We propose a novel sensing technique called proactive sensing. Proactive sensing continually repositions a camera-based sensor as a way to improve hand pose estimation. Our core contribution is a scheme that effectively learns how to move the sensor to improve pose estimation confidence while requiring no ground truth hand poses. We demonstrate this concept using a low-cost rapid swing arm system built around the state-of-the-art commercial sensing system Leap Motion. The results from our user study show that proactive sensing helps estimate users' hand poses with higher confidence compared to both static and random sensing. We further present an online model update to improve performance for each user.

Educational technology requires a delivery mechanism to scale. One method that has not yet seen widespread use is the educational campaign: large-scale, short-term events focused on a specific educational topic, such as the Hour of Code campaign. These are designed to generate media coverage and lend themselves nicely to collaborative or competitive goals, thus potentially leveraging social effects and community excitement to increase engagement and reach students who otherwise would not participate. In this article, we present a case study of three such campaigns that we ran to encourage students to play an algebra game—DragonBox Adaptive: the Washington, Norway, and Minnesota Algebra Challenges. We provide several design recommendations for future campaigns based on our experience, including the effects of different incentive schemes, the insertion of “tests” to fast-forward students to levels of appropriate difficulty, and the strengths and weaknesses of campaigns as a method of collecting experimental data.

There is great interest in leveraging video games to improve student engagement and motivation. However, educational games are not uniformly effective, and little is known about how in-game rewards affect children's learning-related behavior. In this work, we argue that educational games can be improved by fundamentally changing their incentive structures to promote the growth mindset, or the belief that intelligence is malleable. We present "brain points," a system that encourages the development of growth mindset behaviors by directly incentivizing effort, use of strategy, and incremental progress. Through a study of 15,000 children, we show that the "brain points" system encourages more low-performing students to persist in the educational game Refraction when compared to a control, and increases overall time played, strategy use, and perseverance after challenge. We believe that this growth mindset incentive structure has great potential in many educational environments.

Video games are increasingly recognized as a compelling platform for instruction that could be leveraged to teach students at scale. Hint systems that provide personalized feedback to students in real time are a central component of many effective interactive learning environments, however little is known about how hints impact player behavior and motivation in educational games. In this work, we study the effectiveness of hints by comparing four designs based on successful hint systems in intelligent tutoring systems and commercial games. We present results from a study of 50,000 students showing that all four hint systems negatively impacted performance compared to a baseline condition with no hints. These results suggest that traditional hint systems may not translate well into the educational game environment, highlighting the importance of studying student behavior to understand the impact of new interactive learning technologies.