Dominic Kao

Exploring the Impact of Role Model Avatars on Game Experience in Educational Games

Studies show that role models can boost academic performance. In this paper, we describe an experiment (N = 890) exploring the use of (a) scientist role models, (b) athlete role models and (c) simple geometric shapes, as game avatars. Using the Game Experience Questionnaire (GEQ), we find that over all participants, the use of avatars that looked like scientist and athlete role models led to highest flow and immersion. For female participants, the use of scientist avatars led to highest immersion and positive affect, and lowest tension and negative affect. The results here indicate that role model avatars have the potential to positively affect player game experience. This may especially be impactful for educational games, in which higher engagement could in turn influence learning outcomes.

Computationally Modeling Narratives of Social Group Membership with the Chimeria System

Narratives are often used to form, convey, and reinforce memberships in social groups. Our system, called Chimeria, implements a model of social group membership. Here, we report upon the Chimeria Social Narrative Interface (Chimeria-SN), a component of the Chimeria system, that conveys this model to users through narrative. This component is grounded in a sociolinguistics model of conversational narrative, with some adaptations and extensions in order for it to be applied to an interactive social networking domain. One eventual goal of this work is to be able to extrapolate social group membership by analyzing narratives in social networks; this paper deals with the inverse of that problem, namely, synthesizing narratives from a model of social group membership dynamics.

Toward avatar models to enhance performance and engagement in educational games

This paper presents work toward better understanding the roles that avatars can play in supporting learning in educational games. Specifically, the paper presents results of empirical studies on the impact of avatar type on learner/player performance and engagement. These results constitute work establishing baseline understandings to inform our longer term goal of developing models that use dynamic avatars to best support learners in educational games. Our aim is motivated by a convergence of research in the social sciences establishing that identity plays an important role in learning. Of note, aspects of social identity (e.g., race, ethnicity, and gender) have been shown to impact student performance [1] via triggering stereotypes [2]. Recently, performance and engagement studies in our educational game for Science, Technology, Engineering and Mathematics (STEM) learning suggest these same phenomena can be activated through virtual avatars [3], [4]. Here, we present results of a comparative study between avatars in the likeness of players and avatars as geometric shapes. In our STEM learning game, results show that players that had selected and used a shape avatar had significantly higher performance than players that had customized and used a likeness avatar. Players using the shape avatar also had significantly higher self-reported engagement, despite having lower self-reported affect towards the avatar.

Exploring the Impact of Avatar Color on Game Experience in Educational Games

The color red has been shown to hinder performance, motivation, and affect in a variety of contexts involving cognitively demanding tasks. Teams wearing red have been shown to impair the performance of opposing teams, present even in online gaming. Although color is strongly contextual (e.g., red-failure association), its effects are posited to be sub-conscious and operate powerfully even on nonhuman primates, e.g., Rhesus macaques (Macaca mulatta) take food significantly less often from an experimenter wearing red. Here, we present one of the first studies on avatar color in a single-player game. We compared players using a red avatar to players using a blue avatar. Using the Game Experience Questionnaire (GEQ), we find that players using a red avatar had a decrease in competence, immersion and flow. Our results are of consequence to how we design and choose colors in single-player contexts.

MazeStar : a platform for studying virtual identity and computer science education

This paper presents an overview of the MazeStar platform for Computer Science education. MazeStar is both a game (Mazzy) that teaches programming concepts like loops and conditionals, and a game editor which allows players to create and share their own game levels. By playing and creating, players are using computing concepts (e.g., block structuring, parallelism, etc.) and computing practices (e.g., debugging, iterative prototyping, etc.). To date the MazeStar platform has been used in controlled user studies involving > 10,000 participants. Here, our goal is to detail the different components of the MazeStar platform, and how we have/are leveraging these components to study the interplay of education, games/game-making, and virtual identity.