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Abstract: "Player metrics are an invaluable resource for game designers and QA analysts who wish to understand players, monitor and improve game play, and test design hypotheses. Usually such metrics are collected in a straightforward manner by passively recording players; however, such an approach has several potential drawbacks. First, passive recording might fail to record metrics which correspond to an infrequent player behavior. Secondly, passive recording can be a costly, laborious, and memory intensive process, even with the aid of tools. In this paper, we explore the potential for an active approach to player metric collection which strives to collect data more efficiently, and thus with less cost. We use an online, iterative approach which models the relationship between player metrics and in-game situations probabilistically using a Markov Decision Process (MDP) and solves it for the best game configurations to run. To analyze the benefits and limitations of this approach, we implemented a system, called GAMELAB, for recording player metrics in Second Life."

"This article concerns the design of self-contained digital games for the life-long learning context. Although the potential of games for teaching and learning is undisputed, two main barriers hamper its wide introduction. First, the design of such games tends to be complex, laborious and costly. Second, the requirements for a sensible game do not necessarily coincide with the requirements for effective learning. To solve this problem, we propose a methodology to the design of learning games by using game design patterns and matching these with corresponding learning functions, which is expected to reduce design effort and help determining the right balance between game elements and learning. First empirical results indicate that such a methodology actually can work."