and answer different types of research questions. The novel contribution of CDST is to explicitly consider multiple, interacting processes occurring within the selected timescale. For example, the trend toward increasingly communicative forms of language teaching has been taking place over many years (Canale & Swain, 1980; Savignon, 2000); nested within such a broad trend will be patterns of talking within specific classrooms (King, 2013), and nested within a specific day in the classroom will be fluctuations in communication for teachers and learners as various activities take place (Mystkowska-Wiertelak & Pawlak, 2017). To probe further the conceptual context, de Bot (2014) recommends thinking about processes one timescale level up and one timescale level down in addition to the timescale of the process under study. For example, in one of the studies described below, we studied anxiety changes over the course of a 20-minute presentation. We found it helpful to think about the sub-processes (cognitive, emotional, physiological) that affect anxiety on a per-second timescale as well as how anxiety arousal during a classroom presentation is itself part of longer-term processes, such as learning during a semester-long course and how anxiety changes in the process of becoming a language teacher. Timescales are nested within each other, as seconds are nested in minutes, minutes within hours, hours within days, and so on. It may be that different dynamics are visible on different timescales, or possibly that patterns repeat across different timescales (see the discussion of fractals below).
Openness
The openness of a system refers to the notion that the system is subject to sources of influence that perhaps were not contemplated when planning a research design. Essentially, openness to unexpected influences increases substantially the difficulty of predicting the most relevant processes or systems to study, especially if considered at the individual level where personal psychological idiosyncrasies abound. In the research examples below, we note that allowing for unexpected influences on a system can help to bring clarity to the dynamics involved, highlighting what is typical and what might be unusual. To provide specific examples, we will highlight how density of measurement and including a qualitative component to research designs allows for the description of unexpected factors.
Predictability, stability and variability
A particular strength of CDST is the focus on variability and change over time. Each state of the system under study is taken to be a modification of the system’s previous state, and the trajectory of change in a system can be highly sensitive to its initial conditions. Given that we cannot know everything about a system and the interactions within it, there will always be some level of unpredictability about the kinds of potential changes that may occur. However, unpredictable does not mean ‘anything goes’ because there is not an infinite number of possible outcomes or states of the system. Some states are more likely to arise than others (see attractor states below). It is important to note the range of outcomes is influenced by initial conditions and the system dynamics meaning that the potential states of a system cannot be absolutely anything at all, but also cannot be precisely defined or predicted exactly in advance. The concept of dynamic stability reflects the potential for a system to remain in a relatively steady state for some period of time. It means there can be minor changes, fluctuations and variations but the overall state of the system remains relatively stable, but not fixed or static. In the research examples below, we show the importance of defining the timescale to focus on variability or stability, as processes may be volatile over short periods of time but relatively stable over longer ones.
Attractor and repeller states
The terms attractor and repeller states come from chemistry where they are not loaded with the connotations that occur when they are applied to human beings. In the vernacular, the term attractor connotes valuable and desirable; the term repeller connotes pushing something away. However, this is not the definition of those terms in CDST and their misuse will be problematic unless researchers are clear that attractor states are those to which a system tends to be drawn whether or not the state is thought to be pleasant and welcome – a long-running feud between rival teachers is an attractor state because it is relatively stable, even if it is also a nasty experience. Similarly, a repeller state may be pleasant or unpleasant, but by definition a system will not remain in that state for very long. The terms merely refer to the preferred state of the system, not whether that preferred state is positive or negative in valence. In the research examples below we take individual difference factors that have been studied using other methods, such as willingness to communicate and language anxiety, to be instances of attractor or repeller states of a system.
Emergence, self-organization and soft assembly
The idea of emergence suggests the state of the system can be considered more than the sum of its multiple, continuously interacting parts. Self-organization suggests that systems are not operating according to a predefined blueprint and do not reflect the inevitable unfolding of a plan. Rather, systems organize themselves and have an intrinsic tendency to display coherent patterns. Soft assembly refers to the idea that the interacting parts of a system can be shared and re-configured into coherent patterns, as systems organize themselves. A learner might describe ‘feeling motivated’ or ‘feeling anxious,’ two emergent states that often share features such as engagement with the learning process, involvement of the self, relationships with other people, a role for the teacher, emotional arousal, salience of learning goals and so on. Yet motivation and anxiety are experienced by individuals as qualitatively different states; generally speaking, motivation is pleasant and anxiety is not, motivation favours approach but anxiety suggests avoidance. The concept of emergence has been influential in connecting the dynamics of process to established research on familiar topics previously studied from other perspectives. In the examples below, we offer suggestions about how emergent states (being willing to communicate, experiencing anxiety, feeling motivation) make sense when considered as coherent, organized states dynamically assembled through the interactions of associated systems.
Fractals
Fractalization refers to the characteristic of systems to display self-similar patterns across levels. This means that the behaviour of a system on one timescale or level can potentially predict similar behaviour on different levels. Similarly, if patterns are found across timescales or levels, this can serve as evidence for a complex dynamic system. This means similar attractor/repeller states and similar types of dynamics can be manifested across system levels. For example, Mercer (2015) found that the self system of her learners exhibited comparable kinds of dynamics across different timescales of minutes, hours, weeks and months. The findings implied patterning in the types of system dynamics and system states when examined on different timescales. Fractals reveal that there can be surprising patterns and regularities in what may at first appear as random, chaotic systems, which, of course, can have important implications for research. In the research examples below, we are starting to see that some of the processes that occur on longer timescales also appear on shorter ones (see in particular descriptions of research into willingness to communicate and the self).
Research Questions from Standard and Complexity Approaches
Understanding these characteristics of a complex dynamic system has implications for how we formulate research questions, and even what is the focus of research. To illustrate the nature of research from a CDST perspective, we will outline typical characteristics and forms of quantitative and qualitative studies and show how they can be reconfigured and reconceptualized to better reflect CDST characteristics.
Quantitative compared with CDST approaches
The difference between a CDST approach to research and a traditional quantitative approach is substantial. CDST approaches can use quantitative data, and a number of data analytic techniques have been initiated (see Hiver & Al-Hoorie, 2020). But quantitative data analysis must be guided by research questions. Not only do the research questions themselves change, but the nature of the answers change as well. To clarify, Table 2.1 offers three examples of research questions that have been studied from a quantitative perspective, and a dynamic re-phrasing of questions in the same domain.
| Table 2.1 Quantitatively-oriented research questions compared with CDST variations | |
| Standard questions | CDST re-phrasing |
| Does language
| |