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An Analysis of Automated Answer Evaluation Systems based on Machine Learning

机译:基于机器学习的自动答案评估系统分析

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Evaluation of the answers remain as one of the most important factors in the learning and teaching process. Automatic evaluation of the answers is very necessary thus, many system has been developed in this digital era. Usually, the subjective answers are in either short form or long answers. The existing system available for evaluation has shown mediocre result in evaluating and scoring the answers. In such frameworks, the data recovery technique to gauge likeness between understudies answer and references answer is utilized, yet such scoring framework doesn't give the best outcome yet. There are very few keywords available in short answers. The answers with such limited number of keywords needs special care, especially while calculating the weighting score of the answers. In the presented study, we try to summarize the existing mechanism and analyses the performance of the system used for automatic grading of the long and descriptive answers.
机译:答案的评估仍然是学与教过程中最重要的因素之一。答案的自动评估是非常必要的,因此,在这个数字时代已经开发了许多系统。通常,主观答案可以是简短答案,也可以是长答案。现有的可用于评估的系统在评估和评分答案方面显示出中等的结果。在这样的框架中,使用了用于评估研究答案和参考答案之间的相似性的数据恢复技术,但是这种评分框架并未给出最佳结果。简短答案中几乎没有可用的关键字。关键字数量如此有限的答案需要特别注意,特别是在计算答案的加权分数时。在提出的研究中,我们试图总结现有的机制,并分析用于长篇幅和描述性答案的自动评分的系统的性能。

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