Learning Objects: Using Language Structures to Understand the Transition from Affordance Systems to Intelligent Systems

Abstract

There has, of recent, been a marked interest in the utilization of learning objects, specifically on the potential of granularity and reusability to make instructional development more efficient, and learning experiences more customized and meaningful . Juxtaposition of affordance systems and intelligent systems reveals powerful possibilities for granularity and reusability. Biology, architecture and language are investigated as systems to explore the role of granularity and reusability, in the quest to better define current perceptions of learning objects in the realm of design and technology in instruction. The notion of subsystems, and the rules that apply within each system (intra-systemic rules) and between subsystems (inter-systemic rules) as it extends to holistic purposes, defines the role of granularity. The inverse relationship between granular reusability and learning-objective specificity is addressed. In conclusion, learning objects within the virtual context are evaluated to identify commonalities and patterns to inform the design and development of future learning objects, and to clarify granularity and how it impacts the scale and scope of reusability.