My current interests consider the use of games to support the problem-solving that occurs in computer science education. Programming involves continuous learning---repeatedly solving the problem of determining an algorithm for solving a problem. Experienced programmers may perform this problem-solving through iterative trial and error experiments, harnessing the immediate feedback of modern computer programs. This constructivist approach can be highly effective for teaching and learning computer science and programming specifically, though such experimentation is frequently frustrating and may drive away many potential programmers from traditionally underrepresented groups. Many successful programmers learn to approach this experimentation from a playful, game-like perspective. Playful approaches are frequently embedded in the practice of software development, such as in documentation that encourages the programmer to "play around" with a library. Experienced programmers are able to "play" with a program or concept by making small changes ("just to see what happens"), and then using the empirical evidence of these experiments to establish mental models of the how the program works (or doesn't). I refer to this form of playful experimentation with code as playful programming.

Assessing people’s understanding of the causal relationships found in large-scale complex systems may be necessary for addressing many critical social concerns, such as environmental sustainability. Existing methods for assessing systems thinking and causal understanding frequently use the technique of cognitive causal mapping. However, the logistics of this methodology may miss valuable and informative indicators of reductionist and linear thinking, both of which conflict with systems understanding. My dissertation research explores how interactive computer systems can aid in the assessment of causal understanding, allowing educators to perform more in-depth analysis of how subjects engage with the process of causal mapping. In addition, it considers how computer games as a particular form of interactive system may be able to support assessment. Games are framed as effectively supporting learning and education and although assessment is a key component of education, the use of video games for performing assessment is under-explored. To address these topics, I present a prototype interactive game system based on Plate’s (2006) framework for assessing causal understanding through cognitive causal mapping. I tested this prototype in a user study with both student and non-student subjects. Through this study, I found that evaluating the structural forms of causal maps created in an interactive system can suggest the presence of reductionist thinking, while the sequence of causal map construction can indicate the presence of linear thinking. Furthermore, I found that although games as interactive systems can be effective in enabling learning, they may be less readily effective in supporting stand-alone assessments due to requiring an a priori understanding of the complex game system used in assessment, as well as traditional educational assessment contexts not supporting the forms of feedback critical to game-based learning. These results indicate how the linear narratives prominently found in both education and games may interfere with effective systems thinking. This dissertation thus suggests that educators in both formal and informal education contexts should consider alternative, non-narrative curricula and games for teaching and assessing causal understanding of complex systems.

More information can be found in my dissertation.

The Causality Project is an online platform for collecting and visualizing causal knowledge that is distributed not only among experts, but also among everyday users who interact with the complex systems that make up human civilizations and the natural world. This project seeks to compile a massive database of causal relationships between factors in the broad domain of environmental sustainability and beyond. I have helped develop a visualization component that dynamically displays the relationships stored in the database as a causal concept map. This visualization allows users to easily browse and explore the entire causal web included in the database, providing the opportunity to discover causal relationships of which they previously were unaware.

This project has previously looked at at how players perform user-generated game design by developing and negotiating "house rules" or obstacles for one another in the context of a pervasive game for change. This allows players to determine what aspects of a broadly defined game they can most effectively act on in their local context; games can be "scaffolded globally, built locally" for encouraging behavior change. I have designed and tested a scaffolding pervasive games through which players can perform this game design, targeting behavior reduction for environmental sustainability (how a game can encourage players to stop performing actions that negatively impact the environment). My current interests in this research consider how players choose to develop challenges for one another, and how this process can be used to support peer-driven education.

See also: CHI 2011 Doctorial Consortium Poster

Many technological systems aim to support pro-social goals such as environmental sustainability by enabling personal data tracking and increasing individual awareness of behaviors and actions. Because these methods emphasize quantifiable data and understanding of quantitative models, gamification seems particularly well-suited for motivating self-tracking. To explore the effects of adding game components to personal informatics systems, we have developed EcoDefender--a pervasive game that is designed to encourage players to perform and track environmentally preferable actions. EcoDefender is an Android-based mobile phone game that uses self-tracked pro-social behaviors as inputs to a location-based "tower-defense" game. In this game, players directly perform sustainable actions in order to defend a virtual, futuristic version of their real surroundings; players attempt to hold back the encroaching flood of sea-level rise by using their performed actions to build and power protective force-fields. The game uses an augmented reality system to allow players to "see" into this virtual future and witness the benefits of their tracked actions, adding a motivating gaming context to the process of personal informatics. By exploring the application of this form of gamification to personal informatics systems, we seek to inform the design of future serious games that can harness personal data tracking for encouraging direct pro-social behaviors.

See also: 2010 SocialCode Report (on EcoPath)

A popular way for people to understand their environmental impact is by using an online carbon footprint calculator. Although there are a variety of such calculators available, the majority share the same form of user interaction. We analyze how, with this mode of interaction, most calculators focus on the environmental impact of individual actions without drawing attention to the broader impacts of those actions on the surrounding community and world. To address these problems, we present the Better Carbon calculator, which uses collaborative filtering and location-based calculation to provide an individual footprint estimate while simultaneously affecting and improving the estimates for other people in a user's community. This method also allows Better Carbon to be more extendable, as additional forms of impact can be considered without requiring additional user effort. Better Carbon can thus provide quicker and easier footprint estimates, and help the process of calculating a carbon footprint create stronger linkages within the communities of its users.

See also: ISSST 2010 paper, ISSST 2010 slides

Amazon.com's Mechanical Turk (mturk.com) is an online system in which workers are paid small sums of money to work on projects that, while quick and easy for a human, are very difficult for computers to perform correctly and efficiently--for example, identifying and object in an image or categorizing data. Furthermore, workers have little or not context for the work they perform through this highly mediated system. In this project, we explore the question of who are the Mechanical Turkers, looking to identify the kinds of people who work on MTurk and understand the reasons (both intrinsic and extrinsic) for their participation. We are also considering how the ubiquity of monetary rewards may shape the interactions with and potential exploitations within this crowdsourcing system.