In the connectedlearning projects, we are studying students' learning of content through exploring and constructing computer-based models of that content. This paper presents a case study of a high school physics...
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In the connectedlearning projects, we are studying students' learning of content through exploring and constructing computer-based models of that content. This paper presents a case study of a high school physics teacher's design and exploration of a computer-based model of gas molecules in a box. We follow up the case study with shorter vignettes of students' exploration and elaboration of the Gas-in-a-Box model. The cases lead us to analyze and discuss the role of model-based inquiry in science and mathematics education as well as to draw some general conclusions with respect to the design of modeling languages and the design of pedagogies and activities appropriate for model-based inquiry in classroom settings.
This column will publish short (from just a few paragraphs to ten or so pages), lively and intriguing computer-related mathematics vignettes. These vignettes or snapshots should illustrate ways in which computer envir...
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Using the well-known Artificial Anasazi simulation for a case study, we investigate the use of genetic algorithms (GAs) for performing two common tasks related to robustness checking of agent-based models: parameter c...
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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 depend...
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Mechanistic reasoning in social domains is understudied. We speculate that the ways that students reason about social phenomena share enough similarities with how they reason about physical and life sciences that we c...
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作者：
Berland, MatthewWilensky, UriUniv Wisconsin
Dept Curriculum & Instruct Madison WI 53706 USA Northwestern Univ
Dept Learning Sci Northwestern Inst Complex Syst Ctr Connected Learning & Computer Based Modeling Chicago IL 60611 USA Northwestern Univ
Dept Comp Sci Ctr Connected Learning & Comp Based Modeling Northwestern Inst Complex Syst Chicago IL 60611 USA
Both complex systems methods (such as agent-basedmodeling) and computational methods (such as programming) provide powerful ways for students to understand new phenomena. To understand how to effectively teach comple...
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Both complex systems methods (such as agent-basedmodeling) and computational methods (such as programming) provide powerful ways for students to understand new phenomena. To understand how to effectively teach complex systems and computational content to younger students, we conducted a study in four urban middle school classrooms comparing 2-week-long curricular units-one using a physical robotics participatory simulation and one using a virtual robotics participatory simulation. We compare the two units for their effectiveness in supporting students' complex systems thinking and computational thinking skills. We find that while both units improved student outcomes to roughly the same extent, they engendered different perspectives on the content. That is, students using the physical system were more likely to interpret situations from a bottom-up ("agent") perspective, and students using the virtual system were more likely to employ a top-down ("aggregate") perspective. Our outcomes suggest that the medium of students' interactions with systems leads to differences in their learning from and about those systems. We explore the reasons for and effects of these differences, challenges in teaching this content, and student learning gains. The paper contributes operationalizable definitions of complex systems perspectives and computational perspectives and provides both a theoretical framework for and empirical evidence of a relationship between those two perspectives.
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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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 o...
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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://***/curriculum/ ProbLab/ ]). ProbLab is a mixed-media unit, which utilizes traditional tools as well as the NetLogo agent-basedmodeling-and-simulation environment (Wilensky 1999) [Wilensky, U. (1999). NetLogo. Northwestern University, Evanston, IL: The center for connected learning and computer-based modeling http://***. ***/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 wit
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