Reasons for difficulties in understanding and teaching complex systems

The earlier sections have described some difficulties in complex systems understanding. This section looks at what may have given rise to the difficulties in teaching and learning complex systems. Appreciating the factors hindering teachers’ understanding and teaching of complex systems can better direct solutions to address them.

Several reasons have been put forth by researchers and they can be roughly organized into three categories. The first category criticizes the science curricula. As briefly mentioned in the introduction, the way existing science curricula has been typically organized is said to linear and disparate (Mohan, Chen & Anderson, 2009; Sabelli, 2006; Senge et al., 2000). Such curricula fall short of a coherent framework for understanding diverse complex systems. The way science curricula have been set up for teaching and learning about scientific systems, tends to delineate the systems into components for easier understanding and neglect the complexity aspect (Mitchell, 2009; Parnafes, 2010; Resnick, 1994). This approach may work well for teaching and learning about systems that are not complex, but such reductionist approach without a complementary emphasis on the relationships among the components does not facilitate teaching and learning of complex systems (Hmelo-Silver & Azevedo, 2006; Lemke & Sabelli, 2008; Lesh, 2006). The challenge for complex systems instruction then lies in

42 making sense of the science curricula and emphasizing the complexity in the scientific systems.

The second category of reasons is somewhat related to the first but specifically refers to a lack of instructional resources to engage in complex systems (Klopfer et al., 2009; Jacobson & Wilensky, 2006). Such engagement can mean exploring the ideas of complex systems through curricular and instructional activities, interpreting scientific systems and phenomena from a complex systems perspective and learning about these ideas directly or indirectly through science instruction. In one of their studies, Yoon and Klopfer (2006) noted the difficulties their 47 teacher-participants in a professional development program faced in teaching complex systems using StarLogo, an agent-based modeling tool that allows learners to model and visualize system-level patterns from the perspective of component-level processes. They made several program design changes and found that teachers were more inclined to use the modeling tool if they had ready- made curriculum materials available to them. Pallant, Lee and Pryputniewicz (2012) also argue that having an instructional activity that can be easily implemented in a regular science classroom can encourage the classroom engagement in complex systems, in their case Earth’s climate. Collectively, these studies allude that with more classroom-ready resources for complex systems instruction, the challenges in teaching the ideas may be surmountable.

The third category of reasons argues from a conceptual viewpoint (Resnick & Wilensky, 1998). One hypothesis points to the dominant scientific paradigm that frames people’s cognition. This paradigm which emphasizes reducing systems to the simplest

43 components and variables, and analyzing them as direct cause-and-effect relationships - much like how one would take apart a clock to see how it works - has been so deeply fixated in people that they generally have trouble in seeing the world in any other ways (Capra, 1996; Yoon, 2008; 2011). Another theory suggests that these conceptual challenges may have arisen because of the different ontological categories the ideas belong to (Chi, 2005; Slotta, 2011; Slotta & Chi, 2006). Defining ontological categories as “the basic categories of realities or the kinds of existence in the world, such as concrete objects, events, and abstractions” (Chi, 2005, p. 163), Chi differentiates between emergent processes which are systemic phenomena occurring as a result of localized interactions at the component level, and direct processes which are also systemic events but arising from direct movements of substances. Slotta (2011) clarifies that many students ontologically perceive emergent processes such as diffusion as direct processes because they have little or no psychological representation of the emergent ontology and hence are unable to ascribe that ontology to these processes. Along similar lines, Wilensky and Resnick (1999) observed students’ and pre-service teachers’ inability to tell apart the behaviors at the component level and the patterns at the system level. They term this phenomenon “confusion of levels” and argue that it is one source of people’s deep misunderstandings about emergent phenomena in the world. “Levels” here refers to descriptions representing the phenomenon at various physical scales (e.g., macroscopic and microscopic). This confusion is believed to be caused by a misguided attribution of intentionality to otherwise random and localized interactions at the component level. Taken collectively, these researchers highlight that the difficulty of understanding

44 complex systems may have to do with the way people perceive and interpret the processes underpinning these systems.

As plausible as the above-mentioned arguments may seem, these reasons remain hypotheses at best since there has been only one study (Yoon & Klopfer, 2006) to systematically investigate the reasons behind the difficulties in teaching complex systems. However, as that particular study is done in a professional development context of teachers learning to implement a computer modeling intervention, how these reasons hold up in a regular science classroom context requires a separate investigation. It is possible that there may be other teacher-related contextual factors impeding their understanding and instruction of complex systems. An empirical investigation of the reasons behind the challenges will go a long way in deepening the understanding of this issue.