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Collaborative, Cross-disciplinary Learning

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Spring 2011

Karen Baker (PAL, CCE)

Review: D. Pennington (2011 online preprint). Collaborative, cross-disciplinary learning and co-emergent innovation in Science teams. International Journal of Earth Science Informatics. URL:

The contemporary realm of eScience has teams of scientists and technologists working together to create community infrastructure in the form of grids and networks. What enables these teams to collaborate effectively? As made clear in the title of this paper, learning is key. Learning is involved in the establishment of a common ground important for generation of innovative interdisciplinary ideas and for development of shared vision.

Before summarizing, I’ll point out that the author, Deanna Pennington, is a member of the LTER community since 1998.  Deana conducted her PhD dissertation at Andrews LTER and upon graduation took a postdoctoral position at the LTER Network Office working with the San Diego Supercomputer Center on emerging informatics solutions for ecology. This led to a research faculty appointment, from which Deana participated over the next years in geospatial and remote sensing analysis at Sevilleta LTER; the NSF-funded SEEK informatics project; and in leading two NSF-funded Cyber-Infrastructure (CI) Team projects. She started a new position last year as research scientist in the CyberShARE Center at University of Texas El Paso that allows her to follow her interests in scientific collaboration.

Pennington’s analysis introduces a key perspective - the ‘learning perspective’ – as we strive to work in a manner sensitive to and inclusive of the individual and the larger cognitive system, of training and learning, and of disciplinary and multi-disciplinary work. With the goal of generating shared conceptualizations and ways to develop unified conceptual frameworks informed by semantic issues, the importance of developing not only hypothesized infrastructure solutions but also activities that facilitate group learning and interdisciplinary problem formulation is highlighted.  Several models for rapid, co-constructed idea generation are presented that are informed by learning theory.

The conceptual work relating to collaborative learning processes is anchored by observations made in her CI Team project where collaborative team activities were explored. Pennington worked with a variety of multi-disciplinary teams while considering the following: "How does one rapidly develop an understanding between those interested in building tools and those interested in using the tools so that their respective efforts can occur simultaneously rather than being lagged in time?" A very useful table is presented that provides us with new vocabulary and sets of categories for expressing elements of collaborative work. The table makes explicit links between models of collaboration, cross-disciplinary learning and models of technology adoption. More concrete examples from the practices observed would have helped situate the discussion. Fortunately, there are more examples and insights in some of Deana’s other recent work (Pennington 2010, 2011).


Pennington, D., (2011), Bridging the Disciplinary Divide: Co-Creating Research Ideas in eScience Teams. Computer Supported Cooperative Work Special Issue on Embedding eResearch Applications: Project Management and Usability.  Online preprint; URL:

Pennington, D., (2010), The dynamics of material artifacts in collaborative research teams. Computer Supported Cooperative Work 19(2):175-199.  DOI: 10.1007/s10606-010-9108-9.  [online] URL: