Designing Computer-Supported Complex Systems Curricula for the Next Generation Science Standards in High School Science Classrooms

Abstract

We present a curriculum and instruction framework for computer-supported teaching and learning about complex systems in high school science classrooms. This work responds to a need in K-12 science education research and practice for the articulation of design features for classroom instruction that can address the Next Generation Science Standards (NGSS) recently launched in the USA. We outline the features of the framework, including curricular relevance, cognitively rich pedagogies, computational tools for teaching and learning, and the development of content expertise, and provide examples of how the framework is translated into practice. We follow this up with evidence from a preliminary study conducted with 10 teachers and 361 students, aimed at understanding the extent to which students learned from the activities. Results demonstrated gains in students' complex systems understanding and biology content knowledge. In interviews, students identified influences of various aspects of the curriculum and instruction framework on their learning.

Teaching and Learning About Complex Systems in K–12 Science Education: A Review of Empirical Studies 1995–2015

“…A number of studies (e.g., Klopfer, Yoon, & Perry, 2005; Repenning et al, 2015; Vattam et al, 2011; Wilensky & Reisman, 2006; Yoon, Koehler-Yom, Anderson, Lin, & Klopfer, 2015) describe computational tools that have been developed to visualize structures and mechanisms that enable users to view the evolution of systems over time. A particularly robust line of research has been aimed at developing agent-based simulations represented in modeling tools such as NetLogo (Wilensky & Reisman, 2006) and StarLogo (Yoon et al, 2016). These simulations allow students to manipulate and construct facsimiles of scientific systems in which changes in initial conditions, random variation, decentralized interactions, and self-organized emergent behaviors (among other system characteristics) are investigated.…”

Interactions between reasoning about complex systems and conceptual understanding in learning chemistry

“…Several earlier designs for learning science with a complex systems perspective implemented the computational ABM. Such designs have demonstrated important advantages to learning through a complexity approach to support conceptual change in several scientific domains [(Holbert & Wilensky, ; Levy & Wilensky, ) in chemistry; (Brady et al, ) in physics; (Dickes, Sengupta, Farris, & Basu, ; Yoon et al, ; Wilkerson‐Jerde et al, ) in biology; (Blikstein & Wilensky, 2009; Jacobson et al, ) in materials science; (Levy & Mioduser, ) in robotics], or in systems thinking as a more general form of reasoning (Rates et al, ). Learning through this approach focuses on entities and their actions, such as movement, interactions, and global flows, and allows students to comprehend parallel processes by which emergent phenomena form.…”

Complex Systems Research in K12 Science Education: A Focus on What Works for Whom and under Which Conditions

“…Users can then compare the code to the visualization of the system on the screen as it is running in real time. Research using such agent-based modeling tools has shown that the ability to study the code while visualizing the system, in addition to the ability to observe systems processes as they emerge over time, creates far deeper meaning for users than when learning from static images [4,5]. StarLogo also allows users to manipulate system variables to create different initial conditions, modify interactional states and agent characteristics, and adjust how fast or slow or specify the period of time that the simulation runs in order to experiment with the tool.…”