開始時刻
2020-06-27 16:15:00
時間
2分
発表担当

終了した勉強会

この勉強会は終了しました。ご参加ありがとうございました。

Pac-Euglena: A Living Cellular Pac-Man Meets Virtual Ghosts
説明

The advancement of biotechnology enabled the development of "biotic video games", where human players manipulate real biological samples for fun and educational human-biology interactions. However, new design principles are needed to both leverage and mitigate biological properties (e.g., variability and stochasticity), and create unique play experiences that transcend traditional video games. This paper describes the implementation of Pac-Euglena, a biotic Pac-Man analog, where players guide live microscopic Euglena cells with light stimuli through a physical microfluidic maze. Through use of multi-modal stimuli, a mixed biology-digital-human reality is achieved, enabling cell interactions with virtual ghosts and collectibles. Through an iterative design process, we illustrate challenges and strategies for designing games with living organisms. A user study (n=18, conducted at a university event) showed that Pac-Euglena was fun, stimulated curiosity, and taught users about Euglena. We conclude with five general guidelines for the design and development of biotic games and HBI interfaces.

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Designing IoT Resources to Support Outdoor Play for Children
説明

We describe a Research-through-Design (RtD) project that explores the Internet of Things (IoT) as a resource for children's free play outdoors. Based on initial insights from a design ethnography, we developed four RtD prototypes for social play in different scenarios of use outdoors, including congregating on a street or in a park to play physical games with IoT. We observed these prototypes in use by children in their free play in two community settings, and report on the qualitative analysis of our fieldwork. Our findings highlight the designs' material qualities that encouraged social and physical play under certain conditions, suggesting social affordances that are central to the success of IoT designs for free play outdoors. We provide directions for future research that addresses the challenges faced when deploying IoT with children, contributing new considerations for interaction design with children in outdoor settings and free play contexts.

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Investigating User-Created Gamification in an Image Tagging Task
説明

Commonly, gamification is designed by developers and not by end-users. In this paper we investigate an approach where users take control of this process. Firstly, users were asked to describe their own gamification concepts which would motivate them to put more effort into an image tagging task. We selected this task as gamification has already been shown to be effective here in previous work. Based on these descriptions, an implementation was made for each concept and given to the creator. In a between-subjects study (n=71), our approach was compared to a no-gamification condition and two conditions with fixed gamification settings. We found that providing participants with an implementation of their own concept significantly increased the amount of generated tags compared to the other conditions. Although the quality of tags was lower, the number of usable tags remained significantly higher in comparison, suggesting the usefulness of this approach.

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Recognizing Affiliation: Using Behavioural Traces to Predict the Quality of Social Interactions in Online Games
説明

Online social interactions in multiplayer games can be supportive and positive or toxic and harmful; however, few methods can easily assess interpersonal interaction quality in games. We use behavioural traces to predict affiliation between dyadic strangers, facilitated through their social interactions in an online gaming setting. We collected audio, video, in-game, and self-report data from 23 dyads, extracted 75 features, trained Random Forest and Support Vector Machine models, and evaluated their performance predicting binary (high/low) as well as continuous affiliation toward a partner. The models can predict both binary and continuous affiliation with up to 79.1% accuracy (F1) and 20.1% explained variance (R2) on unseen data, with features based on verbal communication demonstrating the highest potential. Our findings can inform the design of multiplayer games and game communities, and guide the development of systems for matchmaking and mitigating toxic behaviour in online games.

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