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Top1. Introduction: The Motivation
The objective of this paper is how to understand, visualize and model human social and especially gaming behavior. Findings could be applied also to climate protection which is understood as a long-term collective global learning procedure.
Such a modeling strategy, namely to view the gaming aspect (Ahamer, 2013a) of life and of learning, seeks to improve game based learning design, while it is also used for gaming as such, e.g. for multi-agent systems (MAS) as common to online games.
The motivation of this paper is to understand and visualize important traits of both e-learning and online gaming, more specifically how fundamental dimensions of social behavior are intertwined, interdependent and interacting.
For the purpose of studying real behavior of humans, this paper uses the case of a web-supported university lecture structured along the role-game “Surfing Global Change” (SGC) that was designed, implemented and copyrighted by the author earlier (Ahamer, 2004a; 2004b; 2005; 2006) and might offer some generalized insights for game developers. Some readers might wish to apply the findings later on also to multi-agent system research (Dignum et al., 2009) as well as to situations similar to the ones occurring in MMOGs (Massively Multiplayer Online Games). Typically, multi-agent platforms assume autonomy of the agents (Baumgarten et al., 2009), similar to students who also behave autonomously in a classroom lecture during game-based learning. In MAS, the communication facilities play a crucial role (Dignum et al., 2009, p. 3).
According to earlier experience, important criteria for successful learning and gaming can be (1) self-adaptivity of (learning and gaming) processes, (2) suitable fluctuation of framework conditions and (3) the underdetermined (learning and gaming) paths along which participants move through the “space of possibilities and options”. The above quality criteria will be briefly discussed below and add to the three characteristics that make learning fun cited by Wang & Wu (2009), taken from Malone (1980), namely (i) an appropriate level of challenge, (ii) using imagination and abstractions and (iii) tickling the player’s curiosity. A classical in-depth book in the field is Mark Prensky’s (2001) “Digital Game-Based Learning”, which is still worth reading.