Natural Language Processing System for Self-Reflection and Peer-Evaluation


Peer evaluation has been well established as an effective method to motivate team members to reflect their contribution and performance, to enforce sense of responsibility, to act as an incentive for demonstrating good interpersonal skills and to help the team achieve its goals. Behaviorally anchored rating scale is generally considered as an efficient and fair method to measure certain scores. However, in the application of peer evaluation, numerical rating could be influenced by raters’ biased understanding of the scale based on their cultural background. Supplementing peer-to-peer comments with numerical peer evaluation system could remediate rater bias effect. In this paper, we propose a natural language processing model that (1) processes the peer-to-peer comments about rater’s teammates’ teamwork behaviors; (2) converts comments into numerical space that allow for computation We evaluate our results against CATME data and validate our proposed system.

Proceedings of the International Conference on Industrial Engineering and Operations Management