Within the wider open science reform movement, HCI researchers are actively debating how to foster transparency in their own field. Publication venues play a crucial role in instituting open science practices, especially journals, whose procedures arguably lend themselves better to them than conferences. Yet we know little about how much HCI journals presently support open science practices. We identified the 51 most frequently published-in journals by recent CHI first authors and coded them according to the Transparency and Openness Promotion guidelines, a high-profile standard of evaluating editorial practices. Results indicate that journals in our sample currently do not set or specify clear openness and transparency standards. Out of a maximum of 29, the modal score was 0 (mean = 2.5, SD = 3.6, max = 15). We discuss potential reasons, the aptness of natural science-based guidelines for HCI, and next steps for the HCI community in furthering openness and transparency. CCS CONCEPTS• Human-centered computing → HCI theory, concepts and models; Empirical studies in HCI.
In this paper, we use preference learning techniques to model players' emotional preferences in an AR mobile game. This exploratory study uses player behaviour to make these preference predictions. The described techniques successfully predict players' frustration and challenge levels with high accuracy while all other preferences tested (boredom, excitement and fun) perform better than random chance. This paper describes the AR treasure hunt game we developed, the user study conducted to collect player preference data, analysis performed, and preference learning techniques applied to model this data. This work is motivated to personalize players' experiences by using these computational models to optimize content creation and game balancing systems in these environments. The generality of our technique, limitations, and usability as a tool for personalization of AR mobile games is discussed.
In this paper, we present PlayMapper, a novel variant of the MAP-Elites algorithm that has been adapted to map the level design space of the Super Mario Bros game. Our approach uses player and level based features to create a map of playable levels. We conduct an experiment to compare the effect of different sets of input features on the range of levels generated using this technique. In this work, we show that existing searchbased techniques for PCG can be improved to allow for more control and creative freedom for designers. Current limitations of the system and directions for future work are also discussed.
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