Systematic Review of Approaches to Improve Peer Assessment at Scale

01/27/2020
by   Manikandan Ravikiran, et al.
0

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.

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

01/27/2020

What's happened in MOOC Posts Analysis, Knowledge Tracing and Peer Feedbacks? A Review

Learning Management Systems (LMS) and Educational Data Mining (EDM) are ...
01/30/2021

Can We Automate Scientific Reviewing?

The rapid development of science and technology has been accompanied by ...
03/07/2018

An Application of HodgeRank to Online Peer Assessment

Bias and heterogeneity in peer assessment can lead to the issue of unfai...
03/25/2019

Argument Mining for Understanding Peer Reviews

Peer-review plays a critical role in the scientific writing and publicat...
01/19/2021

Leveraging Peer Review in Visualization Education: A Proposal for a New Model

In visualization education, both science and humanities, the literature ...
01/27/2022

Calibration with Privacy in Peer Review

Reviewers in peer review are often miscalibrated: they may be strict, le...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.