Teamwork Dimensions Classification Using BERT
This work addresses the need for better formative assessment of student teamwork, though it is incremental as it applies an existing NLP method to a specific educational domain.
The researchers tackled the problem of automatically assessing student teamwork by classifying online chat messages into teamwork dimensions, finding that BERT-based classifiers improved performance over traditional machine learning methods and showed potential for generalizability across different contexts and demographics.
Teamwork is a necessary competency for students that is often inadequately assessed. Towards providing a formative assessment of student teamwork, an automated natural language processing approach was developed to identify teamwork dimensions of students' online team chat. Developments in the field of natural language processing and artificial intelligence have resulted in advanced deep transfer learning approaches namely the Bidirectional Encoder Representations from Transformers (BERT) model that allow for more in-depth understanding of the context of the text. While traditional machine learning algorithms were used in the previous work for the automatic classification of chat messages into the different teamwork dimensions, our findings have shown that classifiers based on the pre-trained language model BERT provides improved classification performance, as well as much potential for generalizability in the language use of varying team chat contexts and team member demographics. This model will contribute towards an enhanced learning analytics tool for teamwork assessment and feedback.