Investigating eXtended Reality-powered Digital Twins for Sequential Instruction Learning: the Case of the Rubik's Cube

• Main Authors: Hajahmadi, Shirin, Stacchio, Lorenzo, Giacche, Alessandro, Cascarano, Pasquale, Marfia, Gustavo
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; Cognitive load; Correlation; Digital Twin; Digital twins; Extended reality; Real-time systems; Sensors; Sequential Learning; Statistical analysis; Training Tools; Usability; Visualization
• ISSN: 2473-0726
• DOI: 10.1109/ISMAR62088.2024.00040

Educational practices are increasingly experimenting with eXtended Reality (XR) paradigms to offer novel opportunities for boundaryless learning experiences with real-time interactions in immersive environments. Digital Twins (DT) are also gaining traction in this field to facilitate personalized learning experiences. However, a still unexplored space in learning frameworks amounts to the one where XR intersects with DTs. This work wants to move a step in such a direction with the design, implementation, and test of a DT-driven XR framework to learn procedural tasks. The framework offers three distinct learning modalities where virtual and physical interactions enhance learning retention by engaging users actively in digital and real-world environments. We contextualize such a framework for procedural task learning through one of its pivotal use cases: learning Rubik's Cube notations. To evaluate and compare the effectiveness of these modalities, we perform an experimental user campaign evaluating short-term skill retention, performance accuracy, usability, and cognitive load of each of them. We then provide an extensive statistical analysis to compare each kind of guidance while analyzing correlations between the examined variables, offering insights into optimizing instructional methodologies within XR-based educational frameworks.