activation of job seekers in fast changing labour markets. According to the official flyer: “It builds upon career adaptability and resilience in practice, including quality and evidence- based frameworks for enhanced individual and organisational learning. It also supports the learning process of PES practitioners and managers in their professional identity development by supporting the efficient use of technologies to provide advanced coaching, reflection, networking and learning support services as well as MOOCs.”
One of the aims for research and development is to introduce the use of Learning Analytics within Public Employment Services. Although there is great interest in Learning Analytics by L and D staff, there are few examples of how Learning Analytics might be implanted in the workplace. Indeed looking at research reported by the Society for Learning Analytics Research reveals a paucity of attention to the workplace as a learning venue.
In this video, Graham Attwell proposes an approach to Workplace Learning Analytics based on the Social Learning Platform model (see diagram) adopted by the Employ ID project. He argues that rather merely fathering together possible data and then trying to work out what to do with it, data needs to be sought which can answer well designed research questions aiming to improve the quality of learning and the learning environment.
In the case of EmployID these questions could be linked to the six different foci of the Social Learning Platform, namely:
For some of these activities we already have collected some “docital traces” for instance data on facilitation roles through within a pilot MOOC. In other cases we will have to think how best to develop tools and approaches to data gathering, both qualitative and quantitative.
The video has been produced to coincide with the launch of The Learning Analytics Summer Institute, a strategic event, co-organized by SoLAR and host institutions and by a global network of LASI-Locals who are running their own institutes.
I have been looking hard at Learning Analytics in the last month. In particular, as part of the European EmployID project application, as a bit of a not really thought through objective, we said we would experiment with the use of Learning Analytics in European Public Employment Services. this raises a series of issues which I will come back to in future ports. It seems to me that whilst there is much talk around the potential of Learning Analytics in the workplace, there is very limited research and actual applications.
One of the reasons for this is that so much learning in the workplace in informal. As Boud and Hager (2012) say:
learning is a normal part of working, and indeed most other social activities. It occurs through practice in work settings from addressing the challenges and problems that arise. Most learning takes place not through formalized activities, but through the exigencies of practice with peers and others, drawing on expertise that is accessed in response to need. Problem-solving in which participants tackle challenges which progressively extend their existing capabilities and learn with and from each other appears to be common and frequent form of naturalistic development.
I would also add that much workplace learning is also driven through personal interest – a fact that is largely ignored and which has considerable economic implications in terms of workplace competence development. Although we can dream of a world where water cooler conversations are recorded by smart devices and sensors and added to other traces of digital activity, I am not sure this is a desirable outcome. So we have a challenge. most (university and formal education based) learning analytics focus on analysing digital interactions in, for example, a VLE. How can we sensibly and ethically extend data capture and analysis to informal workplace learning?
I am getting increasingly interested in Learning Analytics. But the more I think about it, the more questions I have. I am impressed with the Jisc project on Learning Analytics on which this presentation is based.
Pontydysgu are working with the UK Data Service to open up three datasets under an open data license and then run an Open Data App Challenge during late spring/summer 2015. This a ESRC (Economic Social Research Council) innovation fund project.
Last Friday I went to a UK Data Service panel session and networking event at the Open Data Institute in London talking about our work and the issues around opening up data under an open data license. The audience was mostly App Challenge members and data owners. This event was held as part of the ESRC Festival of Social Science Week and we invited along some other experts as well.
Ralph has wriiten about the event on the Open Data Challenge web site. “The UKCES and their LMI for All programme have one of the best developed government APIs for accessing open data around jobs, careers and employment statistics)” he says.
“Transport API is the leading provider of open transport data in the UK. Anyone can sign up to their API on a pay per use basis. They have data relating to trains, roads, construction and even Heathrow airport.
Thingful is a discovery or search engine for the Internet of Things. There are many sensors and devices out there that publish their state and if you can link these as a data stream they can enrich many other datasources and services. For example, there are weather sensors on top of most high rise buildings in London. Could they be connected to the Met Office to help with weather based planning?
Louise is the project leader for the Open Data App Challenge project and is based at the University of Essex campus in Colchester.
Ralph Cochrane moderated this panel session and is the founder of App Challenge. He’s a crowdsourcing expert and runs the developer community day-to-day working with many of the world’s leading companies.”
I have to admit I am not a great fan of lectures on line. there seems far to little human interaction and the slick production of things like the TED talks has got both ‘samey’ and somewhat tedious. But I loved this lecture by David Harvey on Karl Marx delivered in Amsterdam with no slides and no notes! As the blurb says “David Harvey is a Distinguished Professor of Anthropology & Geography at the Graduate Center of the City University of New York (CUNY), and the author of numerous books. He has been teaching Karl Marx’s Capital for over 40 years.”
David Harvey does not shy away from the politics of Karl Marx. But his focus is on Marx’s writings and ideas as a tool for social science and analysis. For those of you without the time, interest or patience to listen to the whole video the particular bits I found interesting include his ideas around rational consumption (about 30 minutes in), the idea of accumulation by dispossession (some 38 minutes in), the idea of management of the ommons important (after about 47 minutes) and contradictions over the role of the state (towards the end of the lecture and before the discussion).
Harvey talks a lot about contradictions – the biggest being the contradiction between use value and exchange value. As Wikipedia explains: “In Marx’s critique of political economy, any product has a labor-value and a use-value, and if it is traded as a commodity in markets, it additionally has an exchange value, most often expressed as a money-price. Marx acknowledges that commodities being traded also have a general utility, implied by the fact that people want them, but he argues that this by itself tells us nothing about the specific character of the economy in which they are produced and sold.”
Much of David Harvey;s work has been in the area of urban development and housing and he explains how this contradiction applies there and its implications. But it may also be a useful explanation of understanding what is happening with social networks. Social networks have a use value for us all in allowing us to stay in touch with friends, develop personal learning networks, learn about new ideas or just letting off steam to anyone who will listen. OK – the exchange value is not expressed as a money price. But most people now realise that social networking applications are seldom free. Instead of paying money we give our data away for them to use. And in turn they use this data to try to extract money from us through buying commodities. This is all fine as long as the use value exceeds the exchange value. But as social network providers try to monetise their products they are constantly upping the ante in terms of exchange value. In other words we are increasingly being required to sign over our data as well as our privacy in order to use their applications.
Alternatively social networks are trying to push ever more commodities at us. An article in the Gaurdian newspaper yesterday over Twitters attempts to build a business model noted: “Chief executive Dick Costolo has talked longingly about growing, and eventually making money from, the huge number of people who view tweets without signing up. This is fine on YouTube, where most of us watch the content without producing it and only sigh a little as we’re forced to watch ads when we do so. In contrast, sponsored tweets are a bit like being asked to pay for gossip from your colleague over the coffee machine.”
All this means more and more people are questioning whether the use value of Facebook and Twitter is worth the exchange value.
And such contradictions are hard to resolve!
Much of the focus of the open education movement has been on Open Educvational Resources and MOOCs. But just as important, in my humble opinion, is opening up research to a wider public. This is not only confined to opening up access to the results of research but allowing access to a wider audience than acandmicsx to raw research data. And there are a growing number of web sites that are doing this. One of the sites i am loving is the Understanding Society website based on the UK Households survey and run by designed and managed by a team of longitudinal survey experts at the Institute for Social and Economic Research (ISER), at the University of Essex.
Understanding Society, they say, “is a unique and valuable academic study that captures important information every year about the social and economic circumstances and attitudes of people living in 40,000 UK households.
It also collects additional health information from around 20,000 of the people who take part.
Information from the longitudinal survey is primarily used by academics, researchers and policy makers in their work, but the findings are of interest to a much wider group of people including those working in the third sector, health practitioners, business, the media and the general public.”
One study based on the survey and recently posted on the Understanding Society web site looks at Gender differences in educational aspirations and attitudes land examined the ambitions and approaches to study of 11-15 year olds participating in the British Household Panel Survey.
The sudy says that “while girls have more positive aspirations and attitudes than boys, the impacts of gender on children’s attitudes and aspirations vary significantly with parental education level, parental attitudes to education, child’s age and the indirect cost of education.
Boys are more responsive than girls to positive parental characteristics, while educational attitudes and aspirations of boys deteriorate at a younger age than those of girls.
Girls also acknowleged the impact of the recession and increased youth unemployment by working harder. Boys however appear unresponsive to the business cycle. This might reflect misplaced confidence where they believe they will be able to find a job independently from the economic climate. Policies targeting boys with more information on the benefits from investing in education will increase their awareness about the consequences of an unfavourable youth labour market, which may improve their educational attitudes and aspirations and consequently their educational attainment.”
I’m not sure what is make of all this. But I wonder if there is any comparative data from other countries? No doubt it would be a chnallenge to norm such data, but it could greatly help in understanding why boys in the UK are underperforming. If you know of such data plese just add a comment or drop me an email.
As promised, a post on our stand and presentation at Alt-C on the LMIforAll Labour Market Data project, sponsored by UKCES. Working together with the Institute for Employment Research at Warwick University and Raycom, we have developed a database and APi providing access to a range of data about a wide variety of different occupations in the UK including data about:
The API is self documenting and is available free of charge to both for profit and not for profit organisatio0ns and developers. Working with Loud Source we have run a competition for Apps built on the API and together with Rewired State we have organised a series of Hack Days and Mod Days. We are currently redesigning the website to provide better access to the data and to the different applications that have been built to date.
One strange thing that took people visiting our stand some time to understand was that we were not selling anything (I think ours and Jisc were the only non commercial stands). The second thing was that we were not trying to ‘sell’ them a shiny out of teh box project. To get added value from our database and API requires some thought and development effort on the part of organisations wanting to use the data. We provide the tools, they provide the effort to use them. But when people got that concept they were enthusiastic. And most interestingly they were coming up with completely new ideas for where the data might be valuable. As you can see in our presentation above, we have largely focused on the use of LMIforAll for careers planning. University and Further Education researchers and developers saw big potential using the API as a planning too for future courses and curriculum. Others saw it as a valuable resource for measuring employability, a big agenda point for many UK institutions. It was also suggested to us that the labour market data could be mashed together with data derived from learning analytics, providing possibly a more learner centred approach to analytics than has previously been deployed.
If you are interested in any of these ideas have a play on the LMIforAll web site. And feel free to get in touch if you have any questions.
We will broadcast from Berlin on the 3rd and the 4th of December. Both times it will start at 11.00 CET and will go on for about 45 minutes.
Go here to listen to the radio stream: SoB Online EDUCA 2015 LIVE Radio.
Or go to our new stream webpage: Sounds of the Bazaar Radio Stream Page
Teachers and overtime
According to the TES teachers in the UK “are more likely to work unpaid overtime than staff in any other industry, with some working almost 13 extra hours per week, according to research.
A study of official figures from the Trades Union Congress (TUC) found that 61.4 per cent of primary school teachers worked unpaid overtime in 2014, equating to 12.9 additional hours a week.
Among secondary teachers, 57.5 per cent worked unpaid overtime, with an average of 12.5 extra hours.
Across all education staff, including teachers, teaching assistants, playground staff, cleaners and caretakers, 37.6 per cent worked unpaid overtime – a figure higher than that for any other sector.”
The future of English Further Education
The UK Parliament Public Accounts Committee has warned the declining financial health of many FE colleges has “potentially serious consequences for learners and local economies”.
It finds funding and oversight bodies have been slow to address emerging financial and educational risks, with current oversight arrangements leading to confusion over who should intervene and when.
The Report says the Department for Business, Innovation & Skills and the Skills Funding Agency “are not doing enough to help colleges address risks at an early stage”.
Skills in Europe
Cedefop is launching a new SKILLS PANORAMA website, online on 1 December at 11.00 (CET).
Skills Panorama, they say, turns labour market data and information into useful, accurate and timely intelligence that helps policy-makers decide on skills and jobs in Europe.
The new website will provide with a more comprehensive and user-friendly central access point for information and intelligence on skill needs in occupations and sectors across Europe. You can register for the launch at Register now at http://skillspanorama.cedefop.europa.eu/launch/.
Talking about ‘European’ MOOCs
The European EMMA project is launching a webinar series. The first is on Tuesday 17 November 2015 from 14:00 – 15:00 CET.
They say: “In this first webinar we will explore new trends in European MOOCs. Rosanna de Rosa, from UNINA, will present the philosophy and challenges behind the EMMA EU project and MOOC platform developed with the idea of accommodating diversity through multilingualism. Darco Jansen, from EADTU (European Association of Distance Teaching Universities), will talk about Europe’s response to MOOC opportunities. His presentation will highlight the main difference with the U.S. and discuss the consequences for didactical and pedagogical approaches regarding the different contexts.