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Abstract :"Immigrants entering the European Community face a range of challenges in adapting to and understanding the culture of their host nation. Failure to address these challenges can lead to isolation and difficulties integrating into the society of the host country, leading to fragmented communities and a range of social issues . As part of a comprehensive suit e of services for immigrants , the European - funded M obile Assistance for Social Inclusion and Empowerment of Immigrants with Persuasive Learning Technologies and Social Network Services (MASELTOV) project seeks to provide both practical tools and le arning services via mobile devices, providing a readily usable resource for immigrants . In this workshop paper, the game - based learning aspect of the MASELTOV project is introduced, with the rationale behind its design presented. In doing so, the b enefits and implications of mobile platforms and emergent dat a capture techniques for game - based learning are discussed, as are methods for putting engaging gameplay at the forefront of the experience whilst relying on rich data capture and analysis to pro vide an effective learning solution. Through comparison to several other projects, a number of recommendations are put forward for games deployed in contexts similar to that of MASELTOV: a focus on establishing a significant audience with which to conduct ethical research into efficacy, the need for robust pedagogical frameworks suited to the learning context, and the evolution of methods for data capture and analysis of player activity."

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."

Abstract: "In this paper, we describe a methodology for capturing player experience while interacting with a game and we present a data-driven approach for modeling this interaction. We believe the best way to adapt games to a specic player is to use quantitative models of player ex- perience derived from the in-game interaction. Therefore, we rely on crowd-sourced data collected about game context, players behavior and players self-reports of dierent aective states. Based on this informa- tion, we construct estimators of player experience using neuroevolution- ary preference learning. We present the experimental setup and the re- sults obtained from a recent case study where accurate estimators were constructed based on information collected from players playing a rst- person shooter game. The framework presented is part of a bigger picture where the generated models are utilized to tailor content generation to particular player's needs and playing characteristics."

Abstract: "Obesity has become an outstanding public health issue in most countries around the world. Many attempts have been made to address this issue that ranges from taking medication to doing exercise to follow- ing a diet plan to playing games. Few approaches combine exercise and game to engage the obese people in playing fun-based games or pur- poseful games, also known as serious games, while monitoring their bio- signals. However, existing work hardly provides a configurable, scalable and context-aware serious game framework that can be used as a sup- port for obesity treatment. In this paper, we take an attempt to propose such a framework. The proposed framework facilitates bio-signal monitor- ing based on body sensor network, context-awareness based on perva- sive sensors, and on-the-spot activity recommendation based on current game-playing context. It uses the cloud computing platform as infrastruc- tural support that ensures the scalability of the framework. In order to demonstrate the suitability of the proposed framework, we developed a sample serious game; deploy it over a cloud platform; and experiment with it by capturing some psycho-physical data while the obese are en- gaged in game-play. We observed that the obese people were very much engaged in game-play and they had positive experience using the system"

"Without achieving a clear understanding of the learning domain, it is difficult to develop a successful serious game that enables users to achieve the desired learning outcomes. Thus, the first step in serious game design is to establish an understanding of the particular learning domain, usually through consultation with domain experts. Whilst game design is inherently a creative process, we believe the capturing of the knowledge domain can be systematised and we present a structured approach to knowledge elicitation and representation as a basis for serious game design. We have adapted and extended the applied cognitive task analysis (ACTA) method and have combined it with additional knowledge representation frameworks. We explain how the outputs of this approach can inform the game mechanic and the development of non-player characters, and apply it to the design of a serious game aimed at reducing time-tocompetence in soft project management skills for professionals working in corporate environments. A total of 26 domain experts from five different countries were involved in a two-stage interview process. The interviews yielded more than 300 task elements, and information about the cognition underlying the more challenging tasks. This data was incorporated into several representation frameworks and used to indicate features to be implemented in the game and the game mechanics of the supported features."