Toward a Learning Progression of Complex Systems Understanding

Abstract

Recent research on what students know about complex systems shows that they typically have challenges in understanding particular system ideas such as nonlinearity, complex causality, and decentralized control. Yet this research has yet to adopt a systematic approach to learning about complex systems in an ordered way in line with the Next Generation Science Standards’ call for learning pathways that guide teaching and learning along a developmental continuum. In this paper, we propose that learning progressions research can provide a conceptual framework for identifying a learning pathway to complex systems understanding competence. As a first step in developing a progression, we articulate a sequence of complex systems ideas, from the least to most difficult, by analyzing students’ written responses using an item response theory model. Results show that the easiest ideas to comprehend are those that relate to levels or scales within systems and the interconnected nature of systems. The most difficult ideas to grasp are those related to the decentralized organization of the system and the unpredictable or nondeterministic nature of effects. We discuss implications for this research in terms of developing curricular content that can guide learning experiences in grades 8–12 science education.

Multidimensional trajectories for understanding ecosystems

“…Few research studies have documented the learning trajectories that students take to understanding ecosystems. Our focus on learning trajectories is consistent with the recent emphasis on learning progressions (Duncan & Hmelo‐Silver, 2009; Duncan & Rivet, 2013; Duschl et al, 2007; Yoon et al, 2019). Taken together, there is a need to develop more precise mappings of learning trajectories and at smaller grain sizes.…”

“…Although these explanatory accounts of systems may not necessarily fit canonical scientific explanations (Machamer et al, 2000), these show the necessity of connecting the visible SBF with the unseen, which Yoon et al (2019) found to be the most challenging facets of complex systems understanding for students to grasp. Yoon et al also suggest that the more visible facets—identifying different components, levels of the system, and how they interact—would be the most productive topics in middle school instruction, aligning with our findings.…”

“…Although these explanatory accounts of systems may not necessarily fit canonical scientific explanations (Machamer et al, 2000), these show the necessity of connecting the visible SBF with the unseen, which Yoon et al (2019) found to be the most challenging facets of complex systems understanding for students to grasp. Yoon et al…”

Learning Analytics Using Social Network Analysis and Bayesian Network Analysis in Sustainable Computer-Based Formative Assessment System

“…Further, the system can record individual behaviors while interacting with the system as much as possible. These data sources can offer the possibility of capturing evidence for identifying a meaningful problem-solving process and learning patterns [36,37]. This analytic framework is based on computational psychometrics.…”

A Patterning Approach to Complexity Thinking and Understanding for Students: A Case Study

“…Complexity concepts are entering some curricula, yet there are no discoverable approaches to the explicit teaching and learning of complexity thinking and understanding for students and educators in the Australian or US curriculums. For example, Yoon, Goh and Yang (2019) explain that while the Next Generation Science Standards (NGSS, 2013; National Research Council, 2012) incorporate complexity concepts into the framework for the American Science curriculum, their investigation into the development of explicit learning pathways for complexity thinking discovered that many students continue to attempt complexity understanding using linear cause and effect logic.…”