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首页> 外文期刊>Education and information technologies >Short answer scoring system using automatic reference answer generation and geometric average normalized-longest common subsequence (GAN-LCS)
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Short answer scoring system using automatic reference answer generation and geometric average normalized-longest common subsequence (GAN-LCS)

机译：使用自动参考答案生成和几何平均归一化最长公共子序列（GAN-LCS）的简短答案评分系统

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摘要

The Automatic Short Answer Scoring (ASAS) system is one of the tools that can be used to conduct assessment process on e-learning system. One of the methods applied in the ASAS system is a method for measuring similarities between the reference and student answers. There are two issues to be considered in the assessment process using this method. First, this method should be able to provide a variety of reference answers that can handle the diversity of student answers. Secondly, this method should be able to provide an accurate sentence similarity between the reference answers and student answers. Therefore, two methods are proposed to solve both problems. The first method is to generate a variety of reference answers automatically using Maximum Marginal Relevance ( MMR ) method, which obtains an accuracy of 91.95%. The second method is to measure accurately sentence similarity between student answers and reference answers that have significantly different length using GAN-LCS. The performance of the proposed method shows an improvement of the Root Mean Square Error (RMSE) value of 0.884 and a correlation value of 0.468.
机译：自动简答评分（ASAS）系统是可用于对电子学习系统进行评估过程的工具之一。 ASAS系统中应用的一种方法是一种用于测量参考答案和学生答案之间相似度的方法。使用此方法在评估过程中要考虑两个问题。首先，这种方法应该能够提供各种参考答案，可以处理学生答案的多样性。其次，这种方法应该能够在参考答案和学生答案之间提供准确的句子相似度。因此，提出了两种方法来解决这两个问题。第一种方法是使用最大边际相关性（MMR）方法自动生成各种参考答案，其准确性为91.95％。第二种方法是使用GAN-LCS准确测量长度和长度明显不同的学生答案和参考答案之间的句子相似度。所提出方法的性能显示均方根误差（RMSE）值为0.884的改进和相关值为0.468。

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