On the Embedded Complementarity of Agent-Based and Aggregate Reasoning in Students’ Developing Understanding of Dynamic Systems

Stroup, Walter M.; Wilensky, Uri

doi:10.1007/s10758-014-9218-4

Cited by 25 publications

(22 citation statements)

References 41 publications

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“…By levels, we do not refer to a developmental trajectory for understanding complex systems. Instead, we refer to descriptions that highlight some aspects of a system of interest, such as individual interactions, that constrain (but may not entirely illuminate) aspects at a different level, such as mathematical patterns (Holland, 2000;Stroup & Wilensky, 2014). For example, describing the micro-level physical relationship between gas particles and their container highlights what sort of behavior might emerge from an air pressure system.…”

Section: Conceptual Framework: the Calculus Of Complex Systemsmentioning

confidence: 99%

Patterns, Probabilities, and People: Making Sense of Quantitative Change in Complex Systems

Wilkerson

Wilensky

2014

Journal of the Learning Sciences

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The learning sciences community has made significant progress in understanding how people think and learn about complex systems. But less is known about how people make sense of the quantitative patterns and mathematical formalisms often used to study these systems. In this article, we make a case for attending to and supporting connections between the behavior of complex systems, and the quantitative and mathematical descriptions. We introduce a framework to examine how students connect the behavioral and quantitative aspects of complex systems and use it to analyze interviews with 11 high school students as they interacted with an agent-based simulation that produces simple exponential-like population growth. Although the students were comfortable describing many connections between the simulation's behavior and the quantitative patterns it generated, we found that they did not readily describe connections between individual behaviors and patterns of change. Case studies suggest that these missed connections led students who engaged in Correspondence should be addressed to Color versions of one or more of the figures in the article can be found online at www.tandfonline. com/hmet. QUANTITATIVE CHANGE IN COMPLEX SYSTEMS 205productive patterns of sense-making to nonetheless make errors interpreting quantitative patterns in the simulation. These difficulties could be resolved by drawing students' attention to the graph of quantitative change featured in the simulation environment and the underlying rules that generated it. We discuss implications for the design of learning environments, for the study of quantitative reasoning about complex systems, and for the role of mathematical reasoning in complex systems fluency.

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Section: Conceptual Framework: the Calculus Of Complex Systemsmentioning

confidence: 99%

Patterns, Probabilities, and People: Making Sense of Quantitative Change in Complex Systems

Wilkerson

Wilensky

2014

Journal of the Learning Sciences

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“…Using mechanisms to explain population-level outcomes is a mode of causal reasoning often employed by students (Grotzer, Derbiszewska, & Solis, 2017). There is much ABM research on student explanations of complex systems using levels-based reasoning (Sengupta & Wilensky, 2009;Chi, Roscoe, Slotta, Roy,& Chase, 2012;Stroup & Wilensky, 2014). However, scholarship on how learners use mechanistic reasoning is sparse.…”

Section: Mechanistic Reasoning Frameworkmentioning

confidence: 99%

Making sense of models: How teachers use agent‐based modeling to advance mechanistic reasoning

Hsiao¹,

Lee²,

Klopfer³

2019

Brit J Educational Tech

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Computer modeling promotes mechanistic reasoning when learners build and analyze models of complex systems to explore causal mechanisms and use models to generate patterns. StarLogo Nova, an agent-based modeling environment, enables novice programmers to model a system's individual components and investigate its emergent, collective behavior. Through case analysis of teachers using StarLogo Nova, we demonstrate how agent-based modeling advances thinking about mechanisms generating phenomenon. Teachers who used simulation combined with the decoding of StarLogo Nova models utilized mechanistic reasoning to make sense of how and why complex phenomenon emerged.

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“…It has been argued that agent‐level reasoning develops before aggregate‐level reasoning (Goldstone & Wilensky, ; Levy & Wilensky, ). When students use agent‐based‐based models to investigate and understand complex phenomena, the instructional goal is to support the development of a particular form of explanation: agent‐aggregate complementary explanations (Abrahamson & Wilensky, ; Stroup & Wilensky, ). These are explanations in which learners explain a pertinent aspect of the emergent phenomenon in terms of relevant individual‐ or agent‐level attributes and relationships.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Along the dimension of analysis of student learning, we make the following contributions. First, while several frameworks for analyzing student reasoning of complexity have been proposed (Chi, ; Hmelo‐Silver & Pfeffer, ; Stroup & Wilensky, ; Wilensky & Reisman, ; Wilkerson‐Jerde & Wilensky, ; etc. ), we present a more nuanced analysis of the progressive refinement of children's explanations about ecosystems using the lens of mechanistic reasoning (Machamer, Darden, & Carver, ; Russ, Scherr, Hammer, & Mikeska, ).…”

Section: Introductionmentioning

confidence: 99%

Development of Mechanistic Reasoning and Multilevel Explanations of Ecology in Third Grade Using Agent‐Based Models

et al. 2016

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In this paper, we present a third-grade ecology learning environment that integrates two forms of modeling--embodied modeling and agent-based modeling (ABMs)--through the generation of mathematical representations that are common to both forms of modeling. The term "agent" in the context of ABMs indicates individual computational objects or actors that obey simple rules assigned or controlled by the user. It is the interactions between these agents that give rise to emergent, aggregate-level behaviors in complex systems. While several researchers have argued for the effectiveness of ABMs for learning about complex systems, the design of classroom activity systems using ABMs, especially for elementary students, has received relatively less attention. In this paper, we report on a 2-week long proof-of-concept study conducted in a third-grade classroom of 15 students in which students began with an embodied modeling activity of foraging behavior, followed with the generation of mathematical inscriptions based on their embodied actions, and finally, conducted further inquiry of interdependence in an ecosystem using two separate ABMs. Furthermore, we show that the lens of mechanistic reasoning can be productively used to identify the process of students' conceptual development of interdependence in an Correspondence to: Pratim Sengupta;

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On the Embedded Complementarity of Agent-Based and Aggregate Reasoning in Students’ Developing Understanding of Dynamic Systems

Cited by 25 publications

References 41 publications

Patterns, Probabilities, and People: Making Sense of Quantitative Change in Complex Systems

Patterns, Probabilities, and People: Making Sense of Quantitative Change in Complex Systems

Making sense of models: How teachers use agent‐based modeling to advance mechanistic reasoning

Development of Mechanistic Reasoning and Multilevel Explanations of Ecology in Third Grade Using Agent‐Based Models

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