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首页> 外文会议>Proceedings of the Ninth International Conference on Machine Learning and Cybernetics >A new method for students' learning achievement evaluation based on the eigenvector method
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A new method for students' learning achievement evaluation based on the eigenvector method

机译：基于特征向量法的学生学习成绩评估新方法

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This paper presents a new method for students'' learning achievement evaluation based on the eigenvector method. The proposed method transforms the attributes “accuracy rate” and “time rate” into the “effect of accuracy rate” and the “effect of time rate”, respectively. Then, it generates the relative important degrees of the attributes “effect of accuracy rate”, “effect of time rate”, “importance” and “complexity” based on the eigenvector method, respectively. Then, it uses the correlation coefficients between the attribute vectors and the standard deviations of the elements in the attribute vectors to calculate the fitness degrees of the attributes. Then, it generates the weights of the attributes based on the relative important degrees of the attributes and the fitness degrees of the attributes. Then, it generates the important degrees of the questions according to the weights of the attributes and the relation matrix representing the relationships between the questions and the attributes. Based on the important degrees vector of the questions, the grade matrix and the accuracy rate matrix, it calculates the learning achievement index of each student having the same original total score for students'' learning achievement evaluation. The proposed method provides us a useful way for students'' learning achievement evaluation based on the eigenvector method.
机译：本文提出了一种基于特征向量法的学生学习成绩评估的新方法。所提出的方法将属性“准确率”和“时间率”分别转换为“准确率的影响”和“时间率的影响”。然后，基于特征向量法，分别生成属性“准确率的影响”，“时间率的影响”，“重要性”和“复杂性”的相对重要程度。然后，它使用属性向量与属性向量中元素的标准偏差之间的相关系数来计算属性的适应度。然后，它基于属性的相对重要程度和属性的适合度来生成属性的权重。然后，根据属性的权重和代表问题与属性之间关系的关系矩阵，生成问题的重要程度。基于问题的重要程度向量，等级矩阵和准确率矩阵，计算出具有相同原始总得分的每个学生的学习成绩指数，用于学生的学习成绩评估。该方法为基于特征向量法的学生学习成绩评估提供了一种有用的方法。

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