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Project-based Learning (PBL) provides an effective environment for collaborative engineering design education. However, it is difficult to assess students' engagement and provide process-oriented feedback on their collaboration due to limited resources and scalability challenges. This paper presents an empirical study examining the application of Machine Learning (ML) to evaluate student engagement in PBL. A supervised ML model (SML) was developed using Natural Language Processing (NLP) to predict engagement levels using conversation data in online chat groups and comparing them to the ground truth metrics. Eleven teams of engineering students participated in the PBL courses on systems engineering while employing text-based discussions. Communication messages were manually labelled for active engagement metrics collected in our prior research. The SML was constructed with Gradient …