Group work is widespread in education. The growing use of online tools supporting group work generates huge amounts of data. We aim to exploit this data to support mirroring: presenting useful high-level views of information about the group, together with desired patterns characterizing the behavior of strong groups. The goal is to enable the groups and their facilitators to see relevant aspects of the group's operation and provide feedback if these are more likely to be associated with positive or negative outcomes and indicate where the problems are. We explore how useful mirror information can be extracted via a theory-driven approach and a range of clustering and sequential pattern mining. The context is a senior software development project where students use the collaboration tool TRAC. We extract patterns distinguishing the better from the weaker groups and get insights in the success factors. The results point to the importance of leadership and group interaction, and give promising indications if they are occurring. Patterns indicating good individual practices were also identified. We found that some key measures can be mined from early data. The results are promising for advising groups at the start and early identification of effective and poor practices, in time for remediation.

Academic writing, individual or collaborative, is an essential skill for today's graduates. Unfortunately, managing writing activities and providing feedback to students is very labor intensive and academics often opt out of including such learning experiences in their teaching. We describe the architecture for a new collaborative writing support environment used to embed such collaborative learning activities in engineering courses. iWrite provides tools for managing collaborative and individual writing assignments in large cohorts. It outsources the writing tools and the storage of student content to third party cloud-computing vendors (i.e., Google). We further describe how using machine learning and NLP techniques, the architecture provides automated feedback, automatic question generation, and process analysis features.

The teacher has very important roles in the classroom, particularly as manager of most resources for learning activities and in providing timely feedback that can enhance learning. But teachers need to be aware of students' achievements and weaknesses to decide how to time feedback. We present MTFeedback, a system that harnesses the new affordances of interactive tabletops to generate automatic notifications about small group collaborative tasks for the teacher in real-time. We deployed the system on a teacher's hand-held dashboard, which supports orchestration of a multi-tabletop environment, the MTClassroom. We validated our approach in authentic (in-the-wild) classroom activities, with 95 higher education students and three teachers across two sets of six classroom sessions. We evaluated the impact of presenting notifications on feedback that teachers provided to students. The notifications were based on qualitative comparisons of students' artefacts against a representation of both expert knowledge and a set of common misconceptions. We demonstrate that our approach can successfully be deployed in the classroom to generate notifications that help the teacher direct their attention more effectively to provide relevant feedback to their students in small group learning activities.