How Covid-19 Enabled a Global Student Design Team to Achieve Breakthrough Innovation

Abstract

This is a qualitative single case study of a geographically distributed student team that experienced a quite different graduate course, compared to previous year's. This was due to the restrictions placed upon them following coronavirus lockdowns. With already ongoing research, and continuous development of the course, the authors had documented individual reflections and identified patterns and behaviours that seemingly determined the quality of the end result, as well as the students expectations and experiences. Semi-structured interviews, surveys and the author's individual reflection notes were already in place as part of the larger research scope and when the student team during the covid-19 year showed unexpected performance and results, the authors decided to pause the larger research scope and focus on this unique single case and capture those learnings. Not knowing how the Covid-19 situation evolves and leaning on insights from previous years, as well as this unique year, the aim with this paper is to describe the unique Covid-19 year amd share knowledge that can help improve and evolve the development of this longlived collaborative graduate student course, and other similar distributed team contexts.

Impact of Autonomous Solutions on Electric Earthmoving Design Using Machine Learning: Case Study

“…For passenger cars, the major motivations are user experience and environment, whereas in freight transport and construction equipment, the subject of this study, the driving forces are productivity and profitability. The Covid-19 pandemic provided an opportunity for organizations to change "business as usual" and engage in innovation and transformation (Elfsberg et al, 2021), and underlined the importance of ADS in keeping human safe and avoiding production interruptions. Along the construction and mining value chain, AI is predicted to trigger significant changes, and the disruption potential is highest in areas that are characterized by repetitive, known tasks with limited uncertainty (Schober, 2020).…”