Title Learning innovation diffusion as complex adaptive systems through model building , simulation , game play and reflections

To effectively foster innovation diffusion, school leaders need to learn innovation diffusion as Complex Adaptive Systems (CAS). In this study, two school leaders formed a dyad to learn both the knowledge about innovation diffusion and the knowledge in fostering innovation diffusion. Agent-based model building, model simulation, game play of a simulation game and reflections were designed as learning activities in this study. In the learning process, the learners developed the following understanding in innovation diffusion: teachers’ adoption decisions are based on limited rationality and local information; teachers have nonlinear influence on each other through social networks; teachers are heterogonous agents; and diffusion is a process of emergence. The learners also learnt to leverage on social networks to foster effective innovation diffusion. While agent-based model building faces challenges for learning CAS in the social science domain, this study shows that engaging learners in reflection activities helps to overcome the challenges. Innovation Diffusion in Schools: Learning Challenges That School Leaders Face The past decade saw many education innovations, but the diffusion (Rogers, 2003) of such innovations has yet to receive adequate attention in education research (Christensen, Horn, & Johnson, 2008; Surry & Ely, 2001). For school leaders to effectively diffuse innovations, it is essential that they (a) understand innovation diffusion as Complex Adaptive Systems (CAS) (Dooley, 1996; Holland, 1995), and (b) learn how to foster diffusion. Need to learn innovation diffusion as CAS: Rogers, Medina, Rivera and Wiley (2005) conceive innovation diffusion as CAS, a sub-set of Complex Systems (Bar-Yam, 2003). Different from Complex Systems, CAS has multiple semi-autonomous agents (such as human beings) (Holland, 1995) whose decision-making is based on local information and limited rationality (Gigerenzer & Selten, 2002). The agents are constantly evolving and adapting to what the other agents are doing by interacting with other agents (locally) so as to maximize their fitness in the environment (globally). The structures and patterns of a CAS tend to be emergent (Holland, 1995). Huang and Kapur (2007) regard innovation diffusion in school as a result of many teachers (who are seen as semi-autonomous agents in a CAS) interacting with each other in school social networks. As teachers adapt and change (similar to how agents self-organize in a CAS) over time, innovations are re-invented and adopted over time and space in a school. However, there is a strong top-down tradition in innovation diffusion (Niehaves, 2007), which maintains that imposing a top-down mandate in a school for a sufficiently long period will result in sustainable and pervasive adoption of innovations among teachers (Christensen, et al., 2008; Looi, Lim, Koh, & Hung, 2005). This perspective takes a mechanistic view rather than a CAS view on innovation diffusion. Need to learn how to foster innovation diffusion: There are differences between the knowledge about innovation diffusion (“knowledge-about”) and the knowledge in fostering innovation diffusion (“knowledge-indoing”). Using swimming as an example, the “knowledge-about” swimming includes the concept of buoyancy which can be used to describe and explain swimming whereas the “knowledge-in-doing” swimming refers to the personal knowledge that a swimmer has in the act of swimming. The characteristics of the two kinds of knowledge are summarized in Table 1. Table 1: Characteristics of “Knowledge-about” and “Knowledge-in-doing”. Knowledge-about Knowledge-in-doing Knowledge to describe and explain a phenomenon Action dependent knowledge in changing a phenomenon General and abstract Situational and embodied Relatively static Temporal and dynamic Explicit; can be described by the knower Mostly tacit; cannot be totally reduced to mere words, but can be acted out in situations Evidenced by descriptions, artifacts and descriptionoriented actions Evidenced by interpretations of actions and reflections