GradeAid:在教育环境中为自动简短答案评分提供了一个框架 - - 设计,实施和评估.
Emiliano Del Gobbo1, Alfonso Guarino2, Barbara Cafarelli1
1Department of Economics, Management and Territory, University of Foggia, Via da Zara, 11, 71121 Foggia, FG Italy.
概括
GradeAid是自动简短答案分级 (ASAG) 的新框架,它分析了词汇和语义特征. 它实现了与现有系统可比的性能,并支持非英语数据集.
科学领域:
- 自然语言理解 自然语言理解
- 学习分析学习分析
- 教育中的人工智能
背景情况:
- 自动简短答案评分 (ASAG) 对于管理高等教育中大量学生队伍至关重要.
- 现有的ASAG解决方案面临局限性,特别是非英语数据集和强大的验证.
研究的目的:
- 引入GradeAid,这是ASAG的一个新框架.
- 通过结合词汇和语义分析来解决当前ASAG研究的局限性.
- 为未来的ASAG发展提供一个可靠的验证基准.
主要方法:
- 开发了GradeAid,这是一个利用词汇和语义特征联合分析的框架.
- 用最先进的回归器来进行答案得分.
- 对所有公开可用的数据集和一个新的数据集进行了验证和比较.
主要成果:
- 格雷德艾德的性能与现有的ASAG系统相提并论,根平均平方误差低至0.25.
- 该框架成功处理非英语数据集.
- 该研究提供了对GradeAid框架的全面验证和比较.
结论:
- 格雷德艾德为自动简短答案评分提供了一个强大而通用的解决方案.
- 该框架能够处理多样化的数据集,其强的性能使其成为一个有价值的基准.
- 进一步的研究可以在GradeAid的基础上进行,以增强智能辅导系统和反机制.
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