(How) Can Machine Learning Support Teaching Staff in a Virtual Collaborative Learning Environment?

Purpose: Supervising roles within virtual collaborative learning (VCL) environments face many different challenges and are often having difficulties keeping an eye on everything and comprehending every participant’s or group’s workflow. To help supervisors with problems like that we developed a software prototype to support their daily workflow and overcome the mentioned challenges. Study design/methodology/approach: This study used a design science research approach to investigate how learning analytics might be able to support teaching staff in VCL settings. Previous studies demonstrated that this approach is very well suited to derive design guidelines for such software artifacts. Initially, a qualitative interview series with four experienced tutors was carried out to get an in-depth understanding of the challenges and tasks teaching staff typically faces in VCL environments. The interview material was systematically analysed to acquire the underlying software requirements. These were then combined with existing knowledge about support software of similar use cases to create a first prototype, primarily based on a supervised machine learning model that classifies online mess