Yu Guo

Northwestern University | NU · Learning Science

This commentary responds to Hjorth and Wilensky’s article by providing more literature and conversations on three themes: the piecemeal view of knowledge, the difficulties of identifying causality in complex systems, and some considerations on applying computational methods to knowledge analysis.

This research paper presents a design-based research study centring on a constructionist curricular unit, called Mind the Gap (MTG), which was designed to help high school students learn about a complex and controversial social issue in the United States—wealth inequality. The four-day-long unit was implemented in eight economics classes with a tot...

Mind the Gap (MTG) is a curricular unit revolving around a series of three agent-based participatory simulations (ABPSs). The goal of the MTG curricular unit is to help high school students understand important mechanisms of wealth inequality in the U.S., through the lens of complex systems with NetLogo HubNet-based participatory activities. ABPSs...

This HubNet model is the second model of the MTG series. Mind the Gap (MTG) is a curricular unit revolving around a series of three agent-based participatory simulations (ABPSs). The goal of the MTG curricular unit is to help high school students understand important mechanisms of wealth inequality in the U.S. through the lens of complex systems wi...

This HubNet model is the third model of the MTG series. Mind the Gap (MTG) is a curricular unit revolving around a series of three agent-based participatory simulations (ABPSs). The goal of the MTG curricular unit is to help high school students understand important mechanisms of wealth inequality in the U.S. through the lens of complex systems wit...

The purpose of this work is to understand the learning affordances of two different types of simulation-based activities through a quasi-experimental comparative study. We created two versions of a curricular unit—one as a participatory simulation activity (PartSim), the other as a model-based inquiry activity with an agent-based model (ABM). We im...

This poster shows a preliminary analysis on log data generated from students’ interactions with a code-first learning environment. In such environments students learn about the mechanisms of target phenomena by programming the behavior of computational agents with code blocks. We characterize students’ programing practices by four measures we devel...

Invited talk at the Department of Neurobiology and Behavior, Cornell University

Complex systems are challenging for students, especially younger students, to learn. In this paper, we argue that agent-based models (ABMs) of social insects provide an engaging and effective space for students to learn powerful ideas about complex systems. We designed a curricular unit called BeeSmart centering on ABMs of honeybees’ collective beh...

This paper presents Frog Pond, an interactive code-first learning environment about biological evolution. We deployed Frog Pond as part of a six-day curricular unit on natural selection implemented in six 7th grade science classes. Here we describe a case study of two students, Charlie and Aaron who participated in the unit. Comparing pre- and post...

Children's understanding of chance has been one of the central interests of Piagetian research. Most existing studies on this topic use laboratory questions such as the marble tilt box problem or its variations. We propose to present a study on high school students' understanding of randomness within the context of working with computational models...

Reasoning about causal mechanisms is central to scientific inquiry. In science education, it is important for teachers and researchers to detect students’ mechanistic explanations as evidence of their learning. In this paper, we introduce a semi-automated method which combines association rule mining and human rater’s insights to identify students’...

Students’ difficulties with randomness and probability have been identified as one of the major sources of difficulties in learning complex systems and emergent phenomena. This paper explores students’ difficulties with different types of randomness in complex systems through a design-based research study revolving around a computer model-based sci...

BeeSmart HubNet model is part of the BeeSmart Curricular Unit. It is the HubNet version of the BeeSmart Hive Finding model in NetLogo Models library. For detailed description of honeybees hive finding phenomenon, please see the info tab of the BeeSmart Hive Finding model. Unlike the BeeSmart Hive Finding model, the BeeSmart HubNet model allows stud...

Reasoning about causal mechanisms is central to scientific inquiry. In science education, it is important for teachers and researchers to detect students’ mechanistic explanations as evidence of their learning, especially related to causal mechanisms. In this paper, we introduce a semiautomated method that combines association rule mining with huma...

Complex systems concepts are important to learn for both scientific and pedagogical reasons. However, research has shown that these concepts are difficult for students to learn. In this paper we present a series of microworlds called “BeeSmart” for students to engage in a constructionist learning experience through manipulating NetLogo models, disc...

The BeeSmart Hive Finding model shows the swarm intelligence of honeybees during their hive-finding process. A swarm of tens of thousands of honeybees can accurately pick the best new hive site available among dozens of potential choices through self-organizing behavior. The mechanism in this model is based on Honeybee Democracy (Seeley, 2010) with...

Complex problem solving and motivation are often argued as the most important benefits of massively multiplayer role-playing online games. However, little empirical research exists to support these assertions. Current research and educational game design theory are insufficient to explain the relationship between complex problem solving, motivation...