Analysis of Collaboration Dynamics in Hackathons: Combining Quantitative and Qualitative Methods
Name
Karl Rapur
Abstract
Hackathons are intensive, time-bounded events where teams collaborate to solve specific problems or develop innovative artifacts. Over the past years, researchers have primarily used qualitative instruments (e.g., observations and interviews) to study these events. However, these instruments come with inherent limitations. Scalability issues hinder current data collection methods in the context of large hackathons, as they require significant human resource allocation. Furthermore, current methods have limited potential for live feedback, which is necessary for hackathon organizers.
This study demonstrates a combination of qualitative and quantitative data collection instruments to study collaboration dynamics. A case study was conducted during a hackathon with multiple teams, where data was gathered by combining conventional methods with smart badges and sensors. The study uncovered varied collaboration dynamics influenced by experienced participants, constellation changes, language barriers, goal clarity, adaptability, and mentorship. Badge-collected data provided insights into speaker transition logs, speech patterns, transcriptions, and spatial awareness.
As contemporary research has primarily relied on qualitative data collection instruments with inherent limitations, this study showcased the possibilities of using sensors and badges to fill this gap. Therefore, it lays the groundwork for future work to leverage such technology to overcome limitations, study hackathons on a larger scale, and provide live feedback to organizers.
This study demonstrates a combination of qualitative and quantitative data collection instruments to study collaboration dynamics. A case study was conducted during a hackathon with multiple teams, where data was gathered by combining conventional methods with smart badges and sensors. The study uncovered varied collaboration dynamics influenced by experienced participants, constellation changes, language barriers, goal clarity, adaptability, and mentorship. Badge-collected data provided insights into speaker transition logs, speech patterns, transcriptions, and spatial awareness.
As contemporary research has primarily relied on qualitative data collection instruments with inherent limitations, this study showcased the possibilities of using sensors and badges to fill this gap. Therefore, it lays the groundwork for future work to leverage such technology to overcome limitations, study hackathons on a larger scale, and provide live feedback to organizers.
Graduation Thesis language
English
Graduation Thesis type
Master - Software Engineering
Supervisor(s)
Alexander Nolte
Defence year
2024