TY - JOUR
T1 - Applications of artificial intelligence in peer assessment
AU - Gehringer, Edward F.
AU - Pramudianto, Ferry
AU - Medhekar, Abhinav
AU - Rajasekar, Chandrasekar
AU - Xiao, Zhongcan
N1 - Publisher Copyright:
© American Society for Engineering Education, 2018.
PY - 2018/6/23
Y1 - 2018/6/23
N2 - Peer assessment has at least a 50-year history in academia, and online applications for peer assessment have been available for more than 20 years. Until recently, online applications simply transmitted classmates' feedback to each other. But in the past decade, facilities have been incorporated to automatically recognize good reviews. This helps authors know which suggestions to follow and helps reviewers improve their reviews. It can also aid in assigning peer grades. Several types of data can be used to determine review quality. These metrics can be combined using machine-learning and neural-network models to produce better estimates of review quality, and hence better estimates of the quality of reviewed work. This paper discusses past work in automatically assessing reviews, and summarizes our current efforts to build on that work.
AB - Peer assessment has at least a 50-year history in academia, and online applications for peer assessment have been available for more than 20 years. Until recently, online applications simply transmitted classmates' feedback to each other. But in the past decade, facilities have been incorporated to automatically recognize good reviews. This helps authors know which suggestions to follow and helps reviewers improve their reviews. It can also aid in assigning peer grades. Several types of data can be used to determine review quality. These metrics can be combined using machine-learning and neural-network models to produce better estimates of review quality, and hence better estimates of the quality of reviewed work. This paper discusses past work in automatically assessing reviews, and summarizes our current efforts to build on that work.
KW - Convolutional neural networks
KW - Natural language processing
KW - Peer assessment
KW - Peer feedback
KW - Peer review
KW - Tensorflow
UR - http://www.scopus.com/inward/record.url?scp=85051223054&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85051223054
SN - 2153-5965
VL - 2018-June
JO - ASEE Annual Conference and Exposition, Conference Proceedings
JF - ASEE Annual Conference and Exposition, Conference Proceedings
T2 - 125th ASEE Annual Conference and Exposition
Y2 - 23 June 2018 through 27 December 2018
ER -