Program

A theory of epistemic design theory includes both (a) a normative model of epistemic competence and (b) an instructional theory of how to promote this competence through effective design.  In this talk, I will present an overview of a theory of epistemic design that I have been developing in collaboration with my colleagues (including Ravit Golan Duncan, Sarit Barzilai, Ron Rinehart, Ala Samarapungavan, Luke Buckland, William Pluta, and Gale Sinatra).  Much of this research has been situated in the context of middle-school life science classes focused on model-based inquiry. I will discuss implications of our work in middle-school classes for a theory of epistemic design, including potential extensions to STEM education at the college level.

I will begin by discussing the goals of epistemic design, and then turn to the issue of what design features can achieve these goals. The theoretical framework that guides our theory of epistemic design theory is the AIR model of epistemic cognition, which views epistemic cognition as comprising Aims, Ideals (or standards), and Reliable Processes for achieving epistemic aims. I will discuss the AIR framework and explain how we have used it in epistemic design, using data from our research project to support critical design recommendations. 

A well-structured explanation of cellular processes coordinates reasoning about the mechanisms with visual, symbolic, and data representations. Since students have problems both understanding biomolecular mechanisms depicted by representations and how to competently explain them, we decided to simultaneously address both types of difficulties through a hybrid model composing a combination of the key elements of two different established expert models, the Methods-Analogies-Context-How (MACH) and Concept-Reasoning-Mode of representation (CRM) models. The MACH model represents the key components of biology expert explanations of mechanisms, while the CRM model depicts the factors affecting a person’s ability to interpret representations. In this presentation, in collaboration with Nancy Pelaez and Kamali Sripathi, I will demonstrate how this hybrid model can be used as a guiding framework for the design of instruction about the mechanism of oxidative phosphorylation in a biochemistry class. I will also demonstrate with qualitative data how the hybrid model can be used to identify and code for various elements of instructor explanations and ways of reasoning. A future goal is to use the hybrid model to inform the design of student activities and assessments aimed at developing expert-like explanatory and reasoning skills to do with biomolecular processes and their mechanisms.

By scientific modeling, I refer to a practice of embodying ideas of how and why a system works or a phenomenon occurs into a model, testing and evaluating the model, and revising the model to explain and predict the world. Yet engaging learners in scientific modeling can be challenging for a number of reasons, and the outcomes of larger scale implementations of various forms of model-based instruction is not always well-understood. This talk will share approaches and findings from scientific modeling research in which I have engaged over the past twenty year. The context of that research ranges from elementary school modeling of the hydrological cycle, to upper elementary and middle school work on the particle nature of matter and force and motion, to introductory college-level biology. In particular, I will highlight (a) the nature of scientific modeling in these contexts, (b) research findings and outcomes of engaging learners in modeling, (c) indicators of productive designs and support for learners and their instructors, and (d) implications for modeling across the spectrum of learning.

These were the two research questions that informed our four year ethnographic studies of two university bioengineering research laboratories—a tissue engineering and a neuroengineering lab. While the work and objectives for these labs were in no way related, we found similarities in their modeling practices and in their efforts to bridge the ­in vivo/in vitro divide. This talk will focus on two ­in vitro model systems, one from each lab, that were central to their work, with a particular emphasis on the coordination and integration of biological and engineering models needed to design, fabricate and use these devices. I will end with implications for education.

SiMSAM and DataSketch invite middle school students to create, revise, and test their own scientific models - expressed as computational simulations or data visualizations - using their own sketches and animations as starting points. We are exploring how such tools (1) allow youth the representational flexibility needed for them to truly engage as *authors* of scientific models, while also (2) structuring those ideas so that students' models can be compared and contrasted with one another, and with conventional scientific models. I will describe the technological, material, and social supports we have found that make complex, computationally-mediated modeling possible in the middle school classroom. I will also briefly describe some emerging theoretical and practical implications of this work for teacher education and theories of learning in modeling-rich classrooms.

We also want to support learning about the processes of scientific modeling in addition to domain knowledge. To these ends, I will present an interactive technology that seeks to support end-to-end modeling and model-based reasoning. I will describe how this technology evolves from more than a decade of research on modeling and model-based reasoning in STEM education, including (1) Structure-Behavior-Function (SBF) models of complex systems, (2) different types of causal explanations within SBF conceptual models, (3) use of SBF conceptual models to support learning about ecological systems, (4) integration of conceptual and simulation modeling, (5) metacognitive tutoring for learning about the processes of scientific modeling, (6) automatic spawning of simulations from conceptual models, and (7) integration with big data to support end-to-end modeling and model-based reasoning.

Thus, equipping future scientists and engineers with modeling and simulation skillset is essential to boost the chances for engineering discovery and innovation success. This agenda proposes a design-based research approach to investigate under what conditions modeling and simulation practices can effectively be integrated with disciplinary courses in undergraduate engineering education.

We have explored two different approaches for modeling and simulation in engineering education: a programming approach and a configuring approach.  Our preliminary findings suggest that in a programming approach, students encounter significant challenges mapping from conceptual to mathematical representations and from mathematical to algorithmic representations. In a configuring approach, students struggle with the complexity of the graphical user interface. However, once students overcome respective challenges, they seemed to benefit from the produced visualizations.  In a second study we further explored the impact of a learning design that implements principles of Cognitive Apprenticeship Model to support different stages of the modeling and simulation process following a programming approach.  Our preliminary findings suggest that this framework can be an appropriate support to help students develop modeling and simulation adaptive expertise.

Mathematical and computational transformations often play a central, but perhaps partially hidden, role in this bridge. These transformations can be approached in very transactional terms, necessary evils of little theoretical value to conceptual reasoning. Or the transformations can be approached in deeply theoretical terms, as central theoretical commitments about physical objects and mechanisms. I present data arguing for the strong benefits of the latter approach in helping high school biology students come to understand underlying phenomena and solve complex problems of genetic inheritance, which includes the productive and flexible use of situation models, process diagrams, and data representations.

A design research perspective then led to over a decade of iterative improvements in the design of these activities, strategies for their implementation, and strategies for assessing student work. Providing students with authentic mathematical modeling experiences goes beyond the provision of problems set in real-world contexts; authenticity extends to the assessment strategies employed. This presentation will focus on four dimensions used to evaluate students work and the nature of written feedback.  

I use pilot data collected from this learning environment to explore questions about the epistemic practices of biology students.

Biology curricula at all levels are dominated by an experimentalist paradigm. With “hybrid labs”, our goal is to situate experiments in a broader theoretical framework. A centerpiece of the design involves engaging students with computer simulations to motivate and extend laboratory experiments. Based on pilot data, we expect to see evidence of students integrating knowledge from these two modalities in their local epistemic practice, i.e. how they make and justify claims in their discourse and in their written lab reports.

In this talk, I describe our design for hybrid labs and present analyses of in-class talk, writing, and interviews to explore two questions: (1) How do students integrate knowledge from experiments and simulations in their local epistemic practices? (2) How do students understand the role, status, and degree of integration of knowledge from experiments and simulations?

The consequence is that theorizing begins with the individual psyche located in some body, which differs from the psyche of another, located in some other body. The social, as well as contact with the world, is the result of the interaction of these separate bodies and psyches. There is, however, a different lineage of thinking, running from Spinoza, Hegel, and Marx, and throughout the works of ecological thinkers such as Vygotsky, Dewey, and Bateson, according to which psychological phenomena are manifestations of a single source, social life. As a result of this lineage of thought, we find that the psyche is transactional, which has as its consequence that there is a primacy of the social. In this presentation, I build on this tradition to elaborate a transactional approach. I anchor my discussion in materials from undergraduate- and graduate-level design courses, and specifically with materials from design critique sessions. My analysis exhibits the irreducible nature of the transactional relation in the processes of growth and becoming—of design artifact, creator, and user. In this regard, the artistic nature of the creative process, “its total integrity is not located in the artifact and not located in the separately considered psyches of creator, and contemplator; it encompasses all three of these factors” (Vološinov, 1976, p. 97). I conclude with reflections on implications for redesigning tertiary education.

References

Vološinov, V. N. (1976). Freudianism: A Marxist critique. New York, NY: Academic Press.

They are used to describe observations, depict abstract principles, and embody invisible phenomena so that practitioners can reason about the nature of reality. In some STEM disciplines, such as organic chemistry, spatial representations are the lingua franca yet they also place a high cognitive demand on students’ spatial ability, and cause many students to struggle. With this presentation, I will summarize my recent research showing that manipulating 3-D molecular models can facilitate the understanding and use of diagrammatic representations. Results suggest that active and goal-oriented use of models help students off-load cognition onto the world by serving as cognitive scaffolds to support reasoning about complex spatial problems. Manipulating models does not just offload difficult mental transformation processes onto external actions. Use of models can lead to the internalization of these transformations and therefore train mental transformation processes. My research also suggests that it does not matter whether models are virtual or concrete or employ a high- or low-congruence interface. The key point is that students benefit as long as they enact processes with the models and receive interpretable feedback, providing new evidence for the potential of cost-effective virtual resources in STEM education. Ultimately, this work is the beginning of a framework that is intended to more precisely characterize the perceptual conditions and cognitive affordances offered by models under different learning conditions.