论文标题
系统评价大规模改善同行评估的方法
Systematic Review of Approaches to Improve Peer Assessment at Scale
论文作者
论文摘要
同行评估是对同龄人对学生写作的分析和评论的任务，是校园和MOOC中所有教育组成部分的核心。但是，随着MOOC及其固有的个性化开放式学习的巨大规模，自动分级和工具在大规模上有助于分级非常重要。以前，我们介绍了有关后分类，知识追踪任务的调查，并以对同行评估（PA）的简要审查结束，但有一些初步问题。在这篇评论中，我们将从改善审核过程本身的角度继续对PA进行审查。因此，本综述的其余部分集中在PA的三个方面，即自动评估和同行评估工具（我们将仅考虑如何进行同行评审/自动分级），处理流氓评论的策略，使用自然语言处理的同行评审改进。如此使用的合并论文和资源集在https://github.com/manikandan-ravikiran/cs6460-survey-2中发布。
Peer Assessment is a task of analysis and commenting on student's writing by peers, is core of all educational components both in campus and in MOOC's. However, with the sheer scale of MOOC's & its inherent personalised open ended learning, automatic grading and tools assisting grading at scale is highly important. Previously we presented survey on tasks of post classification, knowledge tracing and ended with brief review on Peer Assessment (PA), with some initial problems. In this review we shall continue review on PA from perspective of improving the review process itself. As such rest of this review focus on three facets of PA namely Auto grading and Peer Assessment Tools (we shall look only on how peer reviews/auto-grading is carried), strategies to handle Rogue Reviews, Peer Review Improvement using Natural Language Processing. The consolidated set of papers and resources so used are released in https://github.com/manikandan-ravikiran/cs6460-Survey-2.
