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In this paper, we describe our work on developing a system to support the personalization of a captured public experience. Specifically, we are interested in providing students with the ability to personalize the capture of the lecture experiences as part of the Classroom 2000 project. We discuss the issues and challenges involved in designing a system that performs live integration of personal streams of information with multiple other streams of information made available to it through an environment designed to capture public information.

EdgeWrite is a new unistroke text entry method for handheld devices designed to provide high accuracy and stability of motion for people with motor impairments. It is also effective for able-bodied people. An EdgeWrite user enters text by traversing the edges and diagonals of a square hole imposed over the usual text input area. Gesture recognition is accomplished not through pattern recognition but through the sequence of corners that are hit. This means that the full stroke path is unimportant and recognition is highly deterministic, enabling better accuracy than other gestural alphabets such as Graffiti. A study of able-bodied users showed subjects with no prior experience were 18% more accurate during text entry with Edge Write than with Graffiti (p>.05), with no significant difference in speed. A study of 4 subjects with motor impairments revealed that some of them were unable to do Graffiti, but all of them could do Edge Write. Those who could do both methods had dramatically better accuracy with Edge Write.

Many applications provide a form-like interface for requesting information: the user fills in some fields, submits the form, and the application presents corresponding results. Such a procedure becomes burdensome if (1) the user must submit many different requests, for example in pursuing a trial-and-error search, (2) results from one application are to be used as inputs for another, requiring the user to transfer them by hand, or (3) the user wants to compare results, but only the results from one request can be seen at a time. We describe how users can reduce this burden by creating custom interfaces using three mechanisms: clipping of input and result elements from existing applications to form cells on a spreadsheet; connecting these cells using formulas, thus enabling result transfer between applications; and cloning cells so that multiple requests can be handled side by side. We demonstrate a prototype of these mechanisms, initially specialised for handling Web applications, and show how it lets users build new interfaces to suit their individual needs.

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.