YouMove is a novel system that allows users to record and learn physical movement sequences. The recording system is designed to be simple, allowing anyone to create and share training content. The training system uses recorded data to train the user using a large-scale augmented reality mirror. The system trains the user through a series of stages that gradually reduce the user's reliance on guidance and feedback. This paper discusses the design and implementation of YouMove and its interactive mirror. We also present a user study in which YouMove was shown to improve learning and short-term retention by a factor of 2 compared to a traditional video demonstration.

We examine the use of modern recommender system technology to aid command awareness in complex software applications. We first describe our adaptation of traditional recommender system algorithms to meet the unique requirements presented by the domain of software commands. A user study showed that our item-based collaborative filtering algorithm generates 2.1 times as many good suggestions as existing techniques. Motivated by these positive results, we propose a design space framework and its associated algorithms to support both global and contextual recommendations. To evaluate the algorithms, we developed the CommunityCommands plug-in for AutoCAD. This plug-in enabled us to perform a 6-week user study of real-time, within-application command recommendations in actual working environments. We report and visualize command usage behaviors during the study, and discuss how the recommendations affected users behaviors. In particular, we found that the plug-in successfully exposed users to new commands, as unique commands issued significantly increased.

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Users of complex software applications frequently need to consult documentation, tutorials, and support resources to learn how to use the software and further their understanding of its capabilities. Existing online help systems provide limited context awareness through "what's this?" and similar techniques. We examine the possibility of making more use of the user's current context in a particular application to provide useful help resources. We provide an analysis and taxonomy of various aspects of application context and how they may be used in retrieving software help artifacts with web browsers, present the design of a context-aware augmented web search system, and describe a prototype implementation and initial user study of this system. We conclude with a discussion of open issues and an agenda for further research.