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Switching Between Tools in Complex Applications

Will Schroeder

Journal of Usability Studies, Volume 3, Issue 4, August 2008, pp. 173-188

Article Contents


The problem

For a complex application (toolkit), the best design results in timely application of correctly chosen tools, each for as long as it remains the best choice. This means not only that we optimize individual tools, but also (a) minimize the labor of switching, (b) facilitate correct tool selection, and (c) arrange switching at the right time. Options a and b imply either planning of tool use, monitoring of task progress, or both to ensure that correct and timely choices be made. However, optimal work with a single tool requires neither option b nor c.

Long term goal

Tool switching has the following three aspects we would like to improve with better design:

But we must walk before we can run. First we must be able to compare and evaluate patterns of tool choice and switch timing and determine critical incidents for study in realistic (complex) usability tasks or in actual work. Then we will know where to focus design effort and be able to measure how well our new designs foster more effective tool switching strategies.

Where to begin?

Before we get into analysis, and then into design (UCD practitioner) or experimentation (academic), we must ask, can we compare tool switching and patterns of switching effectively? This takes a lot of specific data and fresh ways of looking at it. The amount of raw data needed to study the subtasks and transitions that make up a complex "real" task can overload normal usability techniques. Test observations and facilitator's notes do not suffice. We need "unattended data capture for portions of a long-term evaluation, used along with observations and interviews" (Redish, 2007, p. 107). This data is useless without effective tools and metrics that make it possible to study and to compare the patterns of use and prioritize the design effort.

Simple and complex tasks

In simple tasks, work steps and tool choices are known beforehand. Deviations from expected path and usage are very likely to be errors or inefficiencies. In a complex task, the workflow is not known ahead of time and generally cannot be planned. It is driven by unique or unfamiliar problems users encounter. In response, users apply tools chosen from the set they are aware of as best they can. To pose a complex task, we either (a) give users a problem without a formulaic solution (e.g., a programming problem) or (b) give users a simple task amid a large array of possible tools (e.g., plotting and formatting data). Complex applications evolve to facilitate complex tasks.

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