Archive for the ‘Data’ Category

Making sense of data about education and jobs

June 6th, 2016 by Graham Attwell

restorer
High or low skills? Graduate job or not?

For a number years now I have been working on projects developing the use of open data for careers counselling, advice and guidance. This work has been driven both by the increasing access to open data but also by the realisation of the importance of Labour Market Information (LMI) for those thinking about future education and / or jobs. And of course with high levels of job insecurity, such thinking becomes more urgent and in an unstable economy and labuor market, more tricky.

Yet even if we clean the data, add it to a database, provide and open API for access and develop tools for data visualisation, interpretation is still not easy. Here is one case, taken from this mornings Guardian newspaper.

employment graph

 

Although the article is using the chart to show the rapid growth in knowledge intense occupations, I am not sure it does. Assuming that these are percentage change based on the original job totals, it probably show growth in low skilled jobs is far outstripping high skilled work, especially in the last 12 months. And that is taking into account that (once again probably) most job loss due to technology is focused din low skilled areas – e.g the quoted 70,00 jobs lost in supermarket check outs due to automation.

I am also interested to see from wonkhe that “The Higher Education Statistics Agency (HESA) who have been running the Destination of Leavers Survey (DLHE) and its predecessors for 21 years, are now consulting widely on the future of assessing graduate outcomes.” For some time now there has been disquiet about the numbers of graduates working in ‘non graduate’ jobs. And that raises questions – just like the graph above focusing on high skills occupations – on just what a graduate job is. André Spicer, professor of organisational behaviour at the Cass Business School, City University London has cited “studies suggesting that the jobs which require degree-educated employees have peaked in 2000 and may be going down” and notes that many people apparently employed for their high-level specialist skills end up doing sales and marketing or fairly routine generalist work.

All this of course is highly subversive. Officially we are moving towards a high skilled economy needing more graduates and requiring higher level apprenticeships. My feeling in country slick Spain with high youth unemployment is what we need are apprenticeships in areas like construction and hospitality – both because they are sectors which can provide employment and also where higher skills are desperately needed to improve quality and productivity. Yet for governments there is an awful temptation to launch programmes in new ‘sexy’ areas  like games technologies despite the scarcity of jobs in these fields.

Tensions in Learning Analytics

May 27th, 2016 by Graham Attwell

The debate around Learning Analytics seems to be opening up. And although there is little sign of agreement over future directions, the terms of discussion seem both broader and more nuanced than previously. I think some of this is in response to the disillusionment of early researchers and adopters.

In yesterdays OLDaily, Stephen Downes pointed to an excellent article by Bodong Chen. Bodong points to the surge of interest in Learning Analytics but cautions that: “The surge of this nascent field rests on a promise–and also a premise–that digital traces of learning could be turned into actionable knowledge to promote learning and teaching.

He suggests that: “One approach to understanding learning analytics is to recognize what are not learning analytics” including academic analytics and educational data mining. Instead, he says “learning analytics is more directly concerned with teachers and learners by attending to micro-patterns of learning.”

Bodong draws attention to a tension between learning and analytics “as two pivotal concepts of the field” He points out that “learning analytics deals with educational phenomena at multiple levels”. As an example he says: “collaborative knowledge building as a group phenomenon depends on contributions from individuals, but cannot be reliably inferred from individual learning.”

Understanding and accepting that “the meaning of learning analytics as a term is plural and multifaceted” is an important basis for future research. Within the only just emerging field of workplace Learning Analytics, not only is there the issue of individual and collaborative learning and knowledge development but also issues around proxies for learning. Whilst performance in practice might be seen as a possible proxy, performance may also be seen to involve a wider range of factors, including the working environment, the division of work and opportunities for practice. And the already established field of Performance Analytics seems at considerable tension to learning.

Lack of proxies a problem for Workplace Learning Analytics

May 3rd, 2016 by Graham Attwell
I’ve been spending a lot of time thinking about Learning Analytics lately and this is the first of four or five short posts on the subject. Its all been kicked off by attending the Society of Learning Analytics pre conference workshops last week – LAK16 – in Edinburgh. Sadly I couldn’t afford the time and money to go to both the workshops and the full conference but many of the presentations and papers from the conference are already viable online.
My interest in Learning Analytics stems from the EmployID project which is aiming to support scalable and cost-effective facilitation of professional identity transformation in public employment services. And in our project application under the EU Research Framework (Horizon 2020) we said we would research and develop Learning Analytics services for staff in Public Employment Services. Easier said than done! An early literature review revealed that despite present high levels of interest (hype?) in Learning Analytics in formal education there has been very little research and development in Workplace Learning Analytics: therefore my excitement at a workshop on this subject at LAK16. But sadly despite the  conference selling out with 400 attendees, we only had four papers submitted for the workshop and just 11 attendees. What this did allow was a lot of in-depth discussion, which has left me plenty of issues to think about. And of course one of the issues we discussed was why there is apparently so little interest in Workplace Learning Analytics. It was pointed out that there have been a number of work oriented presentations in previous LAK conferences but these had remained isolated with no real follow up and with no overall community emerging.
There was also a general feeling that the Learning Analytics community was weak in terms of learning theory and pedagogy, both of which were censored central to Workplace Learning Analytics. But perhaps most importantly Learning Analytics approaches in schools and Higher Education lean heavily on proxies for learning, for instance examination results and grades. With the lack of such proxies for learning in the workplace, Learning Analytics has to focus on real learning – usually in the absence of a Learning Management System. And that is simply very hard to design and develop.Yet having said that, most if not all of us in the workshop were convinced that the real future of Learning Analytics in in the workplace, with a focus on understanding learning including informal learning and improving both learning and the environment in which it occurs.
We agreed on some modest next steps and will be launching a LinkedIn Group in the near future. In the meantime the papers and presentation from the workshop can be found at http://learning-layers.eu/laforwork/.

The challenge for Learning Analytics: Sense making

January 28th, 2016 by Graham Attwell

https://sylviamoessinger.files.wordpress.com/2012/06/learninganalytics_chalkboard.jpg

Image: Educause

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.

Predicting mid and long term skills needs in the UK

June 24th, 2015 by Graham Attwell

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.

 

Workplace Learning Analytics

June 16th, 2015 by Graham Attwell

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. socialllearningplatform

 

In the case of EmployID these questions could be linked to the six different foci of the Social Learning Platform, namely:

  • Support for facilitation roles
  • Structuring identity transformation activities
  • Supporting networking in personal networks
  • Supporting organisational networks
  • Supporting cross organisational dialogue
  • Providing social networking facilitation
  • Supporting networking in teams

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.

Workplace Learning and Learning Analytics

April 15th, 2015 by Graham Attwell

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?

Learning Analytics

March 23rd, 2015 by Graham Attwell

Intro to learning analytics universities scotland_dec2014_smn from Sheila MacNeill

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.

Open Data App Challenge

November 12th, 2014 by Graham Attwell

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.

The session included Ralph Cochrane (App Challenge), Louise Corti (UK Data Service), Jonathan Raper (Transport API), Olivia Ely (UKCES), Moeen Khwaja (Thingful)

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.”

Marx, use value, exchange value and social networks

October 13th, 2014 by Graham Attwell

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!

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    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.


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