主观题自动评判算法研究综述  

Survey of Research on Automated Grading Algorithms for Subjective Questions

作　　者：冯筠[1] 栗凯旋 高志泽樟 黄立 孙霞[1] FENG Jun;LI Kaixuan;GAO Zhizezhang;HUANG Li;SUN Xia(School of Information Science and Technology,Northwest University,Xi’an 710000,China)

机构地区：[1]西北大学信息科学与技术学院,西安710000

出　　处：《计算机科学》2024年第10期33-39,共7页Computer Science

基　　金：陕西省科技厅重点产业链项目(2019ZDLGY03-10)。

摘　　要：在教育教学中,试卷评判是教师获取学生知识点掌握情况的重要途径。然而,试题评分是一个耗时的过程,主观题的评判更需要阅卷人认真、投入、细致地审阅,需要耗费大量精力。要减轻教师工作压力,提高主观题评判的效率,基于人工智能的自动评判技术非常重要,其中主观题的自动评判是难点。随着机器学习和深度学习等技术在自然语言处理领域的发展,主观题自动评判技术有了较大进展。文中将主观题分为常规型和开放型两类进行文献梳理,总结主观题自动评价的标准和公开数据集,归纳涉及的方法和技术路线,并对主观题自动评判技术未来的研究方向进行总结和展望。In educational teaching,paper assessment is an important means for teachers to understand students’grasp of know-ledge points.However,grading exam questions is a time-consuming process,and assessing subjective questions requires examiners to review the work carefully,with engagement and attention to detail,often consuming a significant amount of energy.To reduce the workload on teachers and improve the efficiency of subjective question assessment,research on AI-based automatic grading techniques is imperative,with subjective question evaluation posing a particular challenge.With advancements in machine learning and deep learning in the field of natural language processing,significant progress has been made in the automation of subjective question assessment.This paper categorizes subjective questions into conventional and open-ended types,respectively,conducts a literature review,summarizes evaluation criteria and publicly available datasets,and outlines methods and technological approaches involved.Finally,the future research directions of automatic evaluation of subjective questions is summarized and prospected.

关 键 词：自动阅卷 主观题 自然语言处理 深度学习 智能教育 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP399[自动化与计算机技术—控制科学与工程]