performance

This paper presents TinyMotion, a pure software approach for detecting a mobile phone user's hand movement in real time by analyzing image sequences captured by the built-in camera. We present the design and implementation of TinyMotion and several interactive applications based on TinyMotion. Through both an informal evaluation and a formal 17-participant user study, we found that 1. TinyMotion can detect camera movement reliably under most background and illumination conditions. 2. Target acquisition tasks based on TinyMotion follow Fitts' law and Fitts law parameters can be used for TinyMotion based pointing performance measurement. 3. The users can use Vision TiltText, a TinyMotion enabled input method, to enter sentences faster than MultiTap with a few minutes of practicing. 4. Using camera phone as a handwriting capture device and performing large vocabulary, multilingual real time handwriting recognition on the cell phone are feasible. 5. TinyMotion based gaming is enjoyable and immediately available for the current generation camera phones. We also report user experiences and problems with TinyMotion based interaction as resources for future design and development of mobile interfaces.

As technical as we have become, modern computing has not permeated many important areas of our lives, including mathematics education which still involves pencil and paper. In the present study, twenty high school geometry students varying in ability from low to high participated in a comparative assessment of math problem solving using existing pencil and paper work practice (PP), and three different interfaces: an Anoto-based digital stylus and paper interface (DP), pen tablet interface (PT), and graphical tablet interface (GT). Cognitive Load Theory correctly predicted that as interfaces departed more from familiar work practice (GT > PT > DP), students would experience greater cognitive load such that performance would deteriorate in speed, attentional focus, meta-cognitive control, correctness of problem solutions, and memory. In addition, low-performing students experienced elevated cognitive load, with the more challenging interfaces (GT, PT) disrupting their performance disproportionately more than higher performers. The present results indicate that Cognitive Load Theory provides a coherent and powerful basis for predicting the rank ordering of users' performance by type of interface. In the future, new interfaces for areas like education and mobile computing could benefit from designs that minimize users' load so performance is more adequately supported.