Collaboration among students is reported to improve their learning and academic performance. Academic students spend much time on out-of-class assignments in a variety of different environments. A popular choice is a library that offers designated workspaces for students, either for individual or group work. Studies show that a strong correlation exists between efficient learning spaces and interior space attributes. Spatial design affects human parameters such as behavior, experience, and cognitive functions. So far, it has been a challenge to determine the effectiveness of space on student collaboration. This study demonstrates how advanced sensors can determine the effectiveness of specific space attributes on group collaboration among academic students. Experiments were conducted over 12 days with 396 academic students working in groups. In total, 159 groups were involved, divided into sizes of two, three, or four. Technologically, this research differs from previous studies by significantly reducing or eliminating the need for users to participate in the study actively. Discrete non-invasive depth sensors offer micro-level analysis of all users entering the designated test area. As all measured data are not personally sensitive, the method complies with general data protection regulations, and the need for user consent is avoided. As a result, the study demonstrates a novel method to be used by both environmental psychologists and architects to understand and quantify the level of group collaboration as well as to correlate it with different space attributes. In addition, the case study presents statistically significant findings of specific space attributes that enable group collaboration. Two different approaches were used to validate the outcome of the method, 1: 19 hours of ethnographic monitoring of the group’s collaboration level, 2: 142 group members’ assessment of internal collaboration level.
Andrew Khoudi is a third-year industrial Ph.D. student at Aarhus School of Architecture, Denmark, and industrial sponsor Schmidt Hammer Lassen Architects and Soren Jensen Consulting Engineers. The candidate’s academic background is within signal processing, and before commencing the Ph.D. program, Andrew Khoudi spends nearly ten years in the industry. Initially, Andrew Khoudi joined a consulting engineering company for six years. The main task was planning and designing large Danish hospitals. This was done in close collaboration with architects, which allowed the candidate to establish an in-depth understanding of the architects’ work methods and culture. Afterward, four years were spent at a tech start-up developing sensors and analyzing data to quantify human behavior in indoor spaces to improve the user experience. Primary spaces to be monitored and analyzed were learning environments such as universities and public schools. As an alternative Ph.D. student within the architectural environment, the candidate aims to research methods making the architectural design process of indoor spaces more data-driven. The main focus of the research is to enable the use of sensors and algorithms to extract information on human behavior and how to improve the user experience by adjusting the interior space design.