Explaining systems: Investigating middle school students' understanding of emergent phenomena

Abstract

Science, as with all cognitive activities, is fundamentally a matter of interpretation, sense‐making, and explanation. This study focused on a small group of middle school students as they developed understanding of a particular type of phenomena: emergent systems. Such systems are notable in that macro‐level properties emerge as the result of micro‐level interactions between system components. I describe students' initial understanding of emergent systems, as well as the ways in which their thinking came to reflect the following heuristics: (a) recognition that there may not be a singular causal force underlying the system; (b) distinguishing between micro‐ and macro‐levels of analysis; and (c) comprehending that even small changes at the micro‐level can have significant effects at the macro‐level (Resnick, 1994). I conclude by considering some implications for science education. © 2000 John Wiley & Sons, Inc. J Res Sci Teach 37: 784–806, 2000

Instruction in Computer Modeling Can Support Broad Application of Complex Systems Knowledge

“…Learners struggle to grasp complex systems ideas, such as those involved in equilibrium in chemistry and evolution in biology (Bishop and Anderson, 1990;Wilensky and Resnick, 1999;Penner, 2000Penner, , 2001Charles, 2002Charles, , 2003Stieff and Wilensky, 2003), because learners typically employ a centralized schema (i.e., a cognitive framework that helps to interpret, understand, and explain information). Learners with well-developed centralized and "clockwork" mindsets assume systematic control whenever they see patterns in the world and favor explanations that rely upon leaders, rules, and prearranged structures (Resnick, 1994;Resnick and Wilensky, 1998;Jacobson, 2001).…”

“…Limited working memory spans may prohibit mental simulations involving hundreds of agent-level interactions simultaneously (Narayanan and Hegarty, 1998). Similarly, learners struggle to correctly connect the agent-level rules to the aggregate pattern (Wilensky and Resnick, 1999;Penner, 2000). Learners' reasoning often "slips" between levels, as they simplify complex systems by viewing micro-and macrolevels as similar rather than distinct.…”

“…This allows learners to connect aggregate behavior with the relevant agent-level variables. Further, agent-based models easily illuminate how complex patterns arise in ways that other methods (e.g., differential equations) cannot (Penner, 2000;Sherin, 2001). For example, agent-based models can clearly present how complex patterns arise by visually displaying the individual agents and their interactions (Scaife and Rogers, 1996).…”

“…Specifically, students programmed J. H. Conway's game, Life, which has three simple rules: (1) a living patch with two or three alive neighbors remains alive, (2) a dead patch with three neighboring alive cells becomes alive, and (3) all other alive patches become dead. The three simple rules can lead to complex, self-sustaining or apparently chaotic patterns and provide considerable potential for exploring emergence (Penner, 2000;Beer, 2014). Students programmed the model so they could click on individual patches to switch them between being alive and dead.…”

Turning Transfer Inside Out: The Affordances of Virtual Worlds and Mobile Devices in Real World Contexts for Teaching About Causality Across Time and Distance in Ecosystems

“…Research shows that students often frame systems concepts in reductive forms (Jacobson 2001) that fail to capture complex dynamics (e.g. Grotzer and Basca 2003;Hmelo-Silver and Pfeffer 2004;Penner 2000;Raia 2008); learners typically constrain their consideration to the immediate time frame and to local contexts. Further, students often hold limited notions of the environmental concepts that embed complex causal features.…”