Uri Wilensky

Promoting Transfer by Grounding Complex Systems Principles

Understanding scientific phenomena in terms of complex systems principles is both scientifically and pedagogically important. Situations from different disciplines of science are often governed by the same principle, and so promoting knowledge transfer across disciplines makes valuable cross-fertilization and scientific unification possible. Although evidence for this kind of transfer has historically been controversial, experiments and observations of students suggest pedagogical methods for promoting transfer of complex systems principles. One powerful strategy is for students to actively interpret the elements and interactions of perceptually grounded scenarios. Such interpretation can be facilitated through the presentation of a situation alongside a description of how the agents in the situation are behaving, and by students exploring and constructing computational models of the situation. The resulting knowledge can be both concretely grounded yet highly perspective dependent and generalizeable. We discuss methods for coordinating computational and mental models of complex systems, the roles of idealization and concreteness in fostering understanding and generalization, and other complementary theoretical approaches to achieving transfer.

To block or not to block, that is the question: students' perceptions of blocks-based programming

Blocks-based programming tools are becoming increasingly common in high-school introductory computer science classes. Such contexts are quite different than the younger audience and informal settings where these tools are more often used. This paper reports findings from a study looking at how high school students view blocks-based programming tools, what they identify as contributing to the perceived ease-of-use of such tools, and what they see as the most salient differences between blocks-based and text-based programming. Students report that numerous factors contribute to making blocks-based programming easy, including the natural language description of blocks, the drag-and-drop composition interaction, and the ease of browsing the language. Students also identify drawbacks to blocks-based programming compared to the conventional text-based approach, including a perceived lack of authenticity and being less powerful. These findings, along with the identified differences between blocks-based and text-based programming, contribute to our understanding of the suitability of using such tools in formal high school settings and can be used to inform the design of new, and revision of existing, introductory programming tools.

Paradox, programming, and learning probability: A case study in a connected mathematics framework☆

Abstract Formal methods abound in the teaching of probability and statistics. In the Connected Probability project, we explore ways for learners to develop their intuitive conceptions of core probabilistic concepts. This article presents a case study of a learner engaged with a probability paradox. Through engaging with this paradoxical problem, she develops stronger intuitions about notions of randomness and distribution and the connections between them. The case illustrates a Connected Mathematics approach: that primary obstacles to learning probability are conceptual and epistemological; that engagement with paradox can be a powerful means of motivating learners to overcome these obstacles; that overcoming these obstacles involves learners making mathematics—not learning a “received” mathematics and that, through programming computational models, learners can more powerfully express and refine their mathematical understandings.

Examining the Relationship Between Students' Understanding of the Nature of Models and Conceptual Learning in Biology, Physics, and Chemistry

This research addresses high school students’ understandings of the nature of models, and their interaction with model‐based software in three science domains, namely, biology, physics, and chemistry. Data from 736 high school students’ understandings of models were collected using the Students’ Understanding of Models in Science (SUMS) survey as part of a large‐scale, longitudinal study in the context of technology‐based curricular units in each of the three science domains. The results of ANOVA and regression analyses showed that there were differences in students’ pre‐test understandings of models across the three domains, and that higher post‐test scores were associated with having engaged in a greater number of curricular activities, but only in the chemistry domain. The analyses also showed that the relationships between the pre‐test understanding of models subscales scores and post‐test content knowledge varied across domains. Some implications are discussed with regard to how students’ understanding of the nature of models can be promoted.

Evolving viral marketing strategies

One method of viral marketing involves seeding certain consumers within a population to encourage faster adoption of the product throughout the entire population. However, determining how many and which consumers within a particular social network should be seeded to maximize adoption is challenging. We define a strategy space for consumer seeding by weighting a combination of network characteristics such as average path length, clustering coefficient, and degree. We measure strategy effectiveness by simulating adoption on a Bass-like agent-based model, with five different social network structures: four classic theoretical models (random, lattice, small-world, and preferential attachment) and one empirical (extracted from Twitter friendship data). To discover good seeding strategies, we have developed a new tool, called BehaviorSearch, which uses genetic algorithms to search through the parameter-space of agent-based models. This evolutionary search also provides insight into the interaction between strategies and network structure. Our results show that one simple strategy (ranking by node degree) is near-optimal for the four theoretical networks, but that a more nuanced strategy performs significantly better on the empirical Twitter-based network. We also find a correlation between the optimal seeding budget for a network, and the inequality of the degree distribution.

Complex Systems View of Educational Policy Research

Agent-based modeling and network analysis can help integrate knowledge on “micro-level” mechanisms and “macro-level” effects. Education researchers have struggled for decades with questions such as “why are troubled schools so difficult to improve?” or “why is the achievement gap so hard to close?” We argue here that conceptualizing schools and districts as complex adaptive systems, composed of many networked parts that give rise to emergent patterns through their interactions (1), holds promise for understanding such important problems. Although there has been considerable research on the use of complex systems ideas and methods to help students learn science content (2), only recently have researchers begun to apply these tools to issues of educational policy.

Learning axes and bridging tools in a technology-based design for statistics

We introduce a design-based research framework, learning axes and bridging tools, and demonstrate its application in the preparation and study of an implementation of a middle-school experimental computer-based unit on probability and statistics, ProbLab (Probability Laboratory, Abrahamson and Wilensky 2002 [Abrahamson, D., & Wilensky, U. (2002). ProbLab. Northwestern University, Evanston, IL: The Center for Connected Learning and Computer-Based Modeling, Northwestern University. http://www.ccl.northwestern.edu/curriculum/ProbLab/]). ProbLab is a mixed-media unit, which utilizes traditional tools as well as the NetLogo agent-based modeling-and-simulation environment (Wilensky 1999) [Wilensky, U. (1999). NetLogo. Northwestern University, Evanston, IL: The Center for Connected Learning and Computer-Based Modeling http://www.ccl.northwestern.edu/netlogo/] and HubNet, its technological extension for facilitating participatory simulation activities in networked classrooms (Wilensky and Stroup 1999a) [Wilensky, U., & Stroup, W. (1999a). HubNet. Evanston, IL: The Center for Connected Learning and Computer-Based Modeling, Northwestern University]. We will focus on the statistics module of the unit, Statistics As Multi-Participant Learning-Environment Resource (S.A.M.P.L.E.R.). The framework shapes the design rationale toward creating and developing learning tools, activities, and facilitation guidelines. The framework then constitutes a data-analysis lens on implementation cases of student insight into the mathematical content. Working with this methodology, a designer begins by focusing on mathematical representations associated with a target concept—the designer problematizes and deconstructs each representation into a pair of historical/cognitive antecedents (idea elements), each lying at the poles of a learning axis. Next, the designer creates bridging tools, ambiguous artifacts bearing interaction properties of each of the idea elements, and develops activities with these learning tools that evoke cognitive conflict along the axis. Students reconcile the conflict by means of articulating strategies that embrace both idea elements, thus integrating them into the target concept.

Finding forms of flocking: evolutionary search in ABM parameter-spaces

While agent-based models (ABMs) are becoming increasingly popular for simulating complex and emergent phenomena in many fields, understanding and analyzing ABMs poses considerable challenges. ABM behavior often depends on many model parameters, and the task of exploring a models parameter space and discovering the impact of different parameter settings can be difficult and time-consuming. Exhaustively running the model with all combinations of parameter settings is generally infeasible, but judging behavior by varying one parameter at a time risks overlooking complex nonlinear interactions between parameters. Alternatively, we present a case study in computer-aided model exploration, demonstrating how evolutionary search algorithms can be used to probe for several qualitative behaviors (convergence, non-convergence, volatility, and the formation of vee shapes) in two different flocking models. We also introduce a new software tool (BehaviorSearch) for performing parameter search on ABMs created in the NetLogo modeling environment.

Fostering computational literacy in science classrooms

An agent-based approach to integrating computing in secondary-school science courses.

GasLab—an Extensible Modeling Toolkit for Connecting Micro-and Macro-properties of Gases

Computer-based modeling tools have largely grown out of the need to describe, analyze, and display the behavior of dynamic systems. Recent decades have seen increasing recognition of the importance of understanding the behavior of dynamic systems—how systems of many interacting elements change and evolve over time and how global phenomena can arise from local interactions of these elements. New research projects on chaos, self-organization, adaptive systems, nonlinear dynamics, and artificial life are all part of this growing interest in system dynamics. The interest has spread from the scientific community to popular culture, with the publication of general-interest books about research into dynamic systems (Gleick 1987; Waldrop, 1992; GellMann, 1994; Kelly, 1994; Roetzheim, 1994; Holland, 1995; Kauffman, 1995).