摘要
随着以慕课(MOOC)为代表的在线教育平台的迅速发展,如何评价平台学习者提交的大规模主观题作业是面临的重大挑战。同伴互评是有效应对大规模主观题作业评价挑战的主流方案,近年来受到学术界与工业界的广泛关注。鉴于此,对面向在线教育的同伴互评技术进行了调研与分析。首先,概述当前实施同伴互评的通用流程;然后,分别阐述评价者分配、评语分析、异常互评信息检测与处理以及主观题作业真实分数估计等重要的同伴互评流程活动的主要研究成果;其次,对比具有代表性的在线教育平台或已发布的教学系统中的同伴互评功能;最后,总结和展望同伴互评的未来发展趋势,为正在从事或打算从事同伴互评研究的人们提供借鉴与参考。
With the rapid development of online education platforms represented by Massive Open Online Courses(MOOC),how to evaluate the large-scale subjective question assignments submitted by platform learners is a big challenge.Peer grading is the mainstream scheme for the challenge,which has been widely concerned by both academia and industry in recent years.Therefore,peer grading technologies for online education were survyed and analyzed.Firstly,the general process of peer grading was summarized.Secondly,the main research results of important peer grading activities,such as grader allocation,comment analysis,abnormal peer grading information detection and processing,true grade estimation of subjective question assignments,were explained.Thirdly,the peer grading functions of representative online education platforms and published teaching systems were compared.Finally,the future development trends of peer grading was summed up and prospected,thereby providing reference for people who are engaged in or intend to engage in peer grading research.
作者
许嘉
刘静
于戈
吕品
杨攀原
XU Jia;LIU Jing;YU Ge;LYU Pin;YANG Panyuan(School of Computer,Electronics and Information,Guangxi University,Nanning Guangxi 530004,China;Guangxi Key Laboratory of Multimedia Communications and Network Technology(Guangxi University),Nanning Guangxi 530004,China;School of Computer Science and Engineering,Northeastern University,Shenyang Liaoning 110169,China)
出处
《计算机应用》
CSCD
北大核心
2022年第12期3913-3923,共11页
journal of Computer Applications
基金
国家自然科学基金资助项目(62067001,U1811261)
“广西八桂学者”专项
广西自然科学基金资助项目(2019JJA170045)
广西高等教育本科教学改革工程项目(2020JGA116)
广西研究生教育创新计划项目(JGY2021003)。
关键词
在线教育
同伴互评
真实分数估计
评语分析
评价者分配
异常检测
online education
peer grading
true grade estimation
comment analysis
grader allocation
anomaly detection