We present an interface for programming relationships between two or more NetLogo [18] models running concurrently. The interface is designed specifically to help high school aged novices explore and define computatio...
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ISBN:
(纸本)9781450335904
We present an interface for programming relationships between two or more NetLogo [18] models running concurrently. The interface is designed specifically to help high school aged novices explore and define computational relationships between agentbased models, and to investigate how prompting learners to reason about the relationships between complex systems may change how they reason about the systems individually. Copyright is held by the owner/author(s).
'Hybrid modeling' is an innovative technological platform that enables students to link multi-agent models ("in" the computer) and electronic sensors ("outside") in real time. The platform ...
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ISBN:
(纸本)0805861742
'Hybrid modeling' is an innovative technological platform that enables students to link multi-agent models ("in" the computer) and electronic sensors ("outside") in real time. The platform is designed for learners to validate, refine, and debug their computer models using real-world data. Also, the technology broadens the types of scientific explorations possible in classrooms. Pilot studies suggest a real-to-virtual reciprocity that catalyzes further inquiry toward deeper understanding.
The last ten years have seen a proliferation of introductory programming environments for younger learners. Increasingly, these environments are moving into the “cloud” where they can be accessed through web browser...
The paper is a case study of technology-facilitated argumentation. Several graduate students, the first four authors, present and negotiate complementary interpretations of a diagram generated in a computer-simulated ...
The paper is a case study of technology-facilitated argumentation. Several graduate students, the first four authors, present and negotiate complementary interpretations of a diagram generated in a computer-simulated stochastic experiment. Individuals use informal visual metaphors, programming, and formal mathematical analysis to ground the diagram, i.e., to achieve a sense of proof, connection, and understanding. The NetLogo modeling-and-simulation environment (Wilensky, 1999) serves to structure the authors' grounding, appropriating, and presenting of a complex mathematical construct. We demonstrate individuals' implicitly diverse explanatory mechanisms for a shared experience. We show that this epistemological diversity, sometimes thought to undermine learning experiences, can, given appropriate learning environments and technological fluency, foster deeper understanding of mathematics and science.
We propose a new class of crossover operators for genetic algorithms (CrossNet) which use a network-based (or graph-based) chromosomal representation. We designed Cross-Net with the intent of providing a framework for...
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ISBN:
(纸本)9781605581309
We propose a new class of crossover operators for genetic algorithms (CrossNet) which use a network-based (or graph-based) chromosomal representation. We designed Cross-Net with the intent of providing a framework for creating crossover operators that take advantage of domain-specific knowledge for solving problems. Specifically, GA users supply a network which defines the epistatic relationships between genes in the genotype. CrossNet-based crossover uses this information with the goal of improving linkage. We performed two experiments that compared CrossNet-based crossover with one-point and uniform crossover. The first experiment involved the density classification problem for cellular automata (CA), and the second experiment involved fitting two randomly generated hyperplane-defined functions (hdf's). Both of these exploratory experiments support the hypothesis that CrossNet-based crossover can be useful, although performance improvements were modest. We discuss the results and remain hopeful about the successful application of CrossNet to other domains. We conjecture that future work with the CrossNet framework will provide a useful new perspective for investigating linkage and chromosomal representations. Copyright 2008 ACM.
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 with...
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ISBN:
(纸本)9781450300728
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. Copyright 2010 ACM.
This paper provides an overview of a symposium that explored the implications of complexity for the field of the learning sciences. Two papers explored aspects of learning about complex systems in the domains of physi...
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Multi-agent modeling has been successfully used in several scientific fields, oftentimes transforming scientists' practice and mindsets. Educational researchers have also realized the potential of this modeling ap...
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Multi-agent modeling has been successfully used in several scientific fields, oftentimes transforming scientists' practice and mindsets. Educational researchers have also realized the potential of this modeling approach for learning. Studies have suggested that students are able to understand concepts above their expected grade level after interacting with curricula developed using multi-agent simulation. However, most multi-agent models are exclusively 'on-screen', without connection to the physical world. Real-time model validation and real-world sensing are very challenging to accomplish with extant modeling platforms. As an attempt to address this issue, we designed a technological platform to enable students to seamlessly connect multi-agent models and electronic sensors, in real time. The platform is designed for learners to validate, refine, and debug their computer models using real-world data. This paper focuses on the technical and pedagogical aspects of this project, describing pilot studies which suggest a real-to-virtual reciprocity that catalyzes further inquiry toward deeper understanding
There is growing diversity in the design of introductory programming environments. Where once all novices learned to program in conventional text-based languages, today, there exists a growing ecosystem of approaches ...
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作者：
Stonedahl, ForrestAnderson, DavidRand, WilliamEECS Dept.
Center for Connected Learning and Computer-Based Modeling Northwestern University Evanston IL United States Decisions
Operations and Information Technology University of Maryland College Park MD United States Dept. of Marketing
Center for Complexity in Business University of Maryland College Park MD United States
Agent-based models can replicate real-world patterns, but finding parameters that achieve the best match can be difficult. To validate a model, a real-world dataset is often divided into a training set (to calibrate t...
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