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.

通过自然语言处理和BERT模型的深层迁移学习方法，开发了一种自动化评估学生团队合作能力的方法，该方法能够更全面地理解文本的语境以及在不同团队聊天背景和团队成员群体中使用语言的潜力，从而为团队合作评估和反馈提供了改进的学习分析工具。

使用BERT进行团队合作维度分类