DigiTeachVR: Digitally-Enhanced Teaching Platform for Improving Data Science Skills and Virtual Reality Competences in Cross-Disciplinary Engineering Education

Abstract

Integrated factory design (IFD) requires cross-disciplinary competences obtained inter alia from areas of industrial, mechanical, civil engineering and architecture as well as operations, assets and facility management. However, traditional teaching practices in higher education could neither effectively provide solutions to cross-disciplinary competencies for IFD nor supply evolving job market demands (aka skill gaps). Information and Communication Technologies (ICT) are pivotal in this transformation, shaping smart learning environments that integrate collaborative activities and harness freely available online tools and cloud services. However, challenges persist in cross-disciplinary fields like IFD, where data science and visualization technologies like Virtual Reality (VR) are increasingly applied but often not fully integrated into traditional curricula. To address this educational gap, this paper introduces the digiTeachVR platform, which incorporates VR and data science into a collaborative teaching platform designed for IFD. Undergraduate and graduate students from diverse disciplines participate in a parametric design course that leverages industrial data science and VR within an interactive, self-paced, collaborative environment. The digiTeachVR's approach involves self-taught programming lessons followed by collaborative programming on Python. This is to develop parametric designs, subsequently transformed into VR models using Rhino 3D. A pilot study of digiTeachVR at the TU Wien indicates a significant (79%) increase in student enthusiasm, suggesting improved content delivery and enhanced learning outcomes (63%). The paper presents the approach, summarizes the key achievements and identifies the future research pathways for enhancing quality of cross-disciplinary engineering education.