Meta-Analysis of Hackathon Literature in IEEE Xplore Using Affinity Spaces

This research Full Paper conducts a qualitative meta-analysis of hackathon research in IEEE through the lens of Gee's affinity spaces. Hackathon research has resulted in a variety of studies characterizing, adapting, and exploring the hackathon phenomenon in which participants create new projects and technologies to solve problems. Hackathons continue to create valuable learning opportunities within hackathons in the wild, and throughout hackathon adaptations across classrooms, industry, and non-CS fields. Affinity spaces are a community-based framework characterizing spaces in which participants engage at different levels, learn and teach each other regardless of demographics, and are united by a common goal. Affinity spaces resonate deeply with hackathons in the wild for these very attributes as hackathons are spaces where the community gathers to teach and learn from each other alongside their shared aspirations. Additionally, these events encourage participation in a multitude of ways, such as mentorship, presenting, hacking, organizing, and even volunteering. Hackathons have also become a novelty in research, resulting in a growing number of hackathon adaptations. Adaptations leverage a hackathon structure or modify it to meet new goals, not always tech-focused. However, there remains a critical question in preserving the spirit or essence of a hackathon that continues to drive the phenomenon. Do hackathon adaptations still retain these community-supportive attributes, or are they lost in translation? To answer this question, a meta-qualitative analysis is critical. Revisiting past publications is best to identify emerging trends, unite existing work, and encourage research in informed and targeted directions. The meta-qualitative analysis pursued in this work leverages only one database, IEEE Xplore, to briefly explore the literature before engaging in a deeper and longer endeavor across multiple databases. The resultant set of papers was deductively analyzed using codes derived from the affinity spaces framework. Our findings hint at new pathways of study for hackathon research and provide insights into improving hackathon adaptation research.