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Complexity Theory and Teacher Education

Summary and Keywords

Complexity theory offers possibilities for thinking about the challenges and opportunities inherent in teaching, teacher learning, and many other networked systems in teacher education. Complexity theory is a theory of learning systems that provides a framework for those interested in examining how systems develop and change. It is transdisciplinary in nature, drawing on insights from diverse fields across both the hard and social sciences, and when applied to education may provide a complex rather than simplistic view of teaching and learning. Further, complexity theory has the potential to offer a powerful alternative to linear and reductionist conceptualizations, with implications for methodology of teacher education research as well as its analysis and design. This small but growing body of work has influenced teacher education in two ways. First, scholars have argued for complexity theory’s usefulness as a framework to understand and describe how teacher education functions as a complex system. The second category of work, smaller than the first, uses complexity theory to frame and analyze empirical studies. Much of the emerging body of research conducted from a complexity theory perspective is descriptive and largely confirms what has been theorized. Empirical work has confirmed that a variety of systems, at different levels, influence teacher learning and pedagogical decisions. Gaps in our knowledge still exist, however, as theorists and researchers continue to struggle with how complexity theory can best serve teacher education for the benefit of teachers and students.

Keywords: teacher education, complexity theory, teaching, teacher learning, educational philosophy, research methods

Complexity theory is a theory of learning systems (Davis & Sumara, 2006; Holland, 1995; Johnson, 2001). It is not a single, unified theory but can be considered a “paradigmatic orientation” (Kuhn, 2008, p. 182) or “umbrella notion” (Davis & Sumara, 2006, p. 7) for understanding complex phenomena. According to complexity theory, patterns of organization emerge within complex adaptive systems (Johnson, 2001; Weaver, 1948). Understanding why and how these patterns emerge is of interest to complexity scientists, so they investigate the dynamic, non-linear interactions among agents and elements within and across systems. Complexity theory is thus a framework for examining how systems change, develop, evolve, and emerge (Davis & Sumara, 2006; Morrison, 2006).

However, developing understandings of complex, interacting systems in areas such as teacher education is difficult for several reasons. First, the issues of education demand questions that do not focus simply on understanding systems’ organization but how that organization—the patterns of complexity—relates to system outcomes, such as teachers’ practices and students’ abilities. Second, while reductive measurements can begin to grapple with identifying factors that contribute to learning outcomes, “[c]omplexity is not just another category of phenomena, but an acknowledgement that some phenomena are not deterministic and cannot be understood strictly through means of analysis (i.e., literally, by taking part or cutting up)” (Davis & Simmt, 2003). Outcomes from complex social systems are difficult to predict because the agentic action and beliefs of the people involved—including teachers and students—evolve out of multiple systems. Thus, complexity theory and its methodologies have the potential to offer “a powerful alternative to the linear, reductionist approaches to inquiry” (Davis & Sumara, 2006, p. xi).

Complexity theory offers possibilities for thinking about educational practice and research. When applied to education, it makes apparent how systems of interest (e.g., schools, classrooms, and teacher education programs) adapt due to the various agents (e.g., teachers, students, and administrators) and elements (e.g., state curriculum, length of class period, and accreditation standards) at play. In this article, we specifically focus on how complexity theory has been applied to teacher education and its implications for conceptualizing teacher learning and designing teacher education programs.

Historical Orientation

Many credit physicist Warren Weaver (1948) as the first to articulate the notions of simple systems, disorganized complex (complicated) systems, and organized complexity. Simple systems have few components, and their actions can be described in detail and predicted: a ball that is dropped falls to the ground. Complicated systems have many components, but the individual parts can be examined and the whole represents the sum of the parts. The behaviors of both simple and complicated systems can be considered mechanical because they operate through linear causes and effects (Davis & Sumara, 2006). In contrast, complex systems emerge and adapt through interactions as they operate, and they transcend their parts. Components are not fixed but are in adaptive and in constant flux (Davis & Sumara, 2006). In short, both simple and complicated systems operate linearly and predictably, while complex systems operate nonlinearly and are difficult to predict and control.

Understandings of complexity theory evolved during the 1950s and 1960s (Davis & Sumara, 2006) across several fields. Now considered transdisciplinary, it “explicitly aims to embrace, blend, and elaborate the insights of any and all relevant domains of human thought” (Davis & Sumara, 2006, p. 8). It draws from diverse fields such as evolutionary biology, chaos theory, quantum physics, chemistry, cybernetics, systems theory, artificial intelligence, economics, and organizational management (Cochran-Smith, Ell, Ludlow, Grundoff, & Aitken, 2014; Johnson, 2001). Because of its transdisciplinary nature, researchers draw upon various terms when discussing complexity, including complexity theory, complexity research, complexity science, and complexity thinking.

The use of a complexity theory lens in educational research came later than in other fields. The work of Davis and Sumara (2006) is central to conceptualizations of complexity theory in education. However, researchers explored the notion of theorizing and studying the complexities of teaching by examining the situated nature of teaching and learning (Borko & Putnam, 1996) and through activity systems perspectives on teacher development (e.g., Grossman et al., 2000). Some of the conceptualizations from a complexity theory perspective build from prior ecological or complexity-oriented (Davis & Sumara, 2006) theories (e.g., Martin & Dismuke, 2018; Opfer & Pedder, 2011).

Key Aspects of Complexity Theory

Complexity theory is concerned with learning systems. In education, these systems include teachers and students, classrooms, schools, communities, bodies of knowledge, and cultures (Cochran-Smith et al., 2014; Davis & Sumara, 2006; Martin & Dismuke, 2018; McQuitty, 2012). Researchers have identified a number of different features or components exhibited by complex systems, although there is no single list of these features. Different researchers foreground different characteristics, depending on the elements of complex systems relevant to their work. Some features of complex systems include the following:

  • Self-organization: Within complex systems, self-organization occurs. Individuals, or agents, can become interconnected or interlinked. These systems can reorganize and adapt.

  • Emergence: Complex systems are always in the process of changing and becoming, and what they become is more than the sum of the individual traits of the agents. Emergence is not due to an overarching system or set of rules or structures; it occurs through the unique mix of agents within and across systems.

  • Nested: Complex systems exist within and are comprised of other complex systems. For example, a classroom exists within a grade-level team, within a school, within a district, and within a state. Understanding that systems are nested or layered prompts a focus on the recursive interactions among the components of single system as well as those between multiple systems.

  • Dynamic and far from equilibrium: Complexity theory acknowledges constant change and activity as systems interact and adapt. This continual activity means systems are rarely in a state of balance because they are in a process of continual change and response.

  • Difficult to predict outcomes: Due to the nature of complex systems, outcomes are difficult to predict. Unexpected consequences can arise from the interaction of the parts (Cochran-Smith et al., 2014).

  • Interactions: Interactions with complex systems are nonlinear and occur across multiple levels. Within complex systems diversity and redundancy live in tension, but both are necessary for change within systems (Davis & Sumara, 2006). Diversity refers to the range of agents’ ideas and responses (Davis & Sumara, 2010) within a system. Diversity allows for the possibility of new ideas and actions to be enacted or implemented within a complex system. However, redundancy, often in the form of common language and shared purpose or responsibility, can be considered “sameness among agents” (Davis & Simmt, 2003, p. 150). Redundancy allows agents within the systems to communicate with shared understanding.

  • Ambiguously bounded: Clear borders or boundaries are difficult to define in complex systems. Agents often fluidly move across or belong to multiple systems.

  • Feedback loops: Feedback loops occur within and across systems. These feedback loops can be positive, in which action is continually reinforced for the agent, or negative, in which the agent becomes aware of other options for action (Johnson, 2001). These loops can enhance or detract from change within the system (Cilliers, 2005).

Complex systems, such as schools, are comprised of various agents—including teachers, students, administrators—and components—such as district curricula, state standards for learning outcomes, federal regulations, norms of interaction, and resources. Similarly, systems such as teacher education include agents—students, professors, mentor teachers—and components—curriculum, program requirements, and accreditation standards.

Complexity Theory and Teacher Education

Complexity theory has influenced teacher education in two ways. First, teacher educators have argued for complexity theory’s usefulness as a framework to guide research and practice. Work in this category is philosophical and argues for the benefits of conceptualizing teacher education through a lens of complexity and describes how teacher education functions as a complex system. The second category of work, much smaller than the first, uses complexity theory to frame and analyze empirical studies.

(Re)conceptualizing Teacher Education Systems

Central to reconceptualizing teacher education in light of complex systems theory are arguments that the theory holds great promise for aiding both teacher educators and teacher education researchers in understanding the complexities of teacher education. Cochran-Smith et al. (2014) explain that the

growing knowledge of the parts and pieces of teacher education has not provided us with a powerful enough picture of how teacher education works as a whole or why disappointing outcomes continue to occur. . . . To develop more powerful explanations of teacher education, we may need new and more powerful research questions and theoretical explanations. (p. 3)

Complexity theory allows researchers to take a complex view and resist simplification of teacher and student learning (Cochran-Smith et al., 2014). Furthermore, it goes beyond prior theories to include physical and biological influences on learning, in addition to previous sociocultural perspectives (Davis & Sumara, 2006). While complexity theory offers the potential to develop powerful explanations of teacher education, Opfer and Pedder (2011) note the following:

Although there have been significant calls for a more complex conceptualization of teacher professional learning . . . the majority of writing on the topic continues to focus on specific activities, processes, or programs in isolation from the complex teaching and learning environments in which teachers live. (p. 377)

By adopting complexity theory to address issues of teacher education, complexivist scholars bring new directions to understanding teaching, how teachers learn to teach, and what influences this learning.

An Array of Systems

Teacher education as a whole can be conceptualized as a complex array of interdependent systems at multiple levels that seek to influence teaching and teacher learning (Cochran-Smith et al., 2014). The current body of literature has identified and described a variety of systems under the umbrella of teacher education, including teaching (Dotger & McQuitty, 2014; Martin & Dismuke, 2018; VanGeert & Steenbeck, 2014), teacher learning (Ludlow et al., 2017; Martin & Dismuke, 2018; McQuitty, 2012; Opfer & Pedder, 2011), and intersecting systems that influence both teaching and teacher learning systems, such as classroom contexts (Martin & Dismuke, 2018); pre-service education (Cochran-Smith et al., 2014; Ludlow et al., 2017; Waks, 2011); student teacher supervision (Clarke & Collins, 2007); teacher education structures and pedagogies (Clarke, Erickson, Collins, & Phelan, 2005; Ovens, 2017); and in-service professional development (Opfer & Pedder, 2011; Wetzels, Steenbeek, & VanGeert, 2016).

Teaching and teacher learning occupy a central role in complexity-oriented theorizations of teacher education, as they are conceptualized as systems themselves while at the same time they are viewed as outcomes of teacher education. From a complexity theory perspective, teaching and teacher learning are profoundly dependent, co-nested systems in which the constant exchange of information renders their boundaries ambiguous, yet each system can also be viewed as distinct. For instance, outcomes of the teacher learning system have to do with teaching, while the teaching system focuses on student outcomes (Martin & Dismuke, 2018).

Teaching as a System

In theorizing teaching as a system, Van Geert and Steenbeek (2014) describe what they refer to as the simplex systems of agents (e.g., teachers) that behave as embedded complex systems within larger systems. These simplex systems are composed of components such as beliefs, pedagogical content knowledge, and pedagogical practices that organize into “systems of dynamic and self-sustaining ideas, practices, motivations, and so forth” (p. 27). Interactions within the simplex system influence how the agent (teacher) interacts with components of the broader classroom system.

Similarly, Dotger and McQuitty (2014) conceptualize decision-making as coming from the teacher’s operative system in which the ideation level of the system (teacher knowledge, beliefs, attitudes) interact with those at the enacted level (teacher behaviors). This conceptualization builds from notions of complexity thinking in which knowledge and actions are considered parts of the whole. Dotger and McQuitty argue that these two levels interact dynamically both within and across each other. A particular understanding does not transfer directly into action, and actions cannot be traced to individual beliefs or ideas. Even the simplest teacher decisions can have multiple causal pathways (Opfer & Pedder, 2011). Additionally, drawing from Holland’s (1995) concepts of reproducing and recombining knowledge as building blocks within a complex system, Martin and Dismuke (2018) suggest that building blocks within the teacher’s operative system (knowledge, abilities, and attitudes) allow the teacher to respond adaptively to learners and situations within the classroom teaching/learning system. They argue that these need to be diverse enough to allow for novel rather than stagnant responses to the learning needs of individuals and the classroom collective.

Teacher Learning as a System

Martin and Dismuke (2018) theorize that building blocks within the operative system—knowledge, abilities, and attitudes—are the outcomes from what teachers have learned. Opfer and Pedder’s (2011) theorizations have focused on understanding processes of this learning from a complexity theory perspective and what it means for teaching and for professional development. They argue that “teacher learning must be conceptualized as a complex system rather than as an event” (p. 378). Complex systems thinking assumes that teacher learning involves many processes, mechanisms, actions, and elements that are interdependent and reciprocally influential. Change that occurs through teacher learning cannot be seen as a linear or even cyclical process in which changes to beliefs, practices, student outcomes, and further changes to beliefs or practices occur in an orderly fashion (Opfer & Pedder, 2011). Instead, they argue that there are many factors that influence teacher learning: “some may be preconditions, others may be catalysts, others may influence the way learning is produced, and others may be able to directly affect learning, but they also may all work together to produce learning” (p. 382). Thus, from a complexity theory perspective, dynamic influences from networked systems are crucial to teacher learning. Opfer and Pedder suggest, for instance, that change due to teacher learning occurs only through the conjunctions of a network of systems that account for both contexualized learning (e.g., learning from experience/practice) and decontextualized learning experiences (e.g., learning subject matter knowledge)—both important to effective practice. Others focus on theorizing the critical role that influential systems, such as teacher education, play in bringing perturbations to the teaching system and hence to teaching and learning relationships (e.g., Cochran-Smith et al., 2014; Wetzels et al., 2016).

Multidimensional and Dynamic Interactions

Separating out teaching and teacher learning systems from each other, as well as from other influencing systems, has been beneficial for theorizing which systems interact to influence teacher development and practice. These conceptualizations can be an inclusive or more focused view of the networks involved. For example, in order to theorize the development of teacher practice from a complexity theory perspective, Martin and Dismuke (2018) take an inclusive view. They conceptualize teaching and student learning as systems co-engaged in activity linked to ideation systems (e.g., understandings of writing processes). Teaching and student learning systems are embedded in classroom social systems, as well as in larger systems, such as schools, districts, and state and national policy systems. Further, they theorize that personal systems—such as teachers’ knowledge, abilities, attitudes, and identities—are nested within the teaching systems. Each of the systems is seen as affecting teacher practice and development. On the other hand, Wetzels et al. (2016), while acknowledging links to other systems, take a more focused view. Using the term “aspects,” they delineate influences on teacher development by selecting systems to do with the teacher, qualities of the intervention, and school contexts.

From this networked systems perspective, various multidimensional and dynamic interactions influence teaching and teacher development (Cochran-Smith et al., 2014). Theorization of these dynamics has included a focus on issues of systems’ co-adaptations (Davis & Sumara, 2006; Martin & Dismuke, 2018). For example, district- and/or state-specified curricular materials may be available, but how the materials are actually implemented in the classroom will depend on the teaching system. Actual use is therefore bound within both systems. A few have theorized issues of varied effects of influencing systems—the enhancement and diminishing of particular influences on teacher development (Cochran-Smith et al., 2014; Davis & Sumara, 2006). Additionally, in line with Holland’s (1995) notion of levers, wherein even a small influence can create cascading effects throughout the systems, Martin and Dismuke (2018) theorized about the influences of teacher education coursework on teachers’ operative systems. However, the nature of dynamic interactions of systems in teacher education—how they influence teaching learning—appears under-theorized (and under-researched) at the moment.

Using Complexity Theory to Inform Empirical Research

Although scholars have made strong arguments for complexity theory’s usefulness in conceptualizing and studying various aspects of teacher education, only a few studies have used it to guide data collection or analysis. Much of the available research is descriptive (Cochran-Smith et al., 2014) and largely confirms what has been theorized about the nature of teacher education. For example, teacher education and teacher learning do function as complex systems. Pre-service teacher education cohorts (Clarke, Erickson, Collins, & Phelan, 2005; Sanford, Hopper, & Starr, 2015), informal teacher development collectives (Fazio & Gallagher 2009), professional learning communities (Yoon, Liu, & Goh, 2010; Zellmayer & Margolin, 2005), and individual teachers’ learning (McQuitty, 2012) all exhibit features of complex systems. Empirical work has also confirmed that a variety of systems, at different levels, influence teacher learning and pedagogical decisions—including teachers’ personal systems of values, beliefs, and understandings; teacher education and professional development programs; school districts and individual schools; children in classrooms; and the systems that make large-scale policies (Ell et al., 2017; Martin & Dismuke, 2018; McQuitty, 2012; Wetzels et al., 2016).

Studies guided by complexity theory also explain how a simultaneity of influences (Davis & Sumara, 2006), emerging from different systems, prompt nonlinearity and variation in teacher learning (Phelps, Graham, & Watts, 2011; Wetzels et al., 2016). For example, Wetzels et al. (2016) described two teachers in the same professional development program. One teacher experienced high levels of principal and school support; a fit between the tenets of the professional development, her school’s teaching philosophy, and her own learning goals; and enthusiasm about participating in the program. These influences intertwined to create a positive spiral that resulted in positive learning outcomes. The second teacher experienced little principal and school support; little fit between the professional development and her regular teaching duties and learning needs; and frustration with the amount of time the program required. These intertwining influences “unraveled” (p. 97) into a negative experience in which little learning occurred.

Analyzing professional development through complexity theory has yielded explanations for why these programs sometimes fail to achieve their intended outcomes. Several studies confirm Opfer and Pedder’s (2011) contention that professional development has little impact if it does not adequately attend to teachers’ knowledge, beliefs, and past experiences as well as the norms and cultures of the schools where they teach (Dotger & McQuitty, 2014; Walton, Nel, Muller, & Lebeloane, 2014; Zein, 2016). Some of this work also provides insights into possible ways to design more effective professional learning opportunities. Zein (2016), for example, proposed a theoretical model of professional development that accounts for the multiple systems that enable and constrain the learning of English for Young Learners (EYL) teachers. Dotger and McQuitty (2014) theorized that, to be effective, professional development must do more than simply add new ideas and pedagogies to teachers’ operative systems; it must also address how teachers’ current knowledge and teaching interact with each other and with the new ideas and pedagogies that the professional development seeks to disseminate.

Research that analyzes teacher learning through complexity theory has provided insight into the (sometimes surprising) dynamics that create learning opportunities. For example, controversy and contradiction seem to prompt teachers’ learning in some cases (McQuitty, 2012; Zellmayer & Margolin, 2005), and chance happenings can become critical events that radically transform teachers’ beliefs and practices (Phelps, Graham, & Watts, 2011; Zellmayer & Margolin, 2005). In addition, peripheral members of teacher learning communities who seem like passive observers may significantly contribute to and impact the learning of the entire group (Zellmayer & Margolin, 2005). These findings have potential implications for designing effective teacher education systems, though it is currently unclear how teacher educators would—or if it is even possible for them to—intentionally prompt these dynamics to create positive learning outcomes.

A few researchers have described attempts to design teacher education and professional development through a complexity theory lens (Sanford, Hopper, & Starr, 2015; Yoon & Klopfer, 2006). For example, Yoon and Klopfer (2006) designed a professional development program for in-service teachers premised on four complex system design principles: feedback, adaptation, network growth, and self-organization. They found that attending to and designing for these features prompted teacher educators to identify salient system components, effectively distribute expertise, adapt and improve resources and activities, and build technological, human, and social capital—all processes that have a positive impact on teacher learning. They also identified three tensions that teacher educators must consider when they work within the complex systems where teachers learn: (1) balancing structure with encouraging teacher agency, (2) offering opportunities for learning new information and skills while also offering opportunities for exploring that information and skills within the context where it will be implemented, and (3) addressing short-term and long-term learning goals.

Although empirical research tends to confirm the usefulness of complexity-oriented conceptualizations of teacher education, there are some points of divergence. For example, the diversity of ideas and distributed expertise that exists in teacher education systems should be resources for learning and growth; however, they are not always leveraged in ways that lead to positive learning outcomes (Dotger & McQuitty, 2014; Yoon, Liu, & Goh, 2010). Additionally, Martin and Dismuke (2018) found that influences from co-nested systems interacted in very uneven and surprising ways. For example, systems of professional development seemed to fill a void in teachers’ knowledge and practice while classroom systems, including teachers’ interactions with students, did not. Findings such as these provide evidence that more empirical work is needed to understand how complex dynamics impact teacher education systems and how teacher education impacts systems of teacher learning.

Methodology and Complexity Theory

Complexity theory has the potential to impact the methodology of teacher education research as well as its analysis and design. For instance, Gilstrap (2013) argued that the innovative quantitative approaches used in artificial intelligence, such as probability analysis and multicollinearity, could be utilized to answer important educational questions. However, the methods that have been used in teacher education complexity research are qualitative in nature, keeping with research traditions in that area. Given the challenges of complexity theory research, some have attempted new strategies including system mapping (Ell et al., 2017) and a mixed-methods approach (Martin & Dismuke, 2018).

Two studies have used system mapping as a method to better understand complex teacher education systems. Ell et al. (2017) mapped the components of a teacher education program and found that candidates’ personal beliefs and values, mentor teachers, and children in classrooms exerted the strongest influences on what pre-service teachers learned. They argued that teacher educators should further investigate these influential system elements “in order to better understand teacher education’s variable outcomes” (p. 341). Mapping at the level of teachers’ collective understandings and practices, Dotger and McQuitty (2014) mapped the operative system of a small group of teachers who participated in a professional development. The findings indicated that teachers’ operative systems were coherent—every idea and pedagogy made sense in relation to all the other ideas and pedagogies—and that this coherence made it difficult for professional development to substantially impact teachers’ knowledge or their practice. The researchers argued that effective professional development might need to address multiple ideas and practices simultaneously because the interconnected nature of an operative systems makes it resistant to change. Thus, system mapping holds potential as a methodology for better understanding complex systems and identifying how to influence them.

Taking a different approach, Martin and Dismuke (2018) purposefully selected a mixed-methods design (e.g., Creswell & Plano, 2007; Johnson & Onwueguzie, 2004) to investigate complex influences on teacher understandings and practice. Complementary design (Greene, Caracelli, & Graham, 1989) allowed for overlapping and integration of quantitative data that looked at patterns both within and between groups, with qualitative data that illuminated individual differences. Emergence, rather than reduction, of teacher practice and the complex interactions of factors influencing it was the focus of investigation in this study.

Controversy and Challenges in Using Complexity Theory in Teacher Education

Despite the potential usefulness of complexity theory for researching, understanding, and addressing problems in teacher education, applying it to research and practice is challenging for a variety of reasons. Morrison (2008) raised several concerns about the fit between complexity theory tenets and educational endeavors such as teacher education. For example, unpredictability is a central characteristic of multiple-nested complex systems, which is problematic if teacher educators seek to develop programs that produce predictable outcomes. Because it is difficult to imagine a teacher education program that is not required to ensure, at least to some degree, that teachers learn particular knowledge and skills, it seems complexity theory may indeed “undermine its own power” (Morrison, 2008, p. 29) to address the needs and goals of teacher education.

Advocates of complexity theory in teacher education have responded to the problem of unpredictability in a different ways. For instance, Cochran-Smith et al. (2014) question whether teacher education research should seek to prescribe ways to guarantee particular outcomes; they instead argue that complexity theory can describe and explain the complex functioning of specific teacher education systems, which can “contribute insights about the particular that are also useful beyond the local context” (p. 19). Kuhn (2008) agrees, maintaining that descriptive research from a complexity theory mindset may be seen as a catalyst for change.

On the other hand, others present theoretical arguments that work at meshing the tenets of complexity theory with understandings to do with influencing teacher education outcomes. The work of Davis and Sumara (2006) has delved into issues of delineating the conditions that influence the development of systems such as those in teacher education. They maintain that randomness of development is balanced with coherence influenced by constraining structures of the system (Davis & Sumara, 2006). In other words, interactions between systems “coalesce in ways that are unpredictable but also highly patterned” (Clarke & Collins, 2007, p. 161). How social structures and co-embedded systems might serve as enabling constraints (Davis & Sumara, 2006) to influence patterned outcomes in teacher education has not yet been theorized.

Additionally, some have also noted the lack of moral or ethical intent inherent in complexity theory, while education is committed to human betterment (Kuhn, 2008; Morrison, 2008). This creates a lack of fit with teacher education, as well as a challenge to researchers. Cochran-Smith et al. (2014) have argued for a philosophical overlay of critical realism with complexity theory to address this issue. They conclude that the combination of complexity theory and critical realism offers a “unique platform for teacher education research, which has theoretical consistency, methodological integrity, and practical significance” (p. 2). Although they propose an intriguing response to this aspect of complexity theory, a dearth of theoretical work and theoretical application to do with this issue in teacher education remains.

Attempting to embed the concepts of complexity theory in empirical research also raises challenges and questions. The difficulties of using complexity theory to guide research design has led most researchers to apply it retrospectively during analysis rather than to use it prospectively to guide methodological decisions (Cochran-Smith et al., 2014). Retrospective analyses have provided some helpful and interesting descriptions of teacher education, professional development, and professional learning. However, looking at this research with a critical eye, one could conceivably ask: How would the findings have differed if complexity theory was not used? (Morrison, 2008). Some studies appear to use traditional methods and analysis and then simply end with claims that the phenomena under study were complex systems and therefore operated according to complex dynamics. The lack of studies that utilize complexity theory in a substantive way points to the challenges of embedding complexity tenets into empirical work.

Ell et al. (2017) are one of the few research teams that have used complexity theory prospectively, and they discussed the methodological challenges they faced. For example, it proved difficult to design a system-mapping task that allowed participants to document simultaneous influences and represent their perspective on a teacher education program in a complex, holistic way. The researchers indicated that they “found it impossible to ‘take the participants with us’ and explain enough about what we were trying to do for them to be able to make a freely drawn map” (pp. 341–342). As a result, participants worked from a map template and predetermined categories, which constrained the maps they created and limited the conclusions that could be drawn.

Ell et al. (2017) also indicated that while they attempted to use complexity to inform their analysis, “each traditional way of considering the data appeared to contravene complexity principles” (p. 334). Aggregating data—either quantitatively by calculating means or qualitatively by identifying themes—is useful for recognizing patterns, but it reduces the visibility of the outliers that complexity theory claims are important to understanding systems. However, as Cilliers (2005) argues, “knowledge and data-reduction are intertwined” (p. 607), so it is challenging to conceptualize how to apply complexity theory to teacher education research while also distilling findings so they are meaningful. An inherent tension exists between looking for patterns in the data and attending to unique contributions, yet complexity theory suggests that researchers must simultaneously consider both.

Future Directions

Emerging complexity theory conceptualizations provide a solid foundation for thoughtful consideration of teacher education research and practices. But, as noted, important challenges remain. Building from current work and continuing to address issues of how to mesh the perspectives of complexity theory with the normative requirements found in teacher education will be crucial. Importantly, continued empirical work can help to illuminate ways in which conceptual challenges can be addressed. This work, while filled with the potential to deepen understandings in teacher education, has significant implications for researchers. They will have to contend with few conceptual underpinnings and little direction on how to establish prospective methods, identify data analysis processes, and bound studies in ways that provide new and explanatory information for teacher education but remain true to principles of complexity theory. If complexity theory is to fulfill its promise of new ways to understand and investigate important issues in teacher education, however, it is important that the momentum initiated by recent work continues to build despite these challenges.

At this point, studies applying complexity theory to teacher education are few in number and limited in scope. Gaps abound in the findings. Although we have growing understandings of particular systems and arrays of systems, this work needs to be deepened and extended. An important aspect of this work will be to explore the nature of the connections within arrays of systems and how influences of particular systems are diminished or enhanced. Likewise, continued research using Holland’s (1995) notion of levers can provide insight into whether/how particular content or pedagogies in teacher education might serve as levers in teaching and teacher learning systems. Additionally, if teacher education researchers hope to understand how teacher education can make a difference for teachers and students, further investigation of those connections between teacher education, teacher learning, and teaching systems as they intertwine with students’ learning systems amid complex classroom interactions is vital. In particular, further investigation of teachers’ operative systems—how knowledge, skills, and attitudes are instantiated as building blocks in operative systems and why and when specific building blocks are activated in practice—appears to be important to this effort. Investigation of this array of systems from a complexity theory perspective has potentially much to offer current conversations in teacher education about the links between teacher education and teacher practice and those between teacher practices and student learning.

Further Reading

Alhadeff-Jones, M. (2008). Three generations of complexity theories: Nuances and ambiguities. Educational Philosophy and Theory, 40(1), 66–82.Find this resource:

    Cochran-Smith, M., Ell, F., Grudnoff, L., Ludlow, L., Haigh, M., & Hill, M. (2014). When complexity theory meets critical realism: A platform for research on initial teacher education. Teacher Education Quarterly, 41(1), 105–122.Find this resource:

      Cochran-Smith, M., Ell, F., Ludlow, L., Grudnoff, L., & Aitken, G. (2014). The challenge and promise of complexity theory for teacher education research. Teachers College Record, 116(5), 1–38.Find this resource:

        Davis, B., & Simmt, E. (2003). Understanding learning systems: Mathematics education and complexity science. Journal for Research in Mathematics Education, 34(2), 137–167.Find this resource:

          Davis, B., & Sumara, D. (2006). Complexity and education. Mahwah, NJ: Lawrence Erlbaum.Find this resource:

            Dotger, S., & McQuitty, V. (2014). Describing elementary teachers’ operative systems: A case study. Elementary School Journal, 115(1), 73–96.Find this resource:

              Ell, F., Haigh, M., Cochran-Smith, M., Grudnoff, L., Ludlow, L., & Hill, M. (2017). Mapping a complex system: What influences teacher learning during initial teacher education? Asia Pacific Journal of Education, 45(4), 327–345.Find this resource:

                Holland, J. H. (1995). Hidden order: How adaptation builds complexity. New York: Helix Books.Find this resource:

                  Kuhn, L. (2008). Complexity and educational research: A critical reflection. Educational Philosophy and Theory, 40(1), 177–189.Find this resource:

                    Martin, S. D., & Dismuke, S. (2018). Investigating differences in teacher practices through a complexity theory lens: The influence of teacher education. Journal of Teacher Education, 69(1), 22–39.Find this resource:

                      McQuitty, V. (2012). Emerging possibilities: A complex account of learning to teach writing. Research in the Teaching of English, 46(4), 358–389.Find this resource:

                        Opfer, V. D., & Pedder, D. (2011). Conceptualizing teacher professional learning. Review of Educational Research, 81(3), 376–407.Find this resource:

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