Digital twins are increasingly used across a wide range of industries. Modeling is a key to digital twin development—both when considering the models which a digital twin maintains of its real‐world complement (“models in digital twin”) and when considering models of the digital twin as a complex (software) system itself. Thus, systematic development and maintenance of these models is a key factor in effective and efficient digital twin development, maintenance, and use. We argue that model‐driven engineering (MDE), a field with almost three decades of research, will be essential for improving the efficiency and reliability of future digital twin development. To do so, we present an overview of the digital twin life cycle, identifying the different types of models that should be used and re‐used at different life cycle stages (including systems engineering models of the actual system, domain‐specific simulation models, models of data processing pipelines, etc.). We highlight some approaches in MDE that can help create and manage these models and present a roadmap for research towards MDE of digital twins.

Team cohesion is critical in driving successful outcomes for teams in collaborative learning settings. It shapes team behaviour, fostering shared perceptions, group synchrony and a common goal-oriented approach. This affinity becomes evident in dynamic interactions, offering insights into team behaviour through interaction data analysis. Interpreting interaction data proves complex, hampering our understanding and insights into shared team perceptions and task cohesion development. This paper used temporal motif analysis to examine the changes in team members’ cohesive perceptions and behaviours, including task cohesion, performance outcomes, engagement and group synchrony. Trace data from an online work-integrated learning environment captured learning behaviours, while responses to a questionnaire at different stages of a study program captured task cohesion and cohesive perceptions. The findings reveal teams with strong task cohesion and high performance tend to share similar cohesive perceptions driven by interdependent interactions. Conversely, teams with different cohesion perceptions have lower interaction interdependence and poorer performance. Through analysing team interaction data, this study uncovered key insights to promote positive adjustments aligning team perceptions, enhancing collaborative learning and offering support for improved performance, engagement and synchrony among teams, ultimately benefiting learning outcomes and the cultivation of skills and competencies.