Technology Enhanced Boundary Objects and Visualising Data
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
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Brown, J. S., Collins, A., & Duguid, P. (1989) Situated cognition and the culture of learning, Educational Researcher, 18 (1), pp. 32-41.
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Hall, R. (1996) Representation as shared activity: Situated cognition and Dewey’s cartography of experience, Journal of the Learning Sciences, 5 (3), 209-238.
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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.
Technology Enhanced Boundary Objects and Visualising Data http://is.gd/ophDFz