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Background: Maintaining cohesion is critical for teams to achieve shared goals and performance outcomes within a work-integrated learning (WIL) environment. Cohesion is an emergent state that develops over time, representing the synchrony of different behavioural interactions. Cohesive teams will exhibit such phenomena by their temporal coordination of micro-level relations. Objectives: The primary aim of this study is to examine the cohesion of teams in learning environments using a learning analytics approach. Method: This study examines teams from higher education who participate in a WIL environment platform working in teams to develop their collaborative problem-solving skills. Here we show that temporal network motifs can be used as a proxy to measure cohesion. Results and Conclusions: We illustrate three clusters represented by team learning behaviours and found that each cluster has distinctive interactions with learning resources, performance outcomes, temporal network motif group characteristics and emergence over time using learning analytics. Implications: Applying temporal motifs as an analytics-based measure of cohesion is a starting point for understanding how cohesion develops over time without relying on surveys. We anticipate that the same approaches can be applied in most learning management systems containing trace data of teams and their interactions with learning resources to understand cohesion.

This document is a guest editorial from the journal "Enterprise Modelling & Information Systems Architectures" discussing the convergence of enterprise modeling and knowledge graphs. It emphasizes the need to reconnect diagrammatic enterprise modeling and engineering-oriented knowledge structuring. The editorial explores the use of knowledge graphs in enterprise modeling and introduces three papers that showcase different views on how enterprise models and ontologies or semantic graphs can support each other. The document aims to demonstrate the growing research on the convergence between enterprise modeling and knowledge graphs. Additionally, there is a list of references for articles and papers related to enterprise modeling, knowledge graphs, and ontology-driven conceptual modeling, which can provide further insights into these topics for library patrons conducting research. [Extracted from the article]

Cultural heritage archiving is experiencing an increase in digitalisations of artefacts in the last 15 years. The reason behind this trend is a demand for providing information about the artefact in a more accessible way to the audience, for example, through online delivery or virtual reality. Other reasons might be to simplify and automate the management of artefacts. Having a 'digital copy' of artefacts, allows one to search an archive and plan its storage and dissemination in a comprehensive manner. With the increased digitalisation comes an increased use of artificial intelligence [AI] applications. AI can be very beneficial in classifying artefacts automatically through machine learning [ML] and natural language processing [NLP]. For example, an algorithm can identify the source and age of artefacts based on an image and can do this much faster for a large collection of photos than a human. Although AI provides many benefits, it also presents challenges: Sophisticated AI techniques require certain insights on how they work, need specialists to customise a solution, and require an existing large dataset to train an algorithm. Another challenge is that typical AI techniques are regarded as black boxes, which means they decide, but it is not obvious why a decision has been made. This chapter describes a project in collaboration with the South Australian Museum [SAM] on the application of AI to extract material lists from a description of artefacts. A large dataset to train an algorithm did not exist, and hence, a customised approach was required. The outcome of the project was the application of NLP in combination with easy-to-customise rules that can be applied by non-IT specialists. The resulting prototype achieved the extraction of materials from a large list of artefacts within seconds and a flexible solution that can be applied on other collections in the future. [ABSTRACT FROM AUTHOR]

Background: Maintaining cohesion is critical for teams to achieve shared goals and performance outcomes within a work‐integrated learning (WIL) environment. Cohesion is an emergent state that develops over time, representing the synchrony of different behavioural interactions. Cohesive teams will exhibit such phenomena by their temporal coordination of micro‐level relations. Objectives: The primary aim of this study is to examine the cohesion of teams in learning environments using a learning analytics approach. Method: This study examines teams from higher education who participate in a WIL environment platform working in teams to develop their collaborative problem‐solving skills. Here we show that temporal network motifs can be used as a proxy to measure cohesion. Results and Conclusions: We illustrate three clusters represented by team learning behaviours and found that each cluster has distinctive interactions with learning resources, performance outcomes, temporal network motif group characteristics and emergence over time using learning analytics. Implications: Applying temporal motifs as an analytics‐based measure of cohesion is a starting point for understanding how cohesion develops over time without relying on surveys. We anticipate that the same approaches can be applied in most learning management systems containing trace data of teams and their interactions with learning resources to understand cohesion. Lay Description: What is already known about this topic?: The development of team cohesion has been widely studied for decades in face‐to‐face educational contexts but remains underexplored in online environmentsObtrusive tools, such as questionnaire instruments are still the primary method used to examine team cohesionTeam cohesion is an emergent state that changes over time, representing the synchrony of different behavioural interactionsResearch that explores and examines the temporality of cohesion as it unfolds in team learning is limited What this paper adds?: Underlines how emerging team behaviours are characterised by their engagement, performance and cohesion development using temporal network motifsPresents an analytics‐based measure to examine cohesion using temporal network motifs as a proxyProvides a learning analytics approach for researchers and instructors to evaluate cohesion in online learning environments through online learner trace data The implications of study findings for practitioners: The paper provides insights into distinct teams with emerging learning behaviours, performance associations and cohesion in an online learning platform.The paper provides instructors with a way to monitor and support a team's cohesion over time by examining the way temporal motifs occur.The paper offers a new avenue to understanding learner engagement beyond summarisations [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ahj.2003.11.006 Byline: Georg Grossmann (a), Albrecht Hoffmeister (a), Armin Imhof (a), Martin Giesler (a), Vinzenz Hombach (a), Jochen Spiess (a) Abstract: The follow-up of patients with mitral and tricuspid regurgitation is important for their clinical treatment. We aimed to evaluate the reproducibility of the flow convergence method in mitral and tricuspid regurgitation. Author Affiliation: (a) Department of Internal Medicine, Division of Cardiology, University of Ulm, Ulm, Germany Article History: Received 23 June 2003; Accepted 6 November 2003

Multi-level modeling is currently regaining attention in the database and software engineering community with different emerging proposals and implementations. One driver behind this trend is the need to reduce model complexity, a crucial aspect in a time of analytics in Big Data that deal with complex heterogeneous data structures. So far no standard exists for multi-level modeling. Therefore, different formalization approaches have been proposed to address multi-level modeling and verification in different frameworks and tools. In this article, we present an approach that integrates the formalization, implementation, querying, and verification of multi-level models. The approach has been evaluated in an open-source F-Logic implementation and applied in a large-scale data interoperability project in the oil and gas industry. The outcomes show that the framework is adaptable to industry standards, reduces the complexity of specifications, and supports the verification of standards from a software engineering point of view. [ABSTRACT FROM AUTHOR]

In large organizations, multiple stakeholders may modify the same business process. This paper addresses the problem when stakeholders perform changes on process views which become inconsistent with the business process and other views. Related work addressing this problem is based on execution trace analysis which is performed in a post-analysis phase and can be complex when dealing with large business process models. In this paper, we propose a design-based approach that can efficiently check consistency criteria and propagate changes on-the-fly from a process view to its reference process and related process views. The technique is based on consistent specialization of business processes and supports the control flow aspect of processes. Consistency checks can be performed during the design time by checking simple rules which support an efficient change propagation between views and reference process. [ABSTRACT FROM AUTHOR]