Archive for the ‘Open Data’ Category

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.

 

Understanding Labour Market data

April 8th, 2019 by Graham Attwell

The increasing power of processors and the advent of Open Data provides us information in many areas of society including about the Labour Market. Labour Market data has many uses, including for research in understandings society, for economic and social planning and for helping young people and older people in planning and managing their occupation and career.

Yet data on its own is not enough. We have to make sense and meanings from the data and that is often not simple. Gender pay gap figures released by the UK Office of National Statistics last week reveal widespread inequality across British businesses as every industry continues to pay men more on average than women. This video Guardian journalist Leah Green looks at the figures and busts some of the common myths surrounding the gender pay gap.

Data and the future of universities

August 2nd, 2018 by Graham Attwell

I’ve been doing quite a lot of thinking about how we use data in education. In the last few years two things have combined – the computing ability to collect and analyse large datasets, allied to the movement by many governments and administrative bodies towards open data.

Yet despite all the excitement and hype about the potential of using such data in education, it isn’t as easy as it sounds. I have written before about issues with Learning Analytics – in particular that is tends to be used for student management rather than for improving learning.

With others I have been working on how to use data in careers advice, guidance and counselling. I don’t envy young people today in trying to choose and  university or college course and career. Things got pretty tricky with the great recession of 2009. I think just before the banks collapsed we had been putting out data showing how banking was one of the fastest growing jobs in the UK. Add to the unstable economies and labour markets, the increasing impact of new technologies such as AI and robotics on future employment and it is very difficult for anyone to predict the jobs of the future. And the main impact may well be nots o much in new emerging occupations,or occupations disappearing but in the changing skills and knowledge required n different jobs.

One reaction to this from many governments including the UK has been to push the idea of employability. To make their point, they have tried to measure the outcomes of university education. But once more, just as student attainment is used as a proxy for learning in many learning analytics applications, pay is being used as a proxy for employability. Thus the Longitudinal Education Outcomes (LEO) survey, an experimental survey in the UK, users administrative data to measure the pay of graduates after 3, 5 and 0 years, per broad subject grouping per university. The trouble is that the survey does not record the places where graduates are working. And once thing we know for a certainty is that pay in most occupations in the UK is very different in different regions. The LEO survey present a wealth of data. But it is pretty hard to make any sense of it. A few things stand out. First is that UK labour markets look pretty chaotic. Secondly there are consistent gender disparities for graduates of the same subject group form individual universities. The third point is that prior attainment before entering university seems a pretty good predictor of future pay, post graduation. And we already know that prior attainment is closely related to social class.

A lot of this data is excellent for research purposes and it is great that it is being made available. But the collection and release of different data sets may also be ideologically determined in what we want potential students to be able to find out. In the same way by collecting particular data, this is designed to give a strong steer to the directions universities take in planning for the future. It may well be that a broader curriculum and more emphasis on process and learning would most benefits students. Yet the steer towards employability could be seen to encourage a narrower focus on the particular skills and knowledge employers say they want in the short term and inhibit the wider debates we should be having around learning and social inclusion.

 

Learning about Careers: Open data and Labour Market Intelligence

August 1st, 2018 by Graham Attwell

I’ve spent a lot of the last two months writing papers. I am not really sure why – other than people keep asking me to and I really do have a built up load of things which I haven’t written about. But one bad consequence of all this is I seem to have abandoned this blog. So,  time to start catching up here.

This paper – Learning about Careers: Open data and Labour Market Intelligence – is co-written with Deirdre Hughes. It is a preprint and wil be published in RIED – Revista Iboeroamericana de Educación a Distancia (The Iberoamerican Review of Digital Education) some time soon.

The full paper can be found on Research Gate or alternatively you can download it here. The abstract is as follows:

“Decisions about learning and work have to be placed in a particular spatial, labour market and socio-cultural context – individuals are taking decisions within particular ‘opportunity structures’ and their decisions and aspirations are further framed by their understanding of such structures. This article examines ways in which learning about careers using open data and labour market intelligence can be applied. An illustrative case study of the LMI for All project in the UK shows the technical feasibility of designing and developing such systems and a model for dissemination and impact. The movement towards Open Data and increasingly powerful applications for processing and querying data has gathered momentum. This combined with the need for labour market information for decision making in increasingly unstable labour markets have led to the development and piloting of new LMI systems, involving multiple user groups. Universal challenges exist given the increasing use of LMI, especially in job matching and the rapidly expanding use of open source data in differing education and employment settings. We highlight at least six emergent issues that have to be addressed so that open data and labour market intelligence can be applied effectively in differing contexts and settings. We conclude by reflecting on the urgent need to extend the body of research and to develop new methods of co-constructing in innovative collaborative partnerships.”

 

New Insights into UK society today from longitudinal research

December 8th, 2016 by Graham Attwell

Understanding Society has published its fifth annual report highlighting some of theinsights new topical policy-relevant research conducted recently using data from the annual survey which began in 2009 with around 100,000 individuals from 40,000 households.

To support the Insights 2016 launch, the team also published a topic guide on education. This guide explores the content available to analyse in Understanding Society, highlights the types of research questions which could be explored and what research has already been carried out.

Why Open Knowledge?

February 22nd, 2016 by Graham Attwell

I like this presentation on Why open knowledge from Martin Weller. And besides the argumentation he has some very pretty pictures.

A video tutorial: Getting started with the LMI for All API

November 11th, 2015 by Graham Attwell

Regular readers will know that together with Philipp Rustemeier, I have been working on  the UK Commission for Employment and Skills’ LMI for All project. Through the project we are developing a database providing access to open data around the Labour `market. This includes data about occupations, pay, present and projected employment, qualifications and much more. So far, UKCES has focused on the use of the data for careers guidance but I suspect it may have far wider potential uses, including for education and local government planning. When mashed with other data I see LMI for All as pointing to the future is of open data as part of smart cities or rather as providing data about cities for smart citizens.

The LMI for All project does not itself produce applications.Instead we provide access to a open APi, which developers can query to build their own desktop or mobile apps.

One thing we are working on is providing more help for developers wanting to use the API. As part of that we are developing a series of ‘how to’ videos, the first of which is featured above.The video was originally recorded in real time using Google Hangouts and  YouTube.  The 31 minute original was cut to about 15 minutes and a new introduction added.

Any advice about how to make this sort of video will be gratefully received. And the code which Philip developed live in the video can be accessed on GitHub

How Web 2.0 and Open APIs made it easy to create and share Open Educational Resources

October 6th, 2015 by Graham Attwell

Another post on Open Educational Resources. Last week I talked about the early days with the SIGOSEE project, seeking to build awareness of the possibilities of Open Educational Resources and Open Source in education and to start to change policy directions, especially at European Commission level.

In these early projects, we had three main lines of activity. The first was awareness about changing what Open educational Resources were and especially about Creative Commons Licenses. The second was talking with all manner of different stakeholders, including educational organisations and administration, developers and even the more enlightened publishers about the advantage of OERs and pushing for policy changes. But by far the most time consuming work was with practitioners, organising workshops to show them how they could produce Open Educational resources themselves.

And whilst primary school teachers were long used to developing their own learning materials, with the help of sticky back paper, glue, paint and the like, teachers in secondary schools and higher education were much more used to using bought in materials. True, the photocopier had replaced the Banda machines, and data projectors were well on the way to spelling redundancy for overhead projectors. But teachers had little or no experience in producing ICT based learning materials themselves.

With the value of hindsight is was the development of reasonably easy to use content creation applications and even more the advent of Web 2.0 which changed this situation. I can’t quite remember the different work flows we originally created but I think most involved using Open Office to make materials and then using various work arounds to somehow get them into the different VLEs in use at that time (I also seem to remember considerable debates about whether we should allow the use of proprietary software in our workflows).

Interestingly at that time we say standards and metadata as the key answer, especially to allow materials to be played in any Virtual Learning Environment. But it was Web 2.0 and Open APIs allowed not only easy content creation but provided easy means of distribution. Video was expensive and difficult even 10 or so years ago. Even if you had a powerful enough computer to edit and render raw video (I used to leave my computer running overnight to render 30 minutes videos) the issue was how to distribute it. Now with YouTube and a basic WordPress site anyone can make an distribute their own videos (and add a Creative Commons License). Ditto for photos, audio cartoons etc.

Over the last few years the emphasis has shifted from how to create and share Open Educational Resources to how to use them for teaching and learning. And whist there seems to be progress that issue is not yet overcome.

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.

 

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.”