Processes and outcomes of systems thinking in an interactive modeling environment

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An, Sungeun
Goel, Ashok K.
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Modern society is full of natural, social, technological, and socio-technical systems, and thus systems thinking is an essential skill for prospering in the modern society. Developing interactive environments for supporting learning about complex systems requires a robust understanding how learners engage in systems thinking in various learning contexts. In this interdisciplinary work, I use theories and techniques from cognitive science, learning science, and artificial intelligence to develop an understanding of processes and outcomes of systems thinking for college students in pedagogical learning contexts and unspecified learners in self-directed learning contests in the domain of ecology. To achieve this goal, I present the Virtual Experimentation Research Assistant (VERA; vera.cc.gatech.edu)-- an interactive modeling environment to promote understanding and reasoning about ecological systems. VERA enables learners to access large-scale biological knowledge from the Encyclopedia of Life (EOL), construct conceptual models of ecological systems, run agent-based simulations of these models, and revise the models and simulations as needed to explain ecological phenomena. I have used VERA to complete four studies. The first two field studies were conducted in pedagogical contexts. The first study explored the effects of modeling in acquiring domain knowledge. I found that engaging in ecological modeling using VERA helped college students acquire biological knowledge. I also found that access to large-scale domain knowledge helped them construct more complex models and develop a larger number of hypotheses for a given problem. The second study investigated college students’ behaviors in estimating the parameters for agent-based simulations. I discovered that college students use multiple cognitive strategies for parameter estimation such as systematic search, problem reduction/decomposition, and global/local search. VERA is now accessible through Smithsonian Institution’s EOL website (eol.org), and it is used by thousands of self-directed learners around the world. The third study conducted a fine-grained analysis of self-directed learners' behaviors and models outside pedagogical contexts. I used a variety of learning analytics methods to analyze these behaviors including sequential data mining, hierarchical clustering, and Markov chain models. I found that self-directed learners engage in three types of behaviors: observation, construction, and exploration. The fourth study explored the effects of guided learning and self-exploration on modeling behaviors, model quality, and transfer of learning in a pedagogical context. Using in situ A/B experiments, I found that self-exploration in systems thinking leads to more complex and varied models whereas guidance in systems thinking does not have significant benefits in efficiency and accuracy for transfer of learning. Together these four studies lead to a robust understanding of how adult students learn about systems thinking and how to design interactive modeling environments to support self-directed systems thinking in open and ill-defined problems.
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