Distributed Spiral Learning: An Architectural Framework for Coherent Development in Distributed Practice

Traditional spiral learning models assume continuous delivery, stable sequencing, and centralized control conditions rarely met in community sport. This paper proposes Distributed Spiral Learning (DSL), a theoretical model reconceptualising spiral learning as a river system rather than an engineered channel. Drawing on ecological dynamics, distributed cognition, and constraint-led approaches, DSL explains how learning coherence persists across variable attendance, multiple coaches, and disrupted sequences. Rather than enforcing a single prescribed spiral, the model shapes architectural constraints, session structure, recurring practices, safety signals, and layered challenge depth that allows multiple overlapping spirals to coordinate without synchronization. The paper presents falsifiable predictions, specifies boundary conditions, and demonstrates how architectural consistency produces resilience unavailable to content-dependent models. DSL addresses a gap between learning theory's assumptions and the structural realities of distributed practice environments.