面向学科题目的文本分析方法与应用研究综述

分析学科题目含义、模拟人类解决问题,是当前“人工智能+教育”融合研究的重要方向之一。近年来,智能教育系统的快速发展积累了大量学科题目资源,为相关研究提供了数据支撑。为此,利用大数据分析与自然语言处理相关的技术,研究者提出了大量面向学科题目的文本分析方法,开展了许多重要的智能应用任务,对探索人类知识学习等认知能力具有重要意义。该文围绕智能教育与自然语言处理交叉领域,介绍了若干代表性研究任务,包括题目质量分析、机器阅读理解、数学题问答、文章自主评分等,并对相应研究进展进行阐述和总结;此外,对相关数据集和开源工具包进行了总结和介绍;最后,展望了多个未来研究方向。

A Survey on Text Analysis Methods and Applications for Educational Questions

One of the important research directions on the integration of artificial intelligence into pedagogy is analyzing the meanings of educational questions and simulating how humans solve problems. In recent years, a large number of educational question resources have been collected, which provides the data support of the related research. Leveraging the big data analysis and natural language processing related techniques, researchers propose many specific text analysis methods for educational questions, which are of great significance to explore the cognitive abilities of how human master knowledge. In this paper, we summarize several representative topics, including question quality analysis, machine reading comprehension, math problem solving, and automated essay scoring. Moreover, we introduce the relevant public datasets and open-source toolkits. Finally, we conclude by anticipating several future directions.