The paper proposes a solution for a Building Information Modeling (BIM)-enabled Infrastructure Asset Management System (AMS) for road owners. The approach provides asset managers with a strategy for the dynamic use of Information Containers for Linked Document Delivery (ICDDs), considering the requirements of stakeholders across domains in the operational phase. The state of the art shows how information management can be carried out utilizing information containers employing Semantic Web technologies Resource Description Framework (RDF), SPARQL, and R2RML. The key output is developing a web-based platform that implements ICDD containers for asset management. Existing AMS are integrated by using SQL data mapped to RDF-based ontology data in the container. The use of existing domain-specific ontologies for infrastructure in combination with the linkage of domain knowledge to a three-dimensional BIM model is a step beyond the state of the art and practice in the construction industry. Linking inside the container allows for querying data across several information models and ontology-based data to create stakeholder-specific data views. The approach was demonstrated in two use cases. The first was related to the visual inspection of a concrete bridge. The detection of damage and the process of communicating the damage to a contractor charged with the repair were described. The second use case was related to a road pavement and demonstrated how decision-making about maintenance activities can be supported using cross-domain information containers.

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For cooperative learning to be effective, the quality of student–student interaction is crucial. Interactions, which are transactive in nature, are positively related to students’ learning success during cooperative learning. However, little is known about typical interaction patterns during transactive interaction in face-to-face cooperative learning. Therefore, the current study aims to analyze typical interaction patterns of transactive interaction in cooperative learning. Sixty-eight students from seventh to tenth grade were randomly assigned to a total of 23 groups in their classes. The groups were videotaped while solving the same open-ended mathematical modelling task. The interaction behavior was coded, and interaction patterns were analyzed using sequential analysis with first- and second-order Markov chains. The results indicate that the likelihood that students confirm and pick up correct proposals is relatively high, indicating transactive interaction. However, it is almost equally likely that incorrect proposals are confirmed erroneously, as students barely correct them. Still, students do frequently engage in transactive interaction by discussing incorrect proposals, even though these discussions rarely lead to correct solution approaches. Limitations of these results, as well as the practical implications for cooperative learning in classroom settings, are discussed.

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