改进参考答案正确性的新框架,以提高 ASAG 系统的结果
T R Akila Devi1, K Javubar Sathick2, A Abdul Azeez Khan2
1CSE Department, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India.
自动化简短答案分级 (ASAG) 系统需要准确的参考答案. 这项研究开发了一个使用零射击分类器和变压器组合的框架,以生成精确的参考答案,提高分级准确性.
科学领域:
- 教育技术的教育技术
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 越来越多地使用在线学习平台,需要自动化评分系统.
- 自动评分的准确性在很大程度上取决于参考答案的质量.
- 确保参考答案的正确性对于可靠的学习者评估至关重要.
研究的目的:
- 开发一个框架,以提高自动化简短答案分级 (ASAG) 系统中参考答案的准确性.
- 改善学生答案准确评分的基础.
- 为了解决教育技术中参考答案正确性的问题.
主要方法:
- 制定了一个框架,涉及到物质内容的获取和聚类.
- 专家的答案被用作关键组件,由零射击分类器处理以生成参考答案.
- 一组变压器被用来通过生成的参考答案和莫勒数据集来对学生的答案进行评分.
主要成果:
- 开发的框架成功地产生了准确的参考答案.
- 使用这些参考答案的评分系统显示了性能的提高.
- 与以前的数据集值进行比较,显示出优越的RMSE和相关性指标.
结论:
- 拟议的框架大大提高了ASAG系统中参考答案的准确性.
- 这种方法导致更可靠,更准确的简短答案的自动分级.
- 该模型在评估在线平台上的学习者表现方面优于现有的方法.
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