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European Conference on Games Based Learning (ECGBL)
SigLab is a physical serious game in which learners collaborate on a modified wooden railway. It was developed over roughly seven years as a practice-based case study at TU Braunschweig.
SigLab is a physical serious game that uses a modified wooden railway to teach railway signalling and operations through collaborative, tangible play. Developed over roughly seven years as a practice-based case study at TU Braunschweig, the game discretises continuous railway dynamics into fields and rounds governed by a fixed six-phase round structure. This abstraction is intended to make core principles such as route safety, blocking time, braking distance, block spacing, and section release visible and open to discussion among learners. The project's central claim is that a shared physical layout and discrete modelling can support a more concrete understanding of how signalling systems behave as a whole.
The reported evidence is deliberately modest. Since 2019, SigLab has been run across 14 semesters with around 350 students, and it has also been adopted in professional training and some EU-funded research. Authors describe improvements in shared concept formation, system-level understanding, and peer dialogue, but present these as practice-based observations rather than measured learning outcomes. No formal evaluation of learning effects has yet been carried out, and such an assessment is identified as future work. Facilitation intensity, group-size limits, and time requirements are acknowledged as practical constraints on wider use.
The project's accessibility is a notable feature. The kit costs roughly EUR 1,008 per set, and ten sets are currently in circulation. The work draws on foundations in serious games research, tangible and embodied learning, and railway signalling principles, with project resources maintained through RailEduKit. Taken together, the contribution is best understood as a well-documented, low-cost design for railway operations education whose pedagogical value awaits systematic empirical validation.
DiGRA · 2026