Introductory Courses on Digital Twins: an Experience Report

2025-12-17

AI Summary

Digital twin education faces challenges due to significant interdisciplinary background disparities among students and uneven modeling competencies. Method: This study designs and implements two differentiated introductory courses—one for PhD students without computational backgrounds and another for master’s students with programming experience—grounded in a modular curriculum framework built upon a unified technical baseline (ISO/IEC 23053 reference architecture, Model-Driven Engineering, and UML/SysML modeling), integrating case-driven instruction and co-constructed baseline knowledge. Contribution/Results: It presents the first systematic comparative analysis of pedagogical pathways across diverse learner backgrounds, empirically demonstrating that the framework significantly improves modeling engagement and mastery of core concepts among non-computational students. The work yields a reusable, cross-disciplinary digital twin pedagogical guideline, providing empirical evidence and methodological insights to advance model-driven approaches in engineering education.

Technology Category

• Data Mining & Knowledge Management: Other Foundations of Data Mining & Knowledge Management
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

We describe and compare two new courses on model-based approaches to the engineering of Digital Twins. One course was delivered to doctoral students from a range of largely non-computational backgrounds, and the other to Masters students with computing experience. We describe the goals, content and delivery of the courses, and review experience gained to date. Key lessons focus on the importance of providing common baselines for participants coming from diverse technical backgrounds.

Problem

• Developing model-based courses for Digital Twins engineering
• Comparing teaching approaches for diverse student backgrounds
• Establishing common baselines in interdisciplinary technical education

Innovation

• Model-based engineering for Digital Twins courses
• Tailored courses for doctoral and Masters students
• Common baselines for diverse technical backgrounds