Evaluating Assessment Practices in Team-Based Computing Capstone Projects

Hooshangi, Sara; Shakli, Asma; Riddle, Steve; Aydin, Ilknur; Nasir, Nayla; Parupudi, Tejasvi; Rehman, Attiqa; Scott, Michael James; Vahrenhold, Jan; Weerasinghe, Amali; Wu, Xi

Zusammenfassung

Team-based capstone projects are vital in preparing computer science students for real-world work by developing teamwork, communication, and industry-relevant technical skills. Their assessment, however, is challenging, requiring alignment between academic criteria and external stakeholder expectations, fair evaluation of individual contributions, recognition of diverse skills, and clarity on external partners' involvement in the evaluation process. The high stakes of these projects further demand transparent and equitable assessment methods that are perceived as fair by all stakeholders. Our working group (WG) addresses the challenges of capstone project assessment by examining the perspectives of instructors, students, and external stakeholders to support fair and effective evaluation. We used a mixed-methods approach combining surveys and in-depth interviews, collecting 66 survey responses and conducted 30 interviews across multiple countries and institutions, capturing diverse global perspectives. Findings revealed commonalities such as the assessed components and challenges of managing non-contributing members, but also clear contrasts. Instructors were cautious about relying on peer or self-evaluation, preferring scaffolded assessments and early-warning systems to monitor dynamics. Students emphasized the need for more transparency, formative feedback, and recognition of hidden labor, while also noting the social risks of peer critique. Stakeholder involvement in assessment was generally limited to feedback and final showcases. Generative AI emerged as an evolving challenge, with both instructors and students seeking clearer guidance and opportunities to automate aspects of evaluation. Our results offer actionable evidence-based guidance for designing transparent and equitable assessment practices in team-based computing capstones.