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

Abstract

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;

Toward a Phenomenology of Computational Thinking in STEM Education

“…In the former case, it enables learners to generate models of continuous movement (Newtonian mechanics) from temporal aggregations of discrete actions Sengupta, Farris & Wright, 2012). In the latter case, it enables learners to model dynamical systems (e.g., ecological interdependence) in which multiple agents are simultaneously interacting with each other (Dickes & Sengupta, 2013;Dickes et al, 2016).…”

“…Because the agent-level interactions, attributes and behaviors are often body-syntonic (i.e., can be explained and understood through simple embodied actions of the child), young children can model complex scientific phenomena using such forms of computing (Papert, 1980;Danish, 2014;Dickes et al, 2016;Levy & Wilensky, 2008). As Dickes et al (2016) demonstrated, by engaging in agent-based modeling, even young learners can investigate and develop explanations of system-level, emergent behaviors from the perspective of agents within the system. They key argument supported by these studies is that thinking like the agent provides learners an intuitive pathway in exploring emergent outcomes of the system (Wilensky & Reisman, 2006;Levy & Wilensky, 2008).…”

Micro–macro compatibility: When does a complex systems approach strongly benefit science learning?

“…Several earlier designs for learning science with a complex systems perspective usually implemented the computational ABM approach, which encourages causal reasoning and leads to an understanding of the mechanisms underlying the phenomena (Levy & Wilensky, 2008). Such designs have demonstrated important advantages to learning through a complexity approach (in chemistry: Holbert & Wilensky, ; Levy & Wilensky, ; Stieff & Wilensky, 2003; Wilensky, ; in physics: Brady, Holbert, Soylu, Novak, & Wilensky, ; Sengupta & Wilensky, ; in biology: Dickes et al., ; van Mil, Boerwinkel, & Waarlo, ; Van Mil, Postma, Boerwinkel, Klaassen, & Waarlo, ; Wilensky & Reisman, ; Wilkerson‐Jerde, Wagh, & Wilensky, ; in materials science: Blikstein & Wilensky, ; in robotics: Levy & Mioduser, ). For example, in biology education, van Mil et al.…”

“…Finally, this approach provides a general framework that addresses the need to connect between different systemic phenomena (Goldstone & Wilensky, 2008). Several studies have reported on the particular benefits of a complexity approach toward improving student learning in chemistry (Levy & Wilensky, 2009b;Dickes et al, 2016;Holbert & Wilensky, 2014).…”

Complexity in Mathematics Education

“…For example, participatory simulations, in which each learner can themselves play the role of an agent in complex system using embodied, physical and computational forms of modeling, have been shown to be effective pedagogical approaches for modeling emergent mathematical behaviors by highlighting and integrating both individual and collective insight (e.g., Colella 2000). Second, the emphasis on such participatory forms of mathematical modeling, in the context of modeling complex phenomena, can act as a bridge across disciplines (e.g., biology and mathematics education, see Dickes et al 2016). A third key insight is the notion of reflexivity across disciplines -that is, conceptual development within each scientific, engineering and mathematical discipline can be deepened further when relevant phenomena are represented as complex systems using mathematical modeling in ways that also highlight key practices of engineering design such as design thinking (Sengupta et al 2013).…”