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