Laboratory Learning: Model-Based Reasoning in Biomedical Engineering Research and Instructional Laboratories

Principal Investigator: 
Co-Investigator: 
Project Overview
Background & Purpose: 

The purpose of the study has been to investigate cognitive and learning practices in interdisciplinary engineering research laboratories and to translate findings about the ecological features that support successful learning there into instructional settings. In particular, we have been creating and evaluating models of complex, problem-driven classrooms and instructional laboratory settings that promote the cognitive practice of model-based reasoning.

Setting: 

A Department of Biomedical Engineering.

Research Design: 

The research design for this project is longitudinal and comparative, and is designed to generate evidence that is descriptive and interpretive (case study, design research, ethnography, and observation). This project collects original data using unstructured interviews, diaries/journals/records kept by study subjects, assessments of learning/achievement tests, and personal observation.

We developed the Sense-Making Sorter to distinguish degree and quality of framing, observation, analysis, and synthesis in relation to three different phases of a research task (completion of an instructional laboratory notebook exercise). Although developed to distinguish forms of reasoning in biomedical engineering lab notebooks, the instrument could have applicability beyond engineering. The instrument was designed to organize qualitative observations but it yields a quantitative score (0-10 points) allowing for comparison between different classroom formats.

I. We ran two iterations of pre-post test of understanding of the nature of models which we analyzed using statistical methods. We evaluated a final exercise aimed at detecting the ability to spontaneously engage in model-based problem solving through development and application of a coding scheme following a phase of open reading and discussion. The coding scheme targeted reasoning structure and specifically evidence of model-based reasoning, coded as a Model before making Hypothesis.

II. Methods for evaluating laboratory notebooks for BME 3160 consisted of: 1) open reading by an interdisciplinary team to characterize differences between notebooks; 2) extraction of emergent themes; 3) organization of themes into the Sense-Making Sorter (described above); 4) refinement of instrument by application of to a subset of notebooks by three independent raters, comparison of ratings; 5) application to remaining notebooks to obtain comparable (quantitative) scores; 6) analysis of differences between PBL and instructional classroom as indicated by the Sense-Making Sorter and as averaged across three raters.

We have created an on-line data base and will make all materials it was possible to sanitize available upon request.

Findings: 

Our findings are numerous. Here we focus on those most relevant to learning in the research and instructional labs. At the outset we framed the labs as distributed cognitive-cultural systems, and so one objective was to determine the way cognition and culture are mutually implicated in their research practices. Several highly salient categories emerged in coding for cognitive practices, three empirically and theoretically robust notions, in particular, influenced our designs of learning environments:

  • Model-based cognition
  • Cognitive partnering
  • Interlocking models

In each laboratory, the research is driven by the need to formulate and solve complex, cross-domain problems. Because it would be either impossible or unethical to experiment on animals or humans, each laboratory needs to design and build physical in vitro simulation models to investigate in vivo phenomena. So, e.g., the tissue engineering laboratory designs and builds simulation devices such as models of vascular tissue or models that replicate the force of blood flowing through arteries. One researcher referred to this practice of constructing model-based simulations as, “putting a thought into the bench top to see if it works,” which we considered a particularly apt intuitive description of their cognitive practices.

These models are hybrid entities, reflecting the labs as engineering and biological environments, and reflected in the characteristics of the researcher-learners who are part of an educational program aimed explicitly at producing interdisciplinary, integrative thinkers. By “model-based cognition” we mean that researchers understand, explain, and reason by means of structured representations of phenomena, devices, and methods, both mental and physical models. During the course of learning to become a researcher and designing and conducting one’s research, researchers form relationships with other researchers and with certain artifacts essential to their research; we categorize forming these relationships as “cognitive partnering.” Forming relations with others requires developing a healthy mix of independence and interdependence, fostered by lab mentoring practices. Forming relationships with artifacts – simulation devices – is particularly noteworthy. As the researcher matures, the simulation device is conceived as a partner in research. In one sense, it marks coming to understand the research through the lens of what the device affords and constrains, but goes beyond this to an understanding of the devices as possessing quasi-independence – as distinct from the “thought” the researcher put “into the bench top.” This transition is marked by using increasingly anthropomorphic language that attributes agency to the artifact, such as “the cells once they are in the matrix will reorganize it and secrete a new matrix and kind of remodel the matrix into what they think is most appropriate” (construct device, Lab A) or “yeah, seven parameters it has to look at in order to decide what’s a burst” (MEA dish model, Lab D). Finally, “interlocking models” provides a way to categorize integrative interdisciplinary thinking at the individual level, and practices at the system level. Again, linguistic markers provided evidence for conceptual integration, for instance, “it was necessary to shear precondition these derived cells at an arterial shear rate.” “An arterial shear rate” marks an integrated biological and engineering conception of an artery, while the entire sentence expresses an integration of biological and engineering materials and methods.

With the goal of translation to instructional settings in mind, we distilled our findings about successful learning in the BME research labs into 5 Principles of Agentive Learning Environments:

  • Learning is driven by the need to solve complex problems
  • Learning is relational (“cognitive partnering”)
  • Organizational structure is largely non-hierarchical
  • Building serves as entrée
  • Multiple support systems foster resilience in the face of impasses and failures

Based on these and the cognitive practices of BME, our instructional design strategies have been:

  • Construct complex, open-ended cross-domain problems for exploration
  • Cultivate learner’s ability to evoke/authorize people, resources, and technological artifacts as mediators and agents in their research projects.
  • Foster largely non-hierarchical structure, in particular, provide opportunities to tap into the distributed nature of group conceptual, technical, methodological knowledge
  • Create interactional situations that foster rapid participation that use “building” and support build-up of requisite knowledge
  • Create multiple support systems to foster resiliency when failing – faculty-student, TA-undergrad, student-student – to facilitate the development of community
  • Focus interventions towards creating interlocking models to support model-based reasoning and problem solving

As is the practice in design-based research, we have taken our courses and our design strategies through several iterations in developing our classrooms and instructional labs. We have found that when recast for the engineering context, PBL can be used as a tool to implement these strategies with appropriate modifications to support the BME cognitive practice of model-based reasoning (as opposed to the medical practice of hypothetico-deductive reasoning).

Publications & Presentations: 

2009
Chandrasekharan, S. (2009). Building to discover: A common coding model, Cognitive Science, in press.

Nersessian, N.J. (2009). How do engineering scientists think? Model-based simulation in biomedical engineering research laboratories, TopiCS, in press.

Nersessian N.J. & Chandrasekharan, S., (2009). Hybrid analogies in conceptual innovation in science, Journal of Cognitive Systems Research, 10:178-188.

Nersessian, N.J. & Patton, C., (2009). Model-based reasoning in interdisciplinary engineering. In A. Meijers, A. (Ed.): Handbook of the Philosophy of Technology and Engineering Sciences.: Amsterdam: Elsevier, in press.

Osbeck, L. (2009). The critical place of personalism: Comments on Stern and the special issue. New Ideas in Psychology, in press.

Osbeck, L. (2009). Transformations in cognitive science: Implications and issues posed. Journal of Theoretical and  Philosophical Psychology, in press.

Chandrasekharan, S. & Osbeck, L. (2009). Rethinking Situatedness: Environmental structure in the time of common code. Theory and Psychology, in press.

Osbeck, L., Nersessian, N. J., Malone, K., Newstetter, W. (Contracted). Science as Psychology: Sense-making and Social Identity in Science Practice, Cambridge University Press, projected 2009 completion.

2008
Nersessian, N.J. (2008). Creating Scientific Concepts. Cambridge, MA: MIT Press.

Nersessian, N.J., (2008). How Science works: Model-based reasoning in scientific practice. In Teaching Scientific Inquiry: Recommendations for Research and Implementation: R. A. Duschl, R. E. Grandy (Eds.). Rotterdam, NL: Sense Publishers, pp. 57-79).

Nersessian, N.J., (2008). Mental modeling in conceptual change. In S. Vosniadou (Ed.), International Handbook of Conceptual Change. London: Routledge, pp. 391-416).

Chandrasekharan, S. & Nersessian, N.J. (2008). Counterfactuals in science and engineering, Behavioral and Brain Sciences, 30:454-455, 2008.

Harmon, E. & Nersessian, N.J., (2008). Cognitive partnerships on the benchtop: designing to support scientific researchers. Refereed Conference Proceedings of the 7th ACM Conference on : Designing Interactive Systems - DIS 2008 (New York: ACM, Inc., pp. 119-128).

Malone, K.R. and Barabino, G. (2008). Narrations of race in STEM research settings: Identity Formation and its Discontents, Science Education, in press, available on-line at http://www3.interscience.wiley.com/journal/108069556/issue.

Newstetter, W., Johri, A, & Wulf, Volker (2008) Laboratory Learning: Industry and University Research and Innovation as Site for Situated and Distributed Cognition. Proceedings of ICLS Conference 08, AACE.

2007
Newstetter, W. & Nersessian N.J.: Crossing the science/engineering divide: Design principles for interdisciplinary learning environments, Refereed Conference Proceedings of the first International Conference on Research in Engineering Education, 2007.

Osbeck, L., Malone, K., & Nersessian, N., (2007). Dissenters in the Sanctuary: Evolving frameworks in "mainstream" cognitive science, Theory and Psychology, p. 243, vol. 17, (2007).

Tissaw, M. & Osbeck, L., (2007). Reflections on critical engagement with the mainstream, Theory and Psychology, p. 155-168, vol. 17.

2006
Nersessian N.J., (2006). Model-based reasoning in distributed cognitive systems, Philosophy of Science, 73:699-709, vol. 72.

Nersessian, N.J., (2006). The cognitive-cultural systems of the research laboratory, Organization Studies, 27:125-145,vol. 27.

Newstetter, Wendy C., Fostering Integrative Problem Solving in Biomedical Engineering: The PBL Approach, Annuals of Biomedical Engineering, 2:217-225, vol.34.

Osbeck, L. & Nersessian, N.J., The distribution of representation, Journal for the Theory of Social Behavior, p. 141-160, vol. 36, (2006).

Kurz-Milcke, E., Nersessian, N.J. & Newstetter, W., (2004). What has history to do with cognition? Interactive methods for studying research laboratories, Cognition and Culture, p. 663, vol. 4, (2004).

Malone, K., Newstetter, W. Barabino, G., (2006). Valuing diversity as it happens: Exploring laboratory interactions when more is going on than science. Refereed Conference Proceedings [CD-Rom] San Diego, CA.ASEE/IEEE Frontiers in Education.

Newstetter, W. (2006) Laboratory Learning: Cognitive and Learning Practices in University Research Laboratories. Refereed conference Proceedings of the Cognitive Science Society. Vancouver Canada.
Osbeck, L., Newstetter, W., & Nersessian, N.J., (2006). Positioning in the laboratory. Refereed Conference proceedings, Zacatecas, Mexico: International Society for Psychology of Science.

Malone, K. & Kelly, S., (2006). Women in Science: Should we bother with a Psychoanalytic Viewpoint?. Psychoanalysis, Culture & Society, p. 207, vol. 10, (2006).

2005
Malone, K., Nersessian, N.J., Newstetter, N., Gender Writ Small: Gendered Enactments and Gendered Narratives about Lab Organization and Knowledge Transmission in a Bio-Medical Engineering Lab Research Setting, Journal of Women & Minorities in Science & Engineering, p. 61, vol. 11, (2005).

Newstetter, W. (2005), Designing cognitive apprenticeships for biomedical engineering, Journal of Engineering Education, p. 207, vol. 94.

Nersessian, N.J., (2005). Interpreting scientific and engineering practices: Integrating the cognitive, social, and cultural dimensions, M. Gorman, et al. (Eds.), Scientific and Technological Thinking, Hillsdale, NJ: Lawrence Erlbaum pp.17-56.

Sun, Y., Newstetter, W., Nersessian, N.J., (2006). Promoting model-based reasoning in problem-based learning. Refereed Conference Proceediings, N. Myiake, R. Sun (Eds.), Proceedings of the Cognitive Science Society 28.

Nersessian, N.J., Kurz-Milcke, E., Davies, J., (2005). Ubiquitous computing in science and engineering research laboratories: A case study from biomedical engineering. In G. Kouzelis, et al. (Eds.): In-Use Knowledge. Berlin: Peter Lang. Greek Translation in Topika 10:203-237, 2005.

Newstetter, W., (2005). Problem-based learning in biomedical engineering.. Conference proceedings, Jack Linehan (Ed.), Whitaker Biomedical Engineering Summit II.

Other Products: 

1) The Sense Making Sorter discussed above, which we believe will have wider applicability. 2) Models of course and instructional laboratory development in the context of interdisciplinary engineering.

We also have two websites under construction (see below). One will provide information and support for developing problem-driven learning classrooms; the second contains our research papers.