Category Archives: careers

Technology and Careers Education

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I am ever more interested in how we can use technology for supporting young people (and not just young people) in making future careers choices. Talking to a focus group of young people last year, they  make use of the internet, particuarly using Google to search for details about possible future education choices, jobs and careers. I also asked them how far they explored the results of a Google search and not surpisingly they told me that they usually looked at the first three or four results. Try doing this yourself – type in your job of choice and see what comes up. All too often the results can be highly misleading.

Anyway we are working on new tools to asisst young people in choosing their future careers (more details to follow quite soon, I hope). And here is a conference paper, prepared by my colleague Sally Anne Barnes from the University of Warwick and describing the emprirical research we undertook on the use of technology in careers education in the UK (requires html5 compliant browser to view). Comments welcome – I would especially like to know what is happening in other countries than the UK.

The Potential Role of Technology in Careers Education in the UK

STEM and LEM – which pays the most?

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The de facto end of free education in the UK, and in particuar the imposition of coniderable fees for univeristy courses, which is likely to be extended to vocational programmes, is going to have widespread implications for peoples’ choices of careers.

Inthe next few weeks we will be exploring some of those implications on this blog.

One issue is that people are beginning to exmaine the ‘value’ of a degree in terms fo enhnaced lifetime earnings. And the rseulkts may not be as people have assumed. Governements and goevrnmental bodies have long espoused the importance of the so called STEM subjects – Science, Technology, Engineering and Maths – but figures suggest it may not be these subjects which enjoy the best pay premium.

According to the Guardian:

Male graduates in law, economics and management (LEM), for example, enjoyed faster growth in wages early in their career lifecycle compared to other majors, including Stem (science, technology, engineering and maths). Stem graduates, or those with combined degrees, eventually catch up with those who did LEM but not till much later in the lifecycle. For those opting for arts and other social science degrees, the lifetime returns are markedly lower – especially for men. The subject you study, then, makes a big difference to the investment returns, although, so far, only one institution has suggested subject specific pricing, so the costs are broadly the same across subjects. (Note that our research shows that early-career wage levels are not a good predictor of lifetime earnings – but, be warned, the government’s guidance for students on which subjects and institutions to choose will present data on early earnings.)

Among women, the picture is different. LEM graduates saw the highest and fastest rate of return. But women who did a degree – irrespective of which subject – enjoyed substantially higher lifetime earnings than those who didn’t. This can be read as an indication of the kind of discrimination that female non-graduates still face in the labour market. Moreover, the returns were broadly similar across subjects.

Technology Enhanced Boundary Objects and Visualising Data

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I have been spending a lot of time lately on visualising data as part of our efforts for build technology Enhanced Boundary Objects (TEBOs) to support careers professional in understanding and using Labour Market Information. The work is being undertaken as part of the EU funded Mature-IP and G8WAY projects.

In a short series of posts I will be reporting on my experiences with this work. But first more about those TEBOs.

Background to TEBOs

One particularly fruitful way of thinking about skills development at work is to look at the boundaries between different communities of employees within a workplace and the artefacts (documents, graphs, computer software) that are used to communicate between communities (Kent et al., 2007). Following the analysis of Bowker & Star (1999), “boundary objects” are “objects that both inhabit several communities of practice and satisfy the informational requirements of each of them”, thus making possible productive communication and “boundary crossing” of knowledge. In an earlier project on knowledge maturing and organisational performance (including in career guidance) we developed an approach to learning based on the design of symbolic boundary objects which were intended to act as a facilitator of communication across community boundaries, between teams and specialists or experts. Effective learning could follow from engagement in authentic activities that embedded models which were made more visible and manipulable through interactive software tools. In bringing the idea of boundary objects to the present research, we realised that a sub-set of general boundary objects could be ‘TEBOs’ (technology-enhanced boundary objects), resources within an OLME which were software based.

This approach makes use of the notions of boundary object and boundary crossing. The ideas of boundary crossing and tool mediation (Tuomi-Gröhn & Engeström, 2003; Kaptelinin & Miettinen 2005) and situated learning with a close alignment to the importance of a focus upon practice (Brown et al., 1989; Hall, 1996) informed considerations of the role of technologically-enhanced boundary objects in knowledge maturing processes in different contexts. One specific concern is to make visible the epistemological role of symbolic boundary objects in situations in which people from different communities use common artefacts in communication. A fruitful approach to choosing ways to develop particular boundary objects is to focus on what Onstenk (1997) defines as core problems: the problems and dilemmas that are central to the practice of an occupation that have significance both for individual and organisational performance — in this case the problems associated with providing advice relevant for career planning. One method this development project used was therefore to engage in a dialogue with careers guidance practitioners about common scenarios involving Labour Market Information (LMI) which could inform the development of prototype technologically-enhanced boundary objects (TEBOs). The development of the TEBO is therefore informed by a consideration of the following issues:

  • Importance of developing methods and strategies for co-design with users.
  • Need for conceptual tools to help people understand the models and ideas which are part of LMI.
  • Need for a more open pedagogy (than is typical of much existing technology-enhanced learning, and existing workplace training practice).
  • A system in which boundary objects are configurable by end-users. (practitioners) and by guidance trainers to be used in multiple ways
  • Need to build an understanding of how TEBOs may be used in ways that have utility for the employing organisation (in terms of efficiency savings), are empowering for practitioners, and ultimately for clients too.

These concerns could be coupled with another set of issues concerning appropriate skill development:

  • Need for time for people to interact, reflect, use concepts etc.
  • Trying to reach a stage where practitioners have justifiable confidence in the claims they make and can exercise judgement about the value of information when faced with unfamiliar LMI.
  • Choosing between a range of possible use-contexts.
  • Deciding how to employ support from communication and discussion tools.
  • Developing and transmitting Labour Market intelligence – importance of communicating to others.
  • Preconfiguring certain ways of thinking through use of scenarios; discussions can point into and lead from scenarios.

The above sets of issues provided a clear steer to the type of investigations that would be needed to investigate how TEBOs might be used to support the learning and development of careers guidance practitioners. There are also broader questions about the overall design of the learning system and how users might interact with the system in practice.

Communities of Practice

The importance of Labour Market Information (LMI) in Careers Advice, Information and Guidance has been recognized by the EU in its New Skills, New Jobs strategy. LMI is crucial for effective career decision-making because it can help young people in planning future careers or those planning a change in career in selecting training new careers pathways. LMI is also critical for professionals in supporting other stakeholders in education (like careers coordinators in schools) and training planners and providers in determining future skills training provision. LMI is collected by a variety of different organizations and agencies in Europe including government and regional statistical agencies, industry sector bodies and private organisations. Each collects data for different purposes. Some of these data are made available in a standardized form through Eurostat. However access is uneven. Furthermore the format of the data is seldom usable for careers guidance, and there are few tools to enable its use by advisors or job seekers. This is especially an issue at a time of financial pressures on training courses when potential participants will wish to know of the potential benefits of investing in training. It is also often difficult to access potential training opportunities with the lack of data linking potential careers to training places.

The use of LMI, therefore, lays at the boundaries between a number of communities (and emerging communities of practice).

The practice of careers professionals is related to the provision of careers guidance to clients, such as young people, those returning to the labour market, unemployed people and those seeking a change in careers, amongst others.

LMI is predominantly collected by statisticians working for governmental or non-governmental organisations and agencies. Their practice relates to the collection, compiling, curating and interpretation of data. Data are not collected primarily for providing careers guidance, but for economic and social forecasting and policy advice.

The forms of artefacts used in these different practices vary considerably, with data being released in data tables, which make little sense without (re)interpretation and visualisation. Visualisation is an emergent specialist practice itself requiring cross disciplinary knowledge and a new skills base. Furthermore the use of data in careers practice may require the use of statistical and visualisation tools, however basic, which are generally outside the skills and practice of careers professionals.

In the next post in this series I will look at the identification of the core problems as the basis for the pilot TEBO.

References

Ainsworth, S. & Th Loizou, A. (2003) The Effects of Self-explaining When Learning with Text or Diagrams, Cognitive Science, 27 (4), pp. 669-681.

Bowker, G. C., & Star, S. L. (1999). Sorting things out. Classification and its consequences. Cambridge, MA: MIT Press.

Brown, J. S., Collins, A., & Duguid, P. (1989) Situated cognition and the culture of learning, Educational Researcher, 18 (1), pp. 32-41.

Chandler P. (2004) The crucial role of cognitive processes in the design of dynamic visualizations, Learning and Instruction 14 (3), pp. 353-357.

Hall, R. (1996) Representation as shared activity: Situated cognition and Dewey’s cartography of experience, Journal of the Learning Sciences, 5 (3), 209-238.

Hegarty, M. (2004) Dynamic visualizations and learning: getting to the difficult questions, Learning and Instruction 14 (3), pp 343-351.

Kaptelinin, V., & Miettinen, R. (Eds.) (2005). Perspectives on the object of activity. [Special issue]. Mind, Culture, and Activity, 12 (1).

Kent, P., Noss, R., Guile, D., Hoyles, C., & Bakker, A (2007). “Characterising the use of mathematical knowledge in boundary crossing situations at work”. Mind, Culture, and Activity 14, 1-2, 64-82.

Lowe, R.K. (2003) Animation and Learning: selective processing of information in dynamic graphics, Learning and Instruction, 13 (2), pp. 157-176.

Lowe, R. (2004) Changing status: Re-conceptualising text as an aid to graphic comprehension. Paper presented at the European Association for Research on Learning and Instruction (EARLI) SIG2 meeting, ‘Comprehension of Text and Graphics: basic and applied issues’, Valencia, September 9-11.

Narayanan, N. H. & Hegarty, M. (2002) Multimedia design for communication of dynamic information. International Journal of Human-Computer Studies, 57 (4), pp. 279-315.

Onstenk, J. (1997) Core problems, information and communication technologies and innovation in vocational education and training. Amsterdam: SCO Kohnstamn Institut.

Ploetzner R. and Lowe R. (2004) Dynamic Visualisations and Learning, Learning and Instruction 14 (3), pp. 235-240.

Tuomi-Gröhn, T., & Engeström, Y. (2003) Conceptualizing transfer: From standard notions to developmental perspectives. In T. Tuomi-Gröhn & Y. Engeström (Eds.), Between school and work: New perspectives on transfer and boundary-crossing. Amsterdam: Pergamon, pp. 19-38.

van Someren, M., Reimann, P., Boshuizen, H.P.A., & de Jong, T. (1998) Introduction, in M. van Someren, H.P.A. Boshuizen, T. de Jong & P. Reimann (Eds) Learning with Multiple Representations, Kidlington: Pergamon, pp. 1-5.

Story telling with Data

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Today Google Labs released their new data visualisation store. Very impressive it is too, although it is not a straightforward task to register on the site, upload uses an XML format and you cannot download data. But the visualisation is pretty good and Google themselves have linked to a number of large Eurostat data sets.

I have been working on data for the last couple of weeks. I am trying to build a TEBO – a Technology Enhanced Boundary Object (or objects) for explaining Labour Market data to Careers Advice, Information and Guidance (CAIG). Together with my colleagues from the Institute for Employment Research at Warwick University, I have been looking at TEBOs for some time.

Alan Brown explains the conceptual idea behind TEBOs:

The ideas of boundary crossing and tool mediation (Tuomi-Gröhn & Engeström, 2003; Kaptelinin & Miettinen 2005) and situated learning with a close alignment to the importance of a focus upon practice (Brown et al., 1989; Hall, 1996) informed considerations of the role of technologically-enhanced boundary objects in knowledge maturing processes in different contexts. One specific concern is to make visible the epistemological role of symbolic boundary objects in situations in which people from different communities use common artefacts in communication. A fruitful approach to choosing ways to develop particular boundary objects is to focus on what Onstenk (1997) defines as core problems: the problems and dilemmas that are central to the practice of an occupation that have significance both for individual and organisational performance — in this case the problems associated with providing advice relevant for career planning. One method this development project used was therefore to engage in a dialogue with guidance practitioners about common scenarios involving Labour Market Information (LMI) which could inform the development of prototype technologically-enhanced boundary objects (TEBOs). The development … was therefore informed by a consideration of the following issues:

  • Importance of developing methods and strategies for co-design with users
  • Need for conceptual tools to help people understand the models and ideas which are part of LMI
  • Need for a more open pedagogy (than is typical of much existing technology-enhanced learning, and existing workplace training practice)
  • A system in which boundary objects are configurable by end-users (practitioners) and by guidance trainers to be used in multiple ways
  • Need to build an understanding of how TEBOs may be used in ways that are empowering for practitioners, and ultimately for clients too.

These concerns could be coupled with another set of issues concerning appropriate skill development:

  • Need for time for people to interact, reflect, use concepts etc.
  • Trying to reach a stage where practitioners have justifiable confidence in the claims they make and can exercise judgement about the value of information when faced with unfamiliar LMI
  • Choosing between a range of possible use-contexts
  • Decide how to employ support from communication and discussion tools
  • Developing and transmitting Labour Market intelligence – importance of communicating to others
  • Preconfigure certain ways of thinking through use of scenarios; discussions can point into and lead from scenarios.

In practice it is not so easy to develop such TEBOs. Identifying key problmes is probably the most useful approach. But then there is an issue in accessing different data to visualise as part of the process. A great deal of data is now publicly available. But I am no data specialist and have faced a steep learning curve in understanding and interpreting the data myself. then there is the issue of visualisation – I am mainly using Google Gadgets, although we are also working with Tableau (a powerful tool, but unfortunately only available for Windows) and IBM;s Many Eyes. All these tools are good, but are all extremely finicky about how the data is formatted. We are working with data in xls and Apple’s Numbers but I suspect longer term it would be better to use the Open Source R programming environment.

And the hardest task of all is the storyboarding. At the end of the day we are trying to tell stories with data: TEBOs are a storytelling and exploration approach to learning. So for each TEBO I intend to make a short video explaining the key concepts and showing the various visualizations. We will also provide access to the raw data and to static versions of the graphing, along with explanatory notes. And for each TEBO we will try to construct an interactive visualisation tool, allowing learners to play with the data and displays. I also want to try to build some sort of simulations using the Forio tool. No doubt there is better software (and if anyone has any ideas I would be very grateful). But I sort of feel that the more social software, open source or free tools we can use the better. We want to encourage people to do it for themselves. And they have no money to spend on fancy software tools.We cannot possibly provide access to visualisations of all the data available. But if we cane explain what is possible, hopefully interested CAIG professionals will start there own work. And then who knows – a Careers Guidance data store?

Making Sense of Statistics

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At the moment we are doing a  lot of work with careers guidance professionals. And part of this work is around the use of Labour Market Information in the guidance process. What jobs are available? What are future trends in employment? How much can a person expect to earn in any occupation? What qualifications are needed?

Much of this information relies of statistics. There are a lot of statistics provided by governmental and other agencies. In the UK the data.gov.uk web site is providing increasing access to data and encouraging visualisations, mash-ups and reuse.

But statistics require interpretation. By chance this morning I stumbled on a tweet -

DrEvanHarris Exposing dodgy various claims on average start grad pay http://bit.ly/gmjT5x by @Straight_Stats – reported uncritically – even by FT

Following that link took me to the excellent Straight Statistics website. The home page says:

We are a campaign established by journalists and statisticians to improve the understanding and use of statistics by government, politicians, companies, advertisers and the mass media. By exposing bad practice and rewarding good, we aim to restore public confidence in statistics.

The tweet from DrEvanHarris led to an article by Nigel Hawkes entitled Questionable Claims on Government Pay.The recent campaign against rises in student fees in the UK has focused attention on how much graduate earns. And the article suggests that many of the figures quoted in UK newspapers may give an inflated impression of graduate starting salaries because of the way these figures are compiled.

The Straight Statistics website provides a number of excellent and free resources including a simple guide to numerical and statistical traps, Making Sense of Statistics. a simple guide to numerical and statistical traps, Although the guide is “ primarily designed for journalists and press officers”,  the web site says it may be interesting to others as well. And indeed it is, providing clear examples of how statistics can mislead.

One of the issues we have looked at in the Labour Market Information for careers guidance is the impact of gender on pay. Nigel Hawkes explains this depends on how the figures are collected:

How big is the gap between the earnings of men and women? According to the Office for National Statistics (ONS), it is 12.8%. But the Government Equalities Office (GEO) says it is 23%. And the Equality and Human Rights Commission (EHRC) says it’s 17.1%1.

The differences in these figures arise from the different methods used to produce them: the ONS includes only full-time employees, excluding overtime and part-time workers. The GEO includes part-time workers because it says more women than men work part-time and it is wrong to exclude them. The EHRC figure uses the ONS data but compares the mean salaries not the median. It justifies this by saying than men are over-represented at one extreme of the earnings range, and women at the other.

Three figures – all of them right – but asking what is being compared and how it was calculated tells us why there is a difference.

Well worth a read!