Conceptualizing Motivation as a Dynamic System

• In Event: 66.069 - Motivation and Complexity: The Merits of a Dynamic Systems Approach to Theory and Research on Motivation in Education

Abstract

Objectives

The purpose of this presentation is to provide a theoretical frame for the symposium—the three presentations, critical comments, and conversation to follow—by introducing the complex dynamic systems (DS) approach and its conceptual application to motivation theory and research.

Theoretical framework

I will begin by highlighting the growing recognition that motivation is complex, dynamic, and non-linear, and that it “transcends…the traditional false dichotomies between stable-changeable, rationale-irrational, consistent-inconsistent, conscious-unconscious, controlled-automatic, and agentic-routinized descriptions of the individual” (Pintrich, 2003, p. 680). I will follow to introduce complexity science, and specifically the DS approach, as a scientific paradigm that allows to conceptualize, model, and investigate such complex phenomena. I will then provide an overview of several critical tenets of the DS approach that distinguish it from the experimental paradigm in psychological research: (a) Dynamism—the continuous self-emergence of the complex system that calls for a simultaneous focus on content, structure, and process; (b) Nonlinearity and non-determinism—the uncertainty involved in non-linear and chaotic processes that characterizes the complex system and that calls for changing expectations regarding prediction and control; (c) Irreducibility—the interdependence among the complex system’s components that call for a focus on the system as the primary unit-of-analysis; (d) Contextualism—the embedded and open nature of the complex system within and along other systems that call for considering multi-dimensional bottom-up and top-down reciprocal contextual influences on the complex system. I will then point to several assumptions held by contemporary theorists about motivational constructs such as self-efficacy, task value, and achievement goals that correspond with the DS approach; but, also to the discrepancy between contemporary motivational research and these assumptions. I will note several central features of dynamic systems (e.g., attractor states, critical points, turbulence) and highlight their use in addressing motivational phenomena such as anomalous variance, increase in variability, and critical slowing down of processes that are hard to explain with the currently dominant research approach. I will end by pointing to challenges in the application of the DS approach to coherent and parsimonious theory, the collection of empirical quantitative and qualitative data reflecting complex dynamic systems, complexity modeling, and the bridging of expectations and goals of researchers, practitioners, and policy-makers.

Scholarly significance

The DS approach provides alternative epistemological assumptions to those that govern most contemporary motivational research. Emerging findings from other social-science disciplines suggest that utilizing the DS approach to conceptualize phenomena reflects better the nature of these phenomena and increases explanatory power (Guastello et al., 2009). Yet, the merits of adopting the DS approach to the field of motivation have not been critically considered. This presentation will provide the conceptual tenets for a focused consideration and debate.

Author

• Avi Kaplan, Temple University