Anderson, F., and Bischof, W. F. (2013). Learning and performance with gesture guides. ACM SIGCHI Conference on Human Factors in Computing Systems, 1109-1118.
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 evalu- ated 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 sys- tems with higher levels of guidance exhibit high perfor- mance benefits while the guide is being used, but are ulti- mately detrimental to user learning. We propose an adap- tive 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 ex- plained by the guidance hypothesis. They have important implications for the design and evaluation of future gesture learning systems.
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