Gehringer, E. F., & Pramudianto, F., & Medhekar, A., & Rajasekar, C., & Xiao, Z. (2018, June), Board 62: Applications of Artificial Intelligence in Peer Assessment  Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--30073

\bibitem{asee_peer_30073}
  Gehringer, E. F., \& Pramudianto, F., \& Medhekar, A., \& Rajasekar, C., \& Xiao, Z. (2018, June), \emph{Board 62: Applications of Artificial Intelligence in Peer Assessment}
  Paper presented at 2018 ASEE Annual Conference \& Exposition , Salt Lake City, Utah.
  10.18260/1-2--30073

Edward F. Gehringer, Ferry Pramudianto, Abhinav Medhekar, Chandrasekar Rajasekar, and Zhongcan Xiao.  "Board 62: Applications of Artificial Intelligence in Peer Assessment".  2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah, 2018, June.  ASEE Conferences, 2018.  https://peer.asee.org/30073 Internet.  19 Apr, 2024

\bibitem{asee_peer_30073}
  Edward F. Gehringer, Ferry Pramudianto, Abhinav Medhekar, Chandrasekar Rajasekar, and Zhongcan Xiao.
  "Board 62: Applications of Artificial Intelligence in Peer Assessment".
  \emph{2018 ASEE Annual Conference \& Exposition , Salt Lake City, Utah, 2018, June}.
  ASEE Conferences, 2018.
  https://peer.asee.org/30073 Internet.  19 Apr, 2024

@INPROCEEDINGS{asee_peer_30073
author = "Edward F. Gehringer, Ferry Pramudianto, Abhinav Medhekar, Chandrasekar Rajasekar, and Zhongcan Xiao"
title = "Board 62: Applications of Artificial Intelligence in Peer Assessment"
booktitle = "2018 ASEE Annual Conference \& Exposition "
year = "2018"
month = "June"
address = "Salt Lake City, Utah"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/30073}
number = {10.18260/1-2--30073}
}

TY  - CPAPER
AB  - Peer assessment has at least a 50-year history in academia, and online applications 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.
AU  - Edward F. Gehringer
AU  - Ferry Pramudianto
AU  - Abhinav Medhekar
AU  - Chandrasekar Rajasekar
AU  - Zhongcan Xiao
CY  - Salt Lake City, Utah
DA  - 2018/06/23
PB  - ASEE Conferences
TI  - Board 62: Applications of Artificial Intelligence in Peer Assessment
UR  - https://peer.asee.org/30073
DO  - 10.18260/1-2--30073
ER  -