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POLICY CAPTURING AND FUZZY LOGIC: A BETTER APPROACH TOREPRESENTING JUDGMENT DATA?

机译:策略捕获和模糊逻辑:一种更好的表示判断数据的方法?

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At the 45th annual meeting of HFES, we conducted an alternative format session in which fuzzyrnlogic was introduced as an alternative approach to analyzing judgment data and representingrndecision-making policies (see Buff et al., 2001). During the altemative format session, usabilityrnjudgments were collected on-site for Advanced Distance Leaming (ADL) applications. Theserndata provided the basis for an empirical assessment of the value added of one modelingrntechnique, fuzzy logic, over the more traditional approach to analyzing policy capturing data,rnmultiple linear regression. This paper describes the results of an empirical assessment of the twornmodeling techniques. For a discussion of the empirical results of the impact of the differentrnusability dimensions on the learning effectiveness of ADL applications, see Holness, Pharmer,rnand Buff (2002).
机译:在HFES的第45届年会上,我们进行了另一种形式的会议,其中引入了模糊逻辑,作为分析判断数据和代表决策制定政策的一种替代方法(请参阅Buff等,2001)。在替代格式会议期间,针对高级距离学习(ADL)应用程序在现场收集了可用性判断。这些数据为对一种建模技术,模糊逻辑的增值进行实证评估提供了基础,而这种评估技术是较传统的分析策略捕获数据的方法,即多元线性回归。本文介绍了两种建模技术的经验评估结果。有关不同可使用性维度对ADL应用程序学习效果的影响的实证结果的讨论,请参见Holness,Pharmer,rnand Buff(2002)。

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