The DiP‑iT project, funded by the German Federal Ministry of Education and Research under grant 16DHB 3007, investigated how collaborative and cooperative learning can be embedded into software‑development courses. Three universities – Humboldt University of Berlin (HU), Otto‑von‑Guericke University Magdeburg (OVGU) and the Technical University Bergakademie Freiberg (TU‑BAF) – formed the research consortium. HU led the overall coordination and the technical development of a digital teaching guide, OVGU carried out the empirical studies on team learning, and TU‑BAF supplied the data sets from its own programming courses. The project began on 1 February 2020 and ran through 2021, with the first half of 2021 earmarked for the completion of the remaining work packages.
The scientific work started with a comprehensive inventory of existing cooperative and collaborative teaching practices in computer‑science education (AP 1). In the first sub‑task (TAP 1.1), a qualitative study was conducted from April to June 2020, interviewing ten lecturers and fifteen students across the partner universities. The analysis revealed that cooperative activities are present in many programming courses, but they are often limited to informal group work and are rarely integrated into formal assessment. The study also identified the tools used, the pedagogical guidance provided, and the perceived benefits and challenges of team work. These findings formed the basis for a set of actionable recommendations that were incorporated into a didactic model (M 1.1).
A systematic literature review (TAP 1.2) expanded the evidence base and identified key success criteria for collaborative programming. The review highlighted that social competencies of students play a minor role in team performance, while traditional assessment methods – code reviews and written exams – dominate. The review also pointed to a gap in adaptive support for team work, motivating the development of learning‑analytics tools.
Building on these insights, HU and OVGU developed a digital teaching guide (AP 2, TAP 2.1). The guide provides a structured framework for designing, implementing, and evaluating team‑based learning activities. It includes best‑practice guidelines for group formation, process support, and assessment design. The guide is intended to help instructors make evidence‑based design decisions and to embed team work seamlessly into existing curricula.
Parallel to the didactic work, the project focused on data‑driven support for team learning. TU‑BAF’s course data were examined in TAP 4.1, and two extraction tools – GitHub2Pandas and GitLab2Pandas – were created to convert repository activity logs into structured data tables. Process‑mining techniques were then applied to these data sets to model team interaction patterns and to identify distinct usage models. A quality‑assessment model was derived that translates repository metrics into actionable feedback for instructors. This model was validated in the summer and winter semesters of 2020 (TAP 6.1) through pre‑ and post‑questionnaires that measured students’ prior knowledge, learning outcomes, and experiences with collaborative work.
The analytics component culminated in the development of dashboards for both students and teachers. The student dashboard visualises individual contribution, code quality, and collaboration metrics, while the instructor dashboard aggregates team‑level indicators and highlights potential bottlenecks. These dashboards are designed to support adaptive feedback and to foster reflective practice among learners and educators alike.
In summary, DiP‑iT produced a robust evidence base for collaborative programming, a practical digital guide for instructors, and a suite of learning‑analytics tools that translate version‑control data into pedagogically useful insights. The interdisciplinary collaboration among HU, OVGU, and TU‑BAF, supported by the BMBF, enabled the integration of empirical research, didactic design, and data science to advance team‑based software‑development education.