Designing a Learning Design Engine as a Collection of Finite State Machines

Abstract

Specifications for eLearning are becoming increasingly sophisticated and complex as they deal with the core of the learning process. With the arrival of the latest specifications and standards for eLearning, the sophistication, expressiveness and complexity has increased considerably. Simple transformations are not adequate to successfully implement these specifications and standards. IMS Learning Design (LD) (IMS, 2003b) is a representative of such a new specification. It’s declarative nature and expressiveness increases the complexity for any implementation. This probably is the largest hurdle that stands in way of successful general deployment of this type of specifications. This article describes the implementation of an engine for LD as collection of finite state machines (FSMs). Each state is constructed from a set of properties which can either declared explicitly in LD or implicitly by the engine. State transitions are implemented through a mechanism of events and event handlers, completing the finite state machine. By re-using certain properties across FSMs it is possible to create automatic propagation mechanism dealing with group dynamics without additional efforts. With the FSMs in place, personalization becomes a simple task. By combining the principles presented in the article, it becomes clear that an elegant implementation becomes feasible, and has been demonstrated by in a first actual implementation.