A user's mental model of a system should be an important determinant of performance and as well as a means of understanding why particular user errors occur. In particular, experienced users' models should be in closer agreement with the system than less experienced users' models, and deviations of expert models from the system should correspond to difficulties in performance and suggest ways that system usability could be improved. The present study explored the utility of scaling techniques for defining and comparing user and system models. The results support the assertion that with experience users' mental models approach the system model. However, even experienced users had significant deviations from the system model, leading to predictions of where experts would have difficulty using the system and suggestions for improving usability.
系统的用户思维模式应该是性能的重要决定因素,也是理解为什么发生特定用户错误的一种手段。尤其是,有经验的用户模型应与经验较少的用户模型在系统上更接近,并且专家模型与系统的偏差应对应于性能上的困难,并提出可以改善系统可用性的方法。本研究探索了用于定义和比较用户模型和系统模型的缩放技术的实用性。结果支持这样的说法,即经验丰富的用户思维模型接近系统模型。但是,即使是经验丰富的用户也与该系统模型有很大的出入,从而可以预测专家在哪里难以使用该系统,并提出改善可用性的建议。 P>
IBM Thomas J. Watson Research Center, Yorktown Heights, NY;
New Mexico State Univ., Las Cruces;
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