Skills acquisition can be performed using virtual simulators. The issue about assessment of the skills acquired in those environments is a problem not solved since each simulator can collect different interaction data. Data types and distribution can demand specific approaches, i.e, the multiples interaction possibilities between users and virtual simulators do not allow a unified solution. Several assessment models found in the scientific literature are based on Naive Bayes networks, which in turn are based on two kinds of probability measures: the classical one; and that one applied on fuzzy events. In this work, we propose a third one, which use the probability based on fuzzy parameters. The theoretical proposal of a new Naive Bayes network form, which uses Gaussian distribution, is presented and named GAUNB-FP network. It is proposed as the kernel of a new training assessment system and tested considering a bone marrow harvest simulator with three classes of performance provided by experts. Its performance was evaluated, according to decision matrix analysis, Kappa Coefficient and its variance. The results are compared with other four assessment systems based on different networks found in the scientific literature: Naive Bayes Network, Gaussian Naive Bayes Network, Bayesian Network and Multilayer Perceptron Neural Network. The comparison has shown the new assessment system based on GAUNB-FP network provided better results with respect of two of the other networks. It provided also the best results for two of three classes of performance. Therefore, in this comparative experiment, the training assessment system based on GAUNB-FP network presented competitive results.

技能获取可以使用虚拟模拟器执行。关于评估在这些环境中获得的技能的问题是一个无法解决的问题，因为每个模拟器都可以收集不同的交互数据。数据类型和分布可能需要特定的方法，即，用户和虚拟模拟器之间的多重交互可能性不允许统一的解决方案。科学文献中发现的几种评估模型均基于朴素贝叶斯网络，而朴素贝叶斯网络又基于两种概率测度：经典测度；第二种测度。而那个应用于模糊事件。在这项工作中，我们提出了第三种方法，该方法使用基于模糊参数的概率。提出了一种新的朴素贝叶斯网络形式（使用高斯分布）的理论建议，并将其命名为GAUNB-FP网络。它被提议作为新的培训评估系统的核心，并考虑了具有专家提供的三类性能的骨髓采集模拟器进行测试。根据决策矩阵分析，Kappa系数及其方差评估了其性能。将结果与基于科学文献中发现的不同网络的其他四个评估系统进行比较：朴素贝叶斯网络，高斯朴素贝叶斯网络，贝叶斯网络和多层感知器神经网络。比较表明，基于GAUNB-FP网络的新评估系统相对于其他两个网络提供了更好的结果。对于三类性能中的两类，它也提供了最佳结果。因此，在这个比较实验中