Investigating the Post-Training Persistence of Expert Interaction Techniques

Benjamin Lafreniere, Carl Gutwin, Andy Cockburn
<span title="2017-08-23">2017</span> <i title="Association for Computing Machinery (ACM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mh5bz4jhtjgydhiv7cbr2pvuby" style="color: black;">ACM Transactions on Computer-Human Interaction</a> </i> &nbsp;
Expert interaction techniques enable users to greatly improve their performance; however, to realize these advantages, the user must first acquire the skill necessary to use a technique, then choose to use it over competing novice techniques. This article investigates several factors that may influence whether use of an expert technique persists when the context of use changes. Two studies examine the effect of changing performance requirements, and find that a high performance requirement
more &raquo; ... ed in a training context can effectively push users to adopt an expert technique, and that use of the technique is maintained when the requirement is subsequently reduced or removed. In a final study, performance requirement, high-level task, and environment of use are changed-participants played a training game to learn the menu for a drawing application, which they then used to complete a series of drawings over the following week. Participants exhibited a somewhat surprising "all-or-nothing" effect, using the expert technique nearly exclusively or not at all, and maintaining this behavior over a range of qualitatively different tasks. This suggests that switching to an expert technique involves a global change by the user, rather than an incremental change as suggested by previous work.
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