Working Together: Integrating Computational Modeling Approaches to Investigate Complex Phenomena

Complex systems are made up of many entities, whose interactions emerge into distinct collective patterns. Computational modeling platforms can provide a powerful means to investigate emergent phenomena in complex systems. Some research has been carried out in recent years about promoting students' modeling practices, specifically using technologically advanced tools and approaches that allow students to create, manipulate, and test computational models. However, not much research had been carried out on the integration of several modeling approaches when investigating complex phenomena. In this paper, we describe the design principles used to develop a middle school unit about ants' collective behavior that integrates three modeling approaches: conceptual drawn models, agent-based models, and system dynamics models. We provide results from an initial implementation of an 8th grade curricular unit, indicating that students engaged with several aspects of the modeling practice. Students' conceptual knowledge about ant pheromone communication increased following learning the unit. We also found gains in students' metamodeling knowledge about models as tools for investigating phenomena. We discuss the affordances and challenges of engaging students with several modeling approaches in science classroom

Learning an Alternative Car-Following Technique to Avoid Congestion with an Instructional Driving Simulator

This paper addresses the problem of traffic congestion through a learning perspective, highlighting the capabilities of Information and Communication Technologies to transform society. Recent physical and mathematical analysis of congestion reveals that training drivers to keep a safe distance systematically contributes to the emergence and maintenance of interference congestion (so-called phantom traffic jam). This paper presents the WaveDriving Course (WDC), a simulated learning environment designed to help drivers progress from the traditional Drive-to-keep-Distance (DD) technique to a new car-following (CF) principle better suited for wave-like traffic, Drive-to-keep-Inertia (DI). The WDC is based on the ordinary knowledge of the driver (e.g., going through a series of traffic lights), and presents this situation in terms of two possible simultaneous behavioral strategies. The driver has the opportunity to verify that it is possible to achieve the same objective with different consequences. Finally, the WDC checks to what extent this learning generates transfer patterns in the analogous case of CF. The paper focuses on results concerning the first WDC module: the traffic-light analogy. Forty-two participants followed the whole learning procedure for about 30 min. An evaluative CF test was administered before and after visioning the tutorial and practicing on the simulator. Overall, transference from this traffic-light analog to the CF situation (posttest) was successful. Results confirm the adoption of the expected DI strategies (speed variability decreased, distance and distance variability to leader increased, fuel consumption decreased, platoon elongation decreased etc.). The need to improve the WDC teaching of the appropriate CF distance is discussed