Learning and performance with gesture guides

Gesture-based interfaces are becoming more prevalent and complex, requiring non-trivial learning of gesture sets. Many methods for learning gestures have been proposed, but they are often evaluated with short-term recall tests that measure user performance, rather than learning. We evaluated four types of gesture guides using a retention and transfer paradigm common in motor learning experiments and found results different from those typically reported with recall tests. The results indicate that many guide systems with higher levels of guidance exhibit high performance benefits while the guide is being used, but are ultimately detrimental to user learning. We propose an adaptive guide that does not suffer from these drawbacks, and that enables a smooth transition from novice to expert. The results contrasting learning and performance can be explained by the guidance hypothesis. They have important implications for the design and evaluation of future gesture learning systems.