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Six Learning Barriers in End-User Programming Systems

As programming skills increase in demand and utility, the learnability of end-user programming systems is of utmost importance. However, research on learning barriers in programming systems has primarily focused on languages, overlooking potential barriers in the environment and accompanying libraries. To address this, a study of beginning programmers learning Visual Basic.NET was performed. This identified six types of barriers: design, selection, coordination, use, understanding, and information. These barriers inspire a new metaphor of computation, which provides a more learner-centric view of programming system design

Maximizing the guessability of symbolic input

Guessability is essential for symbolic input, in which users enter gestures or keywords to indicate characters or commands, or rely on labels or icons to access features. We present a unified approach to both maximizing and evaluating the guessability of symbolic input. This approach can be used by anyone wishing to design a symbol set with high guessability, or to evaluate the guessability of an existing symbol set. We also present formulae for quantifying guessability and agreement among guesses. An example is offered in which the guessability of the EdgeWrite unistroke alphabet was improved by users from 51.0% to 80.1% without designer intervention. The original and improved alphabets were then tested for their immediate usability with the procedure used by MacKenzie and Zhang (1997). Users entered the original alphabet with 78.8% and 90.2% accuracy after 1 and 5 minutes of learning, respectively. The improved alphabet bettered this to 81.6% and 94.2%. These improved results were competitive with prior results for Graffiti, which were 81.8% and 95.8% for the same measures.

Examining task engagement in sensor-based statistical models of human interruptibility

The computer and communication systems that office workers currently use tend to interrupt at inappropriate times or unduly demand attention because they have no way to determine when an interruption is appropriate. Sensor?based statistical models of human interruptibility offer a potential solution to this problem. Prior work to examine such models has primarily reported results related to social engagement, but it seems that task engagement is also important. Using an approach developed in our prior work on sensor?based statistical models of human interruptibility, we examine task engagement by studying programmers working on a realistic programming task. After examining many potential sensors, we implement a system to log low?level input events in a development environment. We then automatically extract features from these low?level event logs and build a statistical model of interruptibility. By correctly identifying situations in which programmers are non?interruptible and minimizing cases where the model incorrectly estimates that a programmer is non?interruptible, we can support a reduction in costly interruptions while still allowing systems to convey notifications in a timely manner.

Integrated text entry from power wheelchairs

Power wheelchair joysticks have be used to control a mouse cursor on desktop computers, but they offer no integrated text entry solution, confining users to point-and-click or point-and-dwell with on-screen keyboards. On-screen keyboards reduce useful screen real-estate, exacerbating the need for frequent window management, and impose a secondary focus of attention. By contrast, we present two integrated gestural text entry methods designed for use from power wheelchairs: one for use with joysticks and the other for use with touchpads. Both techniques are adaptations of EdgeWrite, originally a stylus-based unistroke method designed for people with tremor. In a preliminary text entry study of 7 power wheelchair users, we found that EdgeWrite with a touchpad was faster than the on-screen keyboard WiViK with a joystick, and EdgeWrite with a joystick was only slightly slower. These results warranted a multi-session comparison of text entry with EdgeWrite and WiViK using joysticks and touchpads, in which we found touchpads faster than joysticks, and EdgeWrite faster than WiViK with both devices after initial learning periods.

Joystick text entry with date stamp, selection keyboard, and EdgeWrite

MOTIVATION Mobile phones and game consoles are two domains in which a good joystick text entry method would be valuable, since these devices lack conventional keyboards. Joysticks have considerable tenure as input devices, but few text entry methods have been developed for them. Common joystick text entry methods, like date stamp and selection keyboard, are selection-based: they require screen real-estate to display options, are difficult to customize, cannot be used without looking, and are slow. A gestural joystick method, on the other hand, could alleviate these limitations in exchange for the need to learn letter-forms. If the letter-forms were easy to learn, such a technique could have significant advantages over current methods.