复杂语义解释和学习的诊断性认知评估:贝叶斯网络方法

Zhidong Zhang
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引用次数: 3

摘要

本研究为强调语义解释的ANOVA评分模型探索了一种诊断性认知评估模型。本研究采用混合方法设计,将ANOVA评分模型分解为可测量分量。这包括熟练学生模型。这些数据通过ANOVA评分模型的贝叶斯网络模型和语义解释评估转换为定量表示。该诊断性认知评估分为解释变量和证据变量28个变量。9个变量是潜在的解释变量。19个变量是证据变量,它们收集学生的学习信息,并将这些信息传递给解释变量。所述数据为模拟数据;12名学生的语义解释被记录并输入到19个证据变量中。语义解释分为低级、中级和高级三个层次。成绩应在82分以上,即达到精通水平。该研究还表明,如果一个学生在一个模块中获得高分,那么他在整体评估模型中获得高分的机会就更大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diagnostically Cognitive Assessment of Complex Semantic Explanations and Learning: A Bayesian Network Approach
This study explored a diagnostically cognitive assessment model for the ANOVA score model emphasizing semantic explanations. The study used the mixed methods designs, in which the ANOVA score model was decomposed into measurable components. This consists of the proficiency student model. Such kinds of data were transferred to a quantitative representation via the Bayesian network model of the ANOVA score model and semantic explanation assessment. This diagnostically cognitive assessment consists of 28 variables hierarchically, which are explanatory variables and evidence variables. Nine variables are explanatory variables that are latent. Nineteen variables are evidence variables that collect students’ learning information and propagate the information to the explanatory variables. The data are simulated data; the semantic explanations from twelve students were recorded and input into the nineteen evidence variables. Semantic explanations indicate 3 levels: lower level, medium level and high level. The score should be more than 82 points, which indicates a mastery level. The study also suggests that if a student achieves a high score in a module, the student has a better chance of achieving a high score in the overall assessment model.
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