Overview of Complex Educational Phenomena and Challenges of Visualizations

• In Event: Utilizing Visualizations of Complex Educational Phenomena as a Way to Justify Data Sources and Sampling

Abstract

The purpose of this section is to provide an overview of complex phenomena in educational settings and the importance of using visualizations in designing appropriate methods for their study. However, visualizing complex phenomena can be challenging. To begin, we will discuss the difference between complex and complicated phenomena as they are often confused. Complicated phenomena can be taken apart into its parts and reassembled from those parts (i.e., it can be described by mathematical systems founded on linearity). A complex phenomena cannot “be analyzed and integrated either in reality or in mathematical representation” (Byrne & Callaghan, 2014, p. 4). A complex phenomena is a set of interrelated elements, within a system containing nested levels, in which the whole is greater than the sum of its parts (Bullock, 2017). These types of phenomena are often referred to as complex adaptive systems (CAS). Davis and Sumara (2006) identified the necessary qualities that are inherent in CAS including, 1) the system is self-organized and manifests bottom-up emergence; 2) the systems’ coherence depends mostly on short-range relationships not centralized control or top-down administration; 3) the system has a nested structure; 4) the system is ambiguously bounded while also being organizationally closed; and 5) the system is far from equilibrium (i.e., it is continuously changing).
In order to design studies of educational CAS, a way must be devised to understand the nested, ambiguously bounded structures of the system while also capturing short-range relationships (within levels) as they relate to long-range relationships (across levels) through the evidence of emergent properties. This understanding is essential to appropriately selecting data sources and sampling plans as well as analytical strategies that can capture non-linear, dynamic factors. Visualizations can provide a way to qualitatively journey towards this type of understanding. Poth and Bullock (in pressb) suggested four research design decisions essential to studying educational CAS. These included the appropriate identification of the unit of analysis at the appropriate nested-level of the system. While this is a good first step, in further work Poth and Bullock (in pressa) suggested ways in which to visually represent CAS structures through the identification of first-, second-, and third-order agents (i.e., stakeholders in an educational setting) and emergent properties. Guiding questions from Poth and Bullock (in press) can help overcome visualization challenges and will be discussed:
1) What are the distinct and/or overlapping nested levels?;
2) Where are the different stakeholders (or collections of stakeholders) located in relation to each other and how are they influencing each other within and across levels?;
3) How does the unit of analysis and nested structures help us conceptualize and measure the emergent properties?
4) What data sources are necessary to capture influences on and from the unit of analysis within and across nested levels?;
5) What sampling techniques are needed to capture both individual and organizational emergent properties relevant for the research question?; and
6) How do the emerging understandings of the educational CAS, the chosen unit of analysis, and the selected data sources inform study procedures?

Author

• Emma P. Bullock, Sam Houston State University
  Presenting Author