Wei, S., & Wang, R., & Ohland, M. W., & Nanda, G. (2020, June), Work in Progress: Automating Anonymous Processing of Peer Evaluation Comments  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35615

\bibitem{asee_peer_35615}
  Wei, S., \& Wang, R., \& Ohland, M. W., \& Nanda, G. (2020, June), \emph{Work in Progress: Automating Anonymous Processing of Peer Evaluation Comments}
  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line .
  10.18260/1-2--35615

Siqing Wei, Rui Wang, Matthew W. Ohland, and Gaurav Nanda.  "Work in Progress: Automating Anonymous Processing of Peer Evaluation Comments".  2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June.  ASEE Conferences, 2020.  https://peer.asee.org/35615 Internet.  18 Apr, 2024

\bibitem{asee_peer_35615}
  Siqing Wei, Rui Wang, Matthew W. Ohland, and Gaurav Nanda.
  "Work in Progress: Automating Anonymous Processing of Peer Evaluation Comments".
  \emph{2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June}.
  ASEE Conferences, 2020.
  https://peer.asee.org/35615 Internet.  18 Apr, 2024

@INPROCEEDINGS{asee_peer_35615
author = "Siqing Wei, Rui Wang, Matthew W. Ohland, and Gaurav Nanda"
title = "Work in Progress: Automating Anonymous Processing of Peer Evaluation Comments"
booktitle = "2020 ASEE Virtual Annual Conference Content Access"
year = "2020"
month = "June"
address = "Virtual On line "
publisher = "ASEE Conferences"
note = {https://peer.asee.org/35615}
number = {10.18260/1-2--35615}
}

TY  - CPAPER
AB  - De-identifying original qualitative datasets is time-consuming and expensive but required to protect the private information of studied subjects. Peer-to-peer comments are very likely to be collected by instructors as one form of peer evaluation in team-based learning courses. Those comments are essential and valuable research data for educational study to investigate but they usually contain identifiable entities, such as names. In this work in progress, we study and propose a pipeline tool to identify all names appearing in CATME team peer evaluation comments and replacing those names with pseudonyms such as Rater 1 and Rater 2. We explored several natural language processing techniques empowered by machine learning methods and then optimized to the final algorithm. At its core, the algorithm combines the long short-term memory (LSTM) and conditional random field (CRF) approaches most common in the field of named entity recognition. The current algorithm performs well, with a high recall of 0.8 with reasonable precision scores resulting in 76 of F_1 score as we want to put an emphasis on recalls. We also propose this as a tool to make a large amount of data available for research that would otherwise be sensitive due to personally identifiable information. 
AU  - Siqing Wei
AU  - Rui Wang
AU  - Matthew W. Ohland
AU  - Gaurav Nanda
CY  - Virtual On line 
DA  - 2020/06/22
PB  - ASEE Conferences
TI  - Work in Progress: Automating Anonymous Processing of Peer Evaluation Comments
UR  - https://peer.asee.org/35615
DO  - 10.18260/1-2--35615
ER  -