INTEGRATING KNOWLEDGE IN

INTERDISCIPLINARY

ENVIRONMENTAL AND

SUSTAINABILITY TEAMS

Dr. Deana Pennington

Assoc. Professor of Geological Sciences

University of Texas at El Paso

Challenges of Interdisciplinary Research

Roy et al. (2013) BioScience

Figure 3 from: Roy ED, Morzillo AT, Seijo F, et al (2013) The

Elusive Pursuit of Interdisciplinarity at the Human–Environment

Interface. BioScience 63:745–753.

Nationwide survey

Natural & social scientists

76-questions

323 responses

Learning and Collaboration

NSF grants: 2006, 2008, 2011 Pennington 2008, 2010, 2011a, 2011b, 2013

SESYNC working group 2013 to present EMBeRS: Employing Model-Based Reasoning in

Socio-Environmental Science JESS Special Issue December 2015

Heterogeneity Problem

7/1/2015

Social Natural

Engineer

Conceptual

Distance

Conceptual

Distance

Collective action

Synthesized

Conceptual

Framework

Why learning?

Learning = The acquisition of knowledge or skills through

experience, study, or by being taught

The integration process:

• I know what I know

• I need to connect what I know with what you know

• I don’t know what you know

• I need to LEARN something about what you know,

so I can connect it with what I know

Experiential learning: individuals

A learning theory put forth by Kolb (1984) that explains the

process of learning from our experiences, including

experiences in teams

Transforming

(create new content)

Experiential Learning across

disciplines

Grasping

(acquire & connect new content)

Double loop learning: Groups

A learning theory put forth by Argyris (1977) that explains the

process of learning from group experiences through iterative

testing and refining of goals

a) Double-loop learning

Governing

variable Consequences

Action

strategy

Vision & Goals

Evaluation

(Single-loop learning)

Linking learning theories

a) Double-loop learning

Reflection Observation

Action

Transforming

(create new content)

b) Experiential Learning

across disciplines

Abstraction

Governing

variable Consequences

Action

strategy

Vision & Goals

Evaluation

(Single-loop learning)

c) Co-created, shared

visions

Triple-loop learning Grasping

(acquire & connect new content)

A model put forth by

Pennington (2010) to

leverage learning

theory in order to co-

create shared research

visions

Improve group process Iterate

Thought experiments

Improve grasping

process Visuals

Pennington’s teamwork model

Forthcoming in JESS Special Issue (December)

Boundary Negotiating Object:

Specific kind of visual that enables crossing boundaries between disciplines

and negotiating the boundaries of an integrated conceptual framework

Results

Enabled better collaboration between

individuals

Improved collaboration between disciplines

Improved my own ability to collaborate

Useful for constructing a shared vision

that included most people

Useful for constructing a cross-disciplinary

conceptual framework

I would attend this workshop again

I would recommend the workshop to others

Agree

Agree somewhat

Neutral

Disagree somewhat

Disagree

N/A

Pennington 2011

Model-Based Reasoning

A cognitive science theory put forth by Nersessian (1999)

explaining how modeling practices, and reasoning with

models, lead to conceptual change in science

Models: Analogies, metaphor, thought experiments, visual models,

and/or simulation models… used for abstraction and communication of

complex concepts

Model-based reasoning:

• Employing models to invoke conceptual change [e.g. learn]

• Reasoning by mental modeling possibly aided by external devices

(Nersessian 1999)

Models enable the offloading and summarizing of complex

information so that individuals can grasp and manipulate more

information [e.g. learn]

(Giere 2002)

Productive team practices

Guidance for leaders

GROUP PROCESSING MODEL

STAGES IN SUSTAINABILITY GROUP WORK

FORMULATION FORMALIZATION INTERROGATION

REASONING WITH VISUAL MODELS

Results forthcoming JESS Special Issue (December)

Conclusion

• Applications of learning theory to the challenges

knowledge integration & synthesis in sustainability

science

• Source of creative thinking about how to better lead

interdisciplinary teams

• Understanding why certain practices work

• Developing heuristics and models for leaders

Acknowledgements

This material is based upon work supported by the National

Science Foundation under Grant Nos. OCI-1135525, #OCI-

0753336, and #OCI-0636317.

Any opinions, findings, and conclusions or

recommendations expressed in this material are those of

the author(s) and do not necessarily reflect the views of the

National Science Foundation or SESYNC

References

7/1/2015

Argyris C (1977) Double loop learning in organizations. Harvard Business Review 55:115–125.

Bailey, K. 2001. Towards unifying science: Applying concepts across disciplinary boundaries. Systems Research and Behavioral Science

18:41-62.

Benda, L., N. L. Poff, C. Tague, M. A. Palmer, J. Pizzuto, S. Cooper, and E. Stanley. 2002. How to avoid train wrecks when using science in

environmental problem solving. BioScience 52(12):1127-1136

Campbell, L. M. 2005. Overcoming obstacles to interdisciplinary research. Conservation Biology 19(2):574-577.

Cottingham, K. 2002. Tackling biocomplexity: The role of people, tools, and scale. BioScience 52(9): 793-799.

Daily, G. C. and P. R. Ehrlich. 1999. Managing earth’s ecosystems: an interdisciplinary challenge, Ecosystems 2:277-280.

Giere, R. (2002). Models as parts of distributed cognitive systems. In Model-Based Reasoning (pp. 227–241). New York: Kl.

Golde, C. and H. Gallagher. 1999. The challenges of conducting interdisciplinary research in traditional doctoral programs. Ecosystems 2:281-

285.

Kolb DA (1984) Experiential Learning. Prentice Hall, Englewood Cliffs, NJ

Lele, S. R. and R. B. Norgaard. 2005. Practicing interdisciplinarity. BioScience 55(11):967-975.

Likens, G. 1998. Limitations to intellectual progress in ecosystem science. In: Successes, Limitations and Frontiers in Ecosystem Science. M.

Pace and P. Groffman. New York, Springer-Verlag, pp. 247-271.

Mezirow J. 1978. Perspective Transformation. Adult Education 28(2): 100–110.

Nersessian, N. J. (1999). Model-Based Reasoning in Conceptual Change. In L. Magnani, N. J. Nersessian, & P. Thagard (Eds.), Model-Based

Reasoning in Scientific Discovery (pp. 5–22). Springer US.

Pennington D (2011a) Collaborative, cross-disciplinary learning and co-emergent innovation in informatics teams. International Journal of

Earth System Informatics 4:55–68.

Pennington D (2011b) 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 20:165–196.

Pennington D (2010) The dynamics of material artifacts in collaborative research teams. Computer Supported Cooperative Work 19:175–199.

Pennington DD (2008) Cross-disciplinary collaboration and learning. Ecology and Society 13:8.

Pennington D, Simpson G, McConnell M, et al (2013) Transdisciplinary science, transformative learning, and transformative science.

BioScience 63:564–573.

Roy ED, Morzillo AT, Seijo F, et al (2013) The Elusive Pursuit of Interdisciplinarity at the Human–Environment Interface. BioScience 63:745–

753. doi: 10.1525/bio.2013.63.9.10

Questions?

Teamwork model

STATE PROCESS

Perspectives 1…n

1

+ Integrated Idea

Generation Capacity 2

Group

Information

Sharing

6

+/- Motivation

If < threshold

Exit collaboration

10 + Collaboration skills

+ Social ties

+ 11

LEGEND:

Individual

Experiential

Learning

3

5 + Shared mental models

+ Transactive memory

- Knowledge heterogeneity 7 If > threshold

Emergence

+ Shared

Vision Collaborative

Action

+ Collaboration

Capacity

9 8

Boundary

Negotiating

Objects

4