have been studied extensively from traditional quantitative and qualitative perspectives. Yet, there is something new about the CDST account that foregrounds interactions among concepts and provides novel insights into the processes underlying familiar concepts.
Insights for Researching Language Learning Psychology from a CDST Perspective
The above illustrative studies and their research methods have presented a number of challenges. Real time data are messy, dense and can be difficult to summarize without losing the nature of the dynamics. Issues that arose were addressed sometimes by blending data sets and forms of analysis at both the group and individual level, with emphasis on the latter. We have also experienced challenges when writing about CDST and the need to use language that can be conceived as jargonistic to those unfamiliar with the notions and concepts underlying CDST as well as the need to explain extensively the theory before getting to the study itself.
Yet, despite the challenges, the benefit of a CDST perspective in the above studies was that it allowed us to offer a different interpretation of what was happening than traditional methods would allow. We feel there are five main types of insights such work can afford:
(1)Exploring different timescales/levels of granularity: In a complex dynamic system, there are many ways of defining a system and it depends where a researcher chooses to set the boundaries and focus. A guiding principle for deciding what is being defined as a system is to consider what functions as a whole (Larsen-Freeman & Cameron, 2008). This can then be considered in terms of levels of granularity and the related timescales of dynamism. By starting with the explicit recognition of the timescale under study, we are able to set limits on the processes under consideration, providing focus for the studies. The idiodynamic studies reported above specified a brief timescale measured in seconds and minutes, the ESM study deals with a timescale of days in a week. But as the notion of fractals suggests, these specific timescales are nested within other timescales, as the study of the self demonstrates. Patterns observed on one timescale can sometimes be found at others. The challenge becomes how to combine research examining systems at various levels of granularity and across timescales.
(2)Investigating various forms of dynamism and stability: The focus of this chapter has been on how systems change or remain stable over time. Dynamism does not have to mean change from one state to another; it can mean fluctuations within a relatively stable state. Dynamic stability can be observed as changes in processes on lower timescales may not have noticeable effects on higher levels of granularity and across longer timescales. Systems may show homeostasis. The challenge is how to view different degrees and types of dynamism and stability and how these may interact across levels and timescales.
(3)Focusing on ongoing processes: When examining dynamics, we have stressed that single data collection points may establish a snapshot of the state of a system for the participants in the research, but the research design does not necessarily reveal processes in action. Research must be designed from the start to examine the actual processes of change as they happen and this necessitates specific research designs with dense data collected at appropriate points in time.
(4)Examining uniqueness AND commonalities: A CDST perspective does not imply that scholars ignore patterns across systems or parts of systems, there is much to be learned from examining how findings may be similar across individuals and units of analysis. However, CDST does foreground strongly the benefits to be gained from looking at outliers, the unusual, the unique and the unexpected. These reflect the core characteristics of a complex dynamic system.
(5)Taking holistic perspectives and conceiving of open systems: The psychology of language learning has defined, differentiated and measured a number of specific concepts such as motivation, anxiety and WTC. However, as we observe them in operation in real time, many theoretical distinctions melt away. A CDST approach requires putting concepts such as motivation, anxiety and WTC in motion, describing how they move together, and what constitutes a meaningful system. The notion of open systems provides a lesson in contrasting the group level of data analysis with individual level. Traditional quantitative methods require defining the concept in the study in advance and measuring them with appropriate instruments. This has the effect of closing the potential influences on the system to only those defined in advance. By allowing for open systems, and gathering qualitative data repeatedly to assess how the processes interact over time, we are able to identify influences from the perspective of a language speaker or teacher that would have been missed otherwise. This allows for a richer description than otherwise would be possible.
Conclusion
In this chapter, we have reflected on our understandings of what researching language learning psychology from a CDST perspective implies. We have considered how the kinds of questions, use of theoretical frameworks and constructs as well as methodologies and forms of analysis must adjust to accommodate the specific characteristics of a complex dynamic system and its commensurate meta-theory. We have focused in particular on the dynamics of such systems and looked at several illustrations of research focused on dynamics, how such studies need to be conducted, the challenges posed by such work and the fresh insights they can generate. We hope to have inspired others who may wish to research from this perspective and examine dynamics specifically. We have shown how challenges can be creatively met and how the insights gained from such work can push forward our understandings to challenge our established and conventional views of constructs and their interrelationships. A CDST perspective is still in its relative infancy in the field and its merit for language learning psychology will be tested out over time. As a theory, it must offer something new and worthwhile, making a substantial contribution of growth to the field; otherwise, it becomes merely another short-lived academic fashion or fad. We feel this perspective offers rich potential, especially for the field of language learning psychology. As we have argued in this chapter and as we believe our work to date has shown, CDST is an innovative lens through which to reflect on the complex and dynamic nature of human psychology and its collective as well as individual characteristics. We feel exciting research times lie ahead and the challenges inherent in such work will be met when scholars work together with a spirit of innovation, criticality and openness to new ways of thinking and researching.
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