Keywords: Model repository, Dataset, UML, Open models, Curation, Community, Education, Machine learning
Astract: Datasets of Unified Modeling Language (UML) models are becoming increasingly valuable for education, empirical research, and tool development in model-driven engineering (MDE) and conceptual modeling. In recent years, several datasets have emerged - mostly compiled through automated crawling of open platforms such as GitHub and GenMyModel. While these efforts have improved access to real-world modeling artifacts, the resulting collections often suffer from serious quality issues: they include syntactically invalid models, semantically incorrect structures, and placeholder or dummy content. Moreover, most models are not accompanied by textual domain descriptions, which are essential for understanding the intent behind the model and assessing its semantic soundness. Therefore these model datasets are far from ideal as a source for modeling exercises or empirical MDE research. This paper presents an initial step toward a community-curated golden dataset of UML models, designed to address these limitations. Our contribution includes i) a curated set of UML models, each paired with a natural language description of the modeled domain requirements, ii) a publicly accessible web platform for exploring and querying the dataset, and iii) a structured process for community-based contribution and evaluation to support sustainable growth and quality assurance of the dataset. By fostering community involvement and providing high-quality, semantically grounded models, this work lays the foundation for a widely accepted benchmark dataset in UML-based research and education.

Keywords: design science, framework, maturity assessment, MBSE adoption
Astract: As engineering systems become increasingly complex, organizations must adopt strategic approaches to manage the interdependencies of their processes, tools, and teams. Model-Based Systems Engineering (MBSE) offers a promising solution, but transitioning from a traditional SE approach to MBSE is a complex endeavor that requires significant organizational change. This paper addresses the need for structured guidance in this process by proposing a maturity assessment framework that supports organizations in navigating this transition. The proposed framework is developed using a design science based approach and identifies key challenges, pitfalls, and best practices that are organized into several maturity levels of MBSE adoption. This structured, high-level approach provides organizations with the tools to understand their current maturity level, prioritize efforts, and avoid common missteps. The framework allows organizations to tailor the insights to their unique context, ensuring practical applicability. It emphasizes the importance of leadership, cultural readiness, technical tools, workforce development, and modeling practices for successful MBSE implementation.

Keywords: Conceptual Modeling Education, Learning Analytics Dashboards, Social Comparison
Astract: Conceptual modeling education benefits from technological support due to the complex nature of the learning processes required to master modeling skills. Along with existing modeling and prototyping tools, providing feedback to students using Learning Analytics Dashboards (LADs) can enhance their learning experience. To interpret LADs, students are provided with a frame of reference, often peer comparison, although its effectiveness is debated. This study presents two LADs used to provide feedback to students from diverse backgrounds enrolled in a conceptual modeling course: a default-LAD with mastery and progress reference frames, and an extended peer-LAD that also includes a performance reference frame. We examine students’ preferences for LAD visuals, the relationship between their study activity and performance, and the relationship between the use patterns of different LAD versions and student activity and performance. The results show that most of the relationships are significant only for the peer-enhanced LAD and are stronger for students with less modeling experience, underscoring the value of peer LADs for novice modelers.

Keywords: Collaboration, Conceptual Modeling, Experiment, Model Visualization, Participatory Enterprise Modeling
Astract: Participatory modeling is considered more effective for creating higher-quality enterprise models with broader stakeholder acceptance compared to traditional approaches. However, involving stakeholders directly requires more time and effort. To elaborate on the benefits, we conducted an experiment to compare the outcome of participatory enterprise modeling and traditional modeling, e.g., by interviewing stakeholders separately. We let groups of participants work in three different settings, varying the possibility of collaborating and working on a preliminary model. Considering the different conditions, we addressed the following research questions: 1) Do the models differ in size? 2) How well-elaborated are the models in terms of connections made between the elements? 3) Are the contributions of the various participants linked differently across the conditions? We found that collaboration slows down the process, which results in smaller models. Collaboration, however, leads to models that are better integrated. We found no evidence that visualization significantly supports the modeling.