Using motivation derived from computer gaming in the context of computer based instruction

This paper was originally presented at the IEEE Technically Sponsored SAI Computing Conference 2016, London, 13-15 July 2016. Abstract— this paper explores how to exploit game based motivation as a way to promote engagement in computer-based instruction, and in particular in online learning interaction. The paper explores the human psychology of gaming and how this can be applied to learning, the computer mechanics of media presentation, affordances and possibilities, and the emerging interaction of playing games and how this itself can provide a pedagogical scaffolding to learning. In doing so the paper focuses on four aspects of Game Based Motivation and how it may be used; (i) the game player’s perception; (ii) the game designers’ model of how to motivate; (iii) team aspects and social interaction as a motivating factor; (iv) psychological models of motivation. This includes the increasing social nature of computer interaction. The paper concludes with a manifesto for exploiting game based motivation in learning

Player agency in interactive narrative: audience, actor & author

The question motivating this review paper is, how can computer-based interactive narrative be used as a constructivist learn- ing activity? The paper proposes that player agency can be used to link interactive narrative to learner agency in constructivist theory, and to classify approaches to interactive narrative. The traditional question driving research in interactive narrative is, ‘how can an in- teractive narrative deal with a high degree of player agency, while maintaining a coherent and well-formed narrative?’ This question derives from an Aristotelian approach to interactive narrative that, as the question shows, is inherently antagonistic to player agency. Within this approach, player agency must be restricted and manip- ulated to maintain the narrative. Two alternative approaches based on Brecht’s Epic Theatre and Boal’s Theatre of the Oppressed are reviewed. If a Boalian approach to interactive narrative is taken the conflict between narrative and player agency dissolves. The question that emerges from this approach is quite different from the traditional question above, and presents a more useful approach to applying in- teractive narrative as a constructivist learning activity

Attack on the clones: managing player perceptions of visual variety and believability in video game crowds

Crowds of non-player characters are increasingly common in contemporary video games. It is often the case that individual models are re-used, lowering visual variety in the crowd and potentially affecting realism and believability. This paper explores a number of approaches to increase visual diversity in large game crowds, and discusses a procedural solution for generating diverse non-player character models. This is evaluated using mixed methods, including a “clone spotting” activity and measurement of impact on computational overheads, in order to present a multi-faceted and adjustable solution to increase believability and variety in video game crowds

Exploring the cooperative regimes in a model of agents without memory or "tags": indirect reciprocity vs. selfish incentives

The self-organization in cooperative regimes in a simple mean-field version of a model based on "selfish" agents which play the Prisoner's Dilemma (PD) game is studied. The agents have no memory and use strategies not based on direct reciprocity nor 'tags'. Two variables are assigned to each agent $i$ at time $t$, measuring its capital $C(i;t)$ and its probability of cooperation $p(i;t)$. At each time step $t$ a pair of agents interact by playing the PD game. These 2 agents update their probability of cooperation $p(i)$ as follows: they compare the profits they made in this interaction $\delta C(i;t)$ with an estimator $\epsilon(i;t)$ and, if $\delta C(i;t) \ge \epsilon(i;t)$, agent $i$ increases its $p(i;t)$ while if $\delta C(i;t) < \epsilon(i;t)$ the agent decreases $p(i;t)$. The 4!=24 different cases produced by permuting the four Prisoner's Dilemma canonical payoffs 3, 0, 1, and 5 - corresponding,respectively, to $R$ (reward), $S$ (sucker's payoff), $T$ (temptation to defect) and $P$ (punishment) - are analyzed. It turns out that for all these 24 possibilities, after a transient,the system self-organizes into a stationary state with average equilibrium probability of cooperation $\bar{p}_\infty$ = constant $> 0$.Depending on the payoff matrix, there are different equilibrium states characterized by their average probability of cooperation and average equilibrium per-capita-income ($\bar{p}_\infty,\bar{\delta C}_\infty$).Comment: 11 pages, 5 figure

Cooperation and Self-Regulation in a Model of Agents Playing Different Games

A simple model for cooperation between "selfish" agents, which play an extended version of the Prisoner's Dilemma(PD) game, in which they use arbitrary payoffs, is presented and studied. A continuous variable, representing the probability of cooperation, $p_k(t) \in$ [0,1], is assigned to each agent $k$ at time $t$. At each time step $t$ a pair of agents, chosen at random, interact by playing the game. The players update their $p_k(t)$ using a criteria based on the comparison of their utilities with the simplest estimate for expected income. The agents have no memory and use strategies not based on direct reciprocity nor 'tags'. Depending on the payoff matrix, the systems self-organizes - after a transient - into stationary states characterized by their average probability of cooperation $\bar{p}_{eq}$ and average equilibrium per-capita-income $\bar{p}_{eq},\bar{U}_\infty$. It turns out that the model exhibit some results that contradict the intuition. In particular, some games which - {\it a priory}- seems to favor defection most, may produce a relatively high degree of cooperation. Conversely, other games, which one would bet that lead to maximum cooperation, indeed are not the optimal for producing cooperation.Comment: 11 pages, 3 figures, keybords: Complex adaptive systems, Agent-based models, Social system

Evaluation of a Pervasive Game for Domestic Energy Engagement Among Teenagers

In this article, we present Power Agent—a pervasive game designed to encourage teenagers and their families to reduce energy consumption in the home. The ideas behind this mobile phonebased game are twofold; to transform the home environment and its devices into a learning arena for hands-on experience with electricity usage and to promote engagement via a team competition scheme. We report on the game’s evaluation with six teenagers and their families who played the game for ten days in two cities in Sweden. Data collection consisted of home energy measurements before, during, and after a game trial, in addition to interviews with participants at the end of the evaluation. The results suggest that the game concept was highly efficient in motivating and engaging the players and their families to change their daily energy-consumption patterns during the game trial. Although the evaluation does not permit any conclusions as to whether the game had any postgame effects on behavior, we can conclude that the pervasive persuasive game approach appears to be highly promising in regard to energy conservation and similar fields or issues