The iLearnRW game : support for students with dyslexia in class and at home

Dyslexia includes a large variety of literacy-related difficulties which demands, in most cases, a personalised intervention. However, as dyslexia affects a large fraction of the population, schools cannot provide individual care for each student. The iLearnRW game provides a tool for students to work on their literacy skills following a personalised teaching programme. The design of the game and adaptation mechanisms integrated with it are aimed at maintaining student engagement for the duration of an open-ended number of playing sessions, while using a limited quantity of assets and literacy content. By focusing on maintenance of engagement, we hope to improve learning outcomes and motivate students to also play the game outside of school.The authors would like to thank all the participants of the experiments.This research was supported, in part, by the ILearnRW (project no: 318803) FP7 ICT EU project.peer-reviewe

Gender Microaggression and Macroaggression Experiences of Women Athletic Trainers

Purpose: Although gender disparities and workplace vitality for women has been studied in athletic training, gender discrimination has not been widely studied. The purpose of this study was to describe gender-based aggressions experienced by women athletic trainers (ATs) within their educational and workplace environments. Methods: We used a cross-sectional design, and the web-based survey was comprised of demographic questions, the Schedule of Sexism Events (SSE), and questions on incident reporting. The SSE asks participant to rank items on a Likert Scale (1 = the event never happened to 6 = the event happens almost all the time). Items within the SSE are contextualized to either events in the past year or events in their entire life. The survey was distributed to 5,667 women ATs through the National Athletic Training Association (NATA). Five hundred thirty-nine (539) participants accessed the survey (9.5%). Four hundred seventy-eight (478) participants (age=34.2±8.6y [range=23–66y], experience=11.0±8.2y [range=0-40y]) completed the entire instrument (88.6%). We used descriptive statistics to analyze demographic variables, gender-based education- and work- related items of the SSE and incident reporting. Results: When asked about unfair treatment while interacting with teachers, professors, or engaging in academics in their lifetime, 89% (n=425) of participants indicated they had experienced unfair treatment. Comparatively, 53% (n=252) of participants experienced unfair treatment from teachers, or professors, or while engaging in academics in the last year. When asked about unfair treatment by an employer, boss, or supervisor, 88% (n=421) of participants experienced unfair treatment in their lifetime, where 55% (n=267) have experienced unfair treatment in the past year. Ninety percent (90%, n=430) indicated they experienced unfair treatment by co-workers, fellow students, or colleagues, in their lifetime compared to 61% (n=292) in the past year. When asked if they had experienced gender-based macroaggressions and microaggressions in the workplace, 41% (n=198) experienced both types of aggressions; 5% (n=22) experienced macroaggressions and 29% (n=137) experienced microaggressions. Only 25% (n=119) of participants have reported aggressions in the workplace. Conclusion: Women ATs experience gender-based aggressions in the workplace but they do not typically report these aggressions. All ATs have the responsibility to work towards an inclusive, equitable, and welcoming workplace that directly addresses aggressions

Extending neuro-evolutionary preference learning through player modelling

In this paper we propose a methodology for improving the accuracy of models that predict self-reported player pairwise preferences. Our approach extends neuro-evolutionary preference learning by embedding a player modeling module for the prediction of player preferences. Player types are identified using self-organization and feed the preference learner. Our experiments on a dataset derived from a game survey of subjects playing a 3D prey/predator game demonstrate that the player model-driven preference learning approach proposed improves the performance of preference learning significantly and shows promise for the construction of more accurate cognitive and affective models.peer-reviewe

Adaptive game level creation through rank-based interactive evolution

This paper introduces Rank-based Interactive Evolution (RIE) which is an alternative to interactive evolution driven by computational models of user preferences to generate personalized content. In RIE, the computational models are adapted to the preferences of users which, in turn, are used as fitness functions for the optimization of the generated content. The preference models are built via ranking-based preference learning, while the content is generated via evolutionary search. The proposed method is evaluated on the creation of strategy game maps, and its performance is tested using artificial agents. Results suggest that RIE is both faster and more robust than standard interactive evolution and outperforms other state-of-the-art interactive evolution approaches.The research is supported, in part, by the FP7 ICT project SIREN (project no: 258453) and by the FP7 ICT project C2Learn (project no: 318480).peer-reviewe

Space maze : experience-driven game camera control

Virtual camera control is a key factor in game experience be- cause the camera dictates how players see the game world. As the complexity and unpredictability of games increases, automatic camera control becomes a fundamental require- ment. In this paper, we present a game technology demon- strator that showcases automatic camera control capable of creating dissimilar experiences within a 3D prey/predator game. An adaptation algorithm informed by predictors of subjective experiences adjusts the behavior of the camera to influence the experience of the player throughout the game.This research was supported, in part, by the ILearnRW (project no: 318803) and the C2Learn (project no. 318480) FP7 ICT EU projects.peer-reviewe

Procedural generation of music-guided weapons

Beyond the standard use of music as a passive and, sometimes, optional component of player experience the impact of music as a guide for the procedural generation of game content has not been explored yet. Being a core elicitor of player experience music can be used to drive the generation of personalized game content for a particular musical theme, song or sound effect being played during the game. In this paper we introduce a proof-of-concept game demonstrator exploring the relationship between music and visual game content across different playing behaviors and styles. For that purpose, we created a side-scroller shooter game where players can affect the relationship between projectiles’ trajectories and the background music through interactive evolution. By coupling neuroevolution of augmented topologies with interactive evolution we are able to create an initial arsenal of innovative weapons. Those weapons are both interesting to play with and also create novel fusions of visual and musical aesthetics.Thanks to Ryan Abela for his input on designing the sound extraction methods. The research was supported, in part, by the FP7 Marie Curie CIG project AutoGameDesign (project no: 630665).peer-reviewe

A computational approach towards conflict resolution for serious games

Conflict is an unavoidable feature of life, but the development of conflict resolution management skills can facilitate the parties involved in resolving their conflicts in a positive manner. The goal of our research is to develop a serious game in which children may experiment with conflict resolution strategies and learn how to work towards positive conflict outcomes. While serious games related to conflict exist at present, our work represents the first attempt to teach conflict resolution skills through a game in a manner informed by sociological and psychological theories of conflict and current best practice for conflict resolution. In this paper, we present a computational approach to conflict generation and resolution. We describe the five phases involved in our conflict modeling process: conflict situation creation, conflict detection, player modeling and conflict strategy prediction, conflict management, and conflict resolution, and discuss the three major elements of our player model: assertiveness, cooperativeness, and relationship. Finally, we overview a simple resource management game we have developed in which we have begun experimenting with our conflict model concepts.peer-reviewe

Saint or Sinner?: A Reconsideration of the Career of Prince Alexandre de Merode, Chair of the International Olympic Committee’s Medical Commission, 1967-2002

This article explores the role of Prince Alexandre de Merode in heading the IOC’s fight against drugs from the 1960s to 2002. History has not served de Merode very well. He has been presented in simplistic ways that emerge from context rather than evidence – as either a saint or a sinner. IOC-sanctioned accounts cast him in the mould of the saint: a moral and intelligent man who saved sports from doping. In contrast, sports academics have tended to portray him as a sinner: an ineffectual leader who did not develop either the testing systems or the punishments required to prevent doping and who deliberately concealed evidence of high-profile doping cases. This article assesses both representations before presenting information to support a richer and more complicated interpretation

A computational approach towards conflict resolution for serious games

Conflict is an unavoidable feature of life, but the development of conflict resolution management skills can facilitate the parties involved in resolving their conflicts in a positive manner. The goal of our research is to develop a serious game in which children may experiment with conflict resolution strategies and learn how to work towards positive conflict outcomes. While serious games related to conflict exist at present, our work represents the first attempt to teach conflict resolution skills through a game in a manner informed by sociological and psychological theories of conflict and current best practice for conflict resolution. In this paper, we present a computational approach to conflict generation and resolution. We describe the five phases involved in our conflict modeling process: conflict situation creation, conflict detection, player modeling and conflict strategy prediction, conflict management, and conflict resolution, and discuss the three major elements of our player model: assertiveness, cooperativeness, and relationship. Finally, we overview a simple resource management game we have developed in which we have begun experimenting with our conflict model concepts.peer-reviewe