Baysian Evaluation System based on Teachers 'Wisdom Using Network Model

(1)提出了贝叶斯分层建模理论，用于分析网络模型。此外，使用吉布斯采样器开发了适用于分层方法的分析方法。为了分析精益生产者的内部过程，需要进行复杂的建模，但可用的数据往往是分类数据。因此，我们需要包含潜变量分布和先验分布的分层模型。同时，Gibbs抽样也是一个非常有用的工具，可以得到参数的可靠估计。(2)一个贝叶斯网络模型的开发，以确定学习者的错误规则，更一般的学习者的学习地图中的状态。该系统计算每个可能的错误规则的后验概率。此外，作为另一个应用，开发了以连续变量为节点的医疗网络诊断系统。该系统可以很容易地修改，以处理教育数据。(3)本研究发展了一个多分类反应的项目反应模式，以获得对教师有用的信息。这种分析可以区分同一成绩的能力参数，并为每个测试项目提供更详细的信息。

(1) The Baysian Theory of Hierarchical Modeling was developed to analyze the network model. Also, the suitable analysis method for the hierarchical method was developed using the Gibbs Sampler. In order to analyze the internal process of the leaner, the complex modeling is necessary, but the available data is often categorical data. Therefore, we need the hierarchical model which includes the distributions of latent variables and the prior distributions. Also, the Gibbs sampling is a very useful tool to obtain the reliable estimates of parameters.(2) A Baysian network model was developed to identify learners' bug rules and more generally learner's status in the learning map. This system calculates the posterior probability for each of the possible bug rules. Also, as another application, the medical network diagnosis system which includes the continuous variables as nodes was developed. This system can be modified easily to handle the educational data.(3) An Item Response Model for the polychotomous responses was developed to obtain useful information for teachers. This analysis can discriminates the ability parameters of the same score, and can provide the more detailed information for each of test items.