Thinking Like a Wolf, a Sheep, or a Firefly: Learning Biology Through Constructing and Testing Computational Theories—An Embodied Modeling Approach

By Uri Wilensky, K. Reisman. 2006

648 citations recorded by the source corpus.

Subfields: Genetics, History and Philosophy of Science, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/a48d9e6aeb0a99cb6a2c501f4febf2afb7bc49a7

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Citations in the corpus

Examining the Relationship Between Students’ Understanding of the Nature of Models and Conceptual Learning in Biology, Physics, and Chemistry (2011, methodology): “…of scientific models and found that students’ understanding of scientific models contained more causal and mechanistic elements when they worked with computer-based multi-agent models when compared to equation-based models (Sengupta & Wilensky, 2009; Wilensky, 1999b; Wilensky & Reisman, 2006).”
System thinking and feeding relations: learning with a live ecosystem model (2012, background): “For most biology students, learning biology mostly involves memorizing details about static components of phenomena while disregarding systemic changes in time (Wilensky and Reisman 2006).”
Identifying Essential Epistemic Heuristics for Guiding Mechanistic Reasoning in Science Learning (2018, background): “In addition, they have argued that levels slippage, or conflating the behaviors and properties of agents at one level with those at a different scalar level, underlies many errors in student reasoning (e.g., Levy & Wilensky, 2009; Sengupta & Wilensky, 2009; Wilensky & Reisman, 2006).”
Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners (2009, background): “The pedagogical benefits of working with scientific models rests critically on having students develop models to articulate their own understanding of how a scientific phenomenon behaves (Acher, Arcà, & Sanmartı́, 2007; Schwarz & White, 2005; Wilensky & Reisman, 2006; Windschitl et al., 2008).”
Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework (2013, background): “…novice learners to develop a deep understanding of dynamic, aggregate-level phenomena— both in kinematics and ecological systems by bootstrapping, rather than discarding their agent-level intuitions (Dickes and Sengupta 2012; Wilensky and Reisman 2006; Levy and Wilensky 2008; Sengupta et al. 2012).”
Misconceived Causal Explanations for Emergent Processes (2012, background): “Aside from these general reasons, there are two additional reasons why we believe that dynamic simulations may be particularly appropriate for learning about emergent processes (Wilensky & Reisman, 2006).”
The Robust Volterra Principle* (2008, background): “For more information on ‘Wolf Sheep Predation,’ see Wilensky & Reisman (2006) .”
Inventing a “Mid Level” to Make Ends Meet: Reasoning between the Levels of Complexity (2008, background): “…Wilensky and colleagues have explored students’ agent-based reasoning and how it can develop through the practice of agent-based modeling (Centola, McKenzie, & Wilensky, 2000; Resnick & Wilensky, 1993, 1998; Wilensky, 1993, 1997a, b, 1999a; Wilensky & Reisman, 2006; Wilensky & Resnick, 1995, 1999).”
Commonsense Conceptions of Emergent Processes: Why Some Misconceptions Are Robust (2005, background): “Their approach to promoting students’ understanding of various emergent processes is to try various innovative instructional intervention such as by engaging students in explorations of computer and participatory simulations (Colella, 2000), as well as role-playing activities that model emergent pro”
Defining Computational Thinking for Mathematics and Science Classrooms (2016, background): “The pedagogical power of computational models comes not just from students using existing models, but also from enabling students to design, build, and assess models of their own (Brady et al. 2015; Gilbert 2004; Penner 2000; White 1993; Wilensky 1995, 2003; Wilensky and Reisman 2006; Wilkerson-Jerd”