Learning Functional and Causal Abstractions of Classroom Aquaria Ashok K. Goel 1 , Swaroop S. Vattam 1 , Spencer Rugaber 1 , David Joyner 1 , Cindy E. Hmelo-Silver 2 , Rebecca Jordan 3 , Sameer Honwad 2 , Steven Gray 3 , Suparna Sinha 2 Design & Intelligence Laboratory, School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA 30332. Department of Educational Psychology, Graduate School of Education, Rutgers University, New Brunswick, NJ School of Environmental and Biological Sciences, Department of Ecology, Evolution, and Natural Resources, and the Program in Science Learning, Rutgers University, New Brunswick, NJ 08901. Abstract Structure-Behavior-Function (SBF) models of complex systems use functions as abstractions to organize knowledge of structural components and causal processes in a system. We describe an interactive learning environment called ACT (Aquarium Construction Toolkit) for constructing simple SBF models of classroom aquaria, and report on a case study on the use of SBF thinking and the ACT tool in middle school science classes. We present initial data indicating that SBF thinking supported in part by the ACT tool leads to enhanced understanding of functions and behaviors of aquaria. Keywords: Science education, Middle school science, Complex systems, Ecological systems, Functional models, Interactive learning. Motivation and Goals Understanding of complex systems enables important tasks such as monitoring, measurement, sensemaking, troubleshooting, explanation, prediction, diagnosis, redesign and design. Thus, understanding complex systems has been recognized as a key idea in science education in national science standards (National Research Council, 1996) as well as local standards (e.g., New Jersey Department of Education, 2006). However, understanding complex systems is cognitively hard not only because of the large number of components and variables in a given system, but also because complex systems are dynamical and contain feedback loops (Forrester 1968) and exhibit hierarchical structure but are only nearly decomposable (Simon 1996); causal processes at one abstraction level in a complex system emerge out of interactions among components and processes at lower levels; and while some components of a complex system may be visible, many components, relations and processes typically are invisible. Thus, understanding complex systems challenges cognitive resources such as attention, memory and perception. The juxtaposition of understanding complex systems as an educational standard and the cognitive difficulty of understanding complex systems in turn poses a practical challenge for cognitive and learning sciences. Theories of understanding complex systems in terms of functional models use functions as abstractions for organizing knowledge of structural components and causal processes (e.g., Chandrasekaran 1994a, 1994b; Kitamura et al. 2004; Rasmussen 1986). In Structure-Behavior-Function (SBF) models, for example, Structure refers to components of a complex system as well as connections among the components; Behaviors pertain to causal processes in the complex system; and Functions are abstractions of structural components and causal behaviors (Goel et al, 1996; Prabhakar & Goel, 1998; Goel, Rugaber & Vattam 2009). Representations of structural components and causal processes specify the functions they accomplish; representations of functions in turn act as indices into the components and processes that combine to accomplish them. The SBF theory of understanding complex systems has led to lesson plans and interactive tools for learning about complex systems in science education. Our ongoing ACT project, for example, is an interactive learning environment that enables middle school children to construct and simulate SBF models of classroom aquaria (Vattam et al. 2010). An initial study indicates that teacher-led SBF thinking about aquaria, supported in part by use of ACT by small teams of students, led to significant improvement in understanding the basic structure, behaviors and functions of aquaria. However, we also found that in practice, middle school teachers and students did not use ACT the way we had planned. Instead of using ACT to construct and simulate full SBF models of aquaria, middle school students in our studies used the tool mainly to construct SBF graphical models of aquaria (Jordan et al. 2009). In this paper, we report on a new study that utilizes a new version of the ACT interactive tool. The new version of ACT (ACT3) directly builds on our observations of SBF thinking practices in middle school science classrooms in the initial studies as well as feedback from the middle school teachers and students on the use of the previous version of ACT (ACT2). Preliminary results from new studies of SBF thinking about aquaria, stimulated, scaffolded and supported in part by the new ACT tool, appear to replicate the findings from the earlier studies with the new and more engaging tool.
[1]
Ashok K. Goel,et al.
Functional modeling for enabling adaptive design of devices for new environments
,
1998,
Artif. Intell. Eng..
[2]
Lei Liu,et al.
Focusing on Function: Thinking below the Surface of Complex Natural Systems.
,
2008
.
[3]
B. Chandrasekaran,et al.
Functional Representation and Causal Processes
,
1994,
Adv. Comput..
[4]
Janet L. Kolodner,et al.
Designing to Learn About Complex Systems
,
2000
.
[5]
Cindy E. Hmelo-Silver,et al.
Fish Swim, Rocks Sit, and Lungs Breathe: Expert-Novice Understanding of Complex Systems
,
2007
.
[6]
Ashok K. Goel,et al.
Towards Design Learning Environments - I: Exploring How Devices Work
,
1996,
Intelligent Tutoring Systems.
[7]
Ashok K. Goel,et al.
Understanding Complex Natural Systems by Articulating Structure-Behavior-Function Models
,
2011,
J. Educ. Technol. Soc..
[8]
Ashok K. Goel,et al.
Connecting the Visible to the Invisible: Helping Middle School Students Understand Complex Ecosystem Processes.
,
2010
.
[9]
Lei Liu,et al.
Promoting complex systems learning through the use of conceptual representations in hypermedia
,
2009
.
[10]
Ashok K. Goel,et al.
Structure, behavior, and function of complex systems: The structure, behavior, and function modeling language
,
2008,
Artificial Intelligence for Engineering Design, Analysis and Manufacturing.
[11]
Michelene T. H. Chi,et al.
Commonsense Conceptions of Emergent Processes: Why Some Misconceptions Are Robust
,
2005
.
[12]
N. Hari Narayanan.
Chapter 17: The Impact of Cognitively Based Design of Expository Multimedia
,
2007
.
[13]
Ashok K. Goel,et al.
Structure , Behavior and Function of Complex Systems : The SBF Modeling Language
,
2008
.
[14]
Denis Alamargot,et al.
Written documents in the workplace
,
2007
.
[15]
Riichiro Mizoguchi,et al.
Deployment of an ontological framework of functional design knowledge
,
2004,
Adv. Eng. Informatics.
[16]
Rebecca Jordan.
Modeling Practices as a Function of Task Structure
,
2009
.
[17]
Nancy J. Nersessian,et al.
Creating Scientific Concepts
,
2008
.
[18]
Annemarie S. Palincsar,et al.
Social constructivist perspectives on teaching and learning.
,
1998,
Annual review of psychology.
[19]
M. Resnick,et al.
Thinking in Levels: A Dynamic Systems Approach to Making Sense of the World
,
1999
.
[20]
J. Forrester.
Principles of systems : text and workbook, chapters 1 through 10
,
1968
.
[21]
Gautam Biswas,et al.
LEARNING BY TEACHING: A NEW AGENT PARADIGM FOR EDUCATIONAL SOFTWARE
,
2005,
Appl. Artif. Intell..
[22]
B. Chandrasekaran,et al.
Functional representation: A brief historical perspective
,
1994,
Appl. Artif. Intell..
[23]
J. Shea.
National Science Education Standards
,
1995
.
[24]
J. Clement.
Creative Model Construction in Scientists and Students: The Role of Imagery, Analogy, and Mental Simulation
,
2008
.
[25]
Tetsuo Tomiyama,et al.
A review of function modeling: Approaches and applications
,
2008,
Artificial Intelligence for Engineering Design, Analysis and Manufacturing.
[26]
Robert B. Kozma,et al.
Th e Use of Multiple Representations and the Social Construction of Understanding in Chemistry
,
2012
.
[27]
Susanne P. Lajoie,et al.
Constructing knowledge in the context of BioWorld
,
2001
.
[28]
Daniel D. Suthers,et al.
Technology affordances for intersubjective meaning making: A research agenda for CSCL
,
2006,
ICCE.
[29]
Ashok K. Goel,et al.
Appropriating conceptual representations: a case of transfer in a middle school science teacher
,
2010,
ICLS.
[30]
Jens Rasmussen,et al.
Information Processing and Human-Machine Interaction
,
1986
.