Kiefer's "Complexity, Class Dynamics, and Distance Learning"

Kiefer, Kate. “Complexity, Class Dynamics, and Distance Learning.” Computers and Composition 23.1 (2006): 125–38

Having taught anomalously dysfunctional groups of students from time to time, both face-to-face and online, Kiefer wants to understand what causes the widely varying dynamics we see between one (writing) class and another taught by the same instructor.  For answers, she turns to complexity theory, which she uses not only for insight but also for perspective on the teacher’s self-concept.  Complexity theory applies to a system that is complex and adaptive.  A complex system is one that cannot be explained by cataloging its parts and their functions (as a complicated system might be).  An adaptive system is one that changes in response to external or internal stimuli.  The key to a complex system is that it is neither stable nor predictable, but that its functioning does follow some (possibly invisible) logic from which patterns emerge.  Kiefer uses traffic patterns as an example; a wreck or malfunctioning traffic light might not be predictable, but once the complex system adapted to the event, a new pattern would emerge, and that pattern would be caused by many individual decisions (e.g., “I’ll take the surface road instead,” “I’ll change the order of my errands.”).  Key to a complex system is the fact that no individual can maintain control; its behavior is determined by many separate actions or decisions.

Kiefer argues that class dynamics represent a complex system because many individual decisions and efforts make for a pattern of behavior that cannot be predicted but is nonetheless recognizable.  She uses many anecdotes from her teaching career as examples, with an informal case study of one online class offering the greatest insights for the distance instructor.  Among the student behaviors the Kiefer outlines as influencers of the complex system are resistance to course activities, banding together of like-minded students, early posting in message boards, and substantive feedback in message boards.  While as a researcher she focuses on observations of these behaviors and recognition of their relevance to complexity theory, her article brings to mind decisions a practitioner might make.  For instance, students who posted early in the window for message board posts got significantly more feedback and engagement from classmates, which likely provided motivation to engage even more fully in the class, while late posters got little feedback and continued to be minimally engaged; it seems that Warnock’s suggestion of using a two-stage message board assignment might guide more students to have an experience similar to Kiefer’s early posters and therefore allow the instructor to influence the complex system even though she cannot control it. 

Similarly, modeling has potential to influence individual decisions and, therefore, class dynamics.  Kiefer describes the change that occurs when students post non-substantive comments in a message board, then others post substantive comments—comments posted thereafter tended to be more substantive.  Kiefer suggests using teacher feedback to model appropriate interactions, but there may be other ways to influence student-student interactions, such as directing students to a particularly substantive post and discussing its salient features (after obtaining the poster’s permission).  In short, complexity theory brings to light ways that  instructors can find “potential opportunities to influence the emergent dynamic of a class” (137), whether face-to-face or online.