Aggregating of Learning Object Units Derived from a Generative Learning Object

Aggregating and sequencing of the content units is at the core of e-learning theories and standards. We discuss the aggregating/sequencing problems in the context of using generative learning objects (GLOs). Proposed by Boyle, Morales, Leeder in 2004, GLOs provide more capabilities, focus on quality issues, and introduce a solid basis for a marked improvement in productivity. We use meta-programming techniques to specify GLOs and then to automatically generate LO units on demand. Aggregating of the generated units to form a compound at a higher granularity level can be performed in various ways depending on the selected criteria or their trade-offs (e.g., complexity, granularity level, semantic density, time constraints, capabilities of modelling the learning process, etc.) that enable to evaluate units in advance. We describe aggregating as an internal sequencing of the content units derived from a GLO. Our contribution is a formal graph-based model to specify the problem when the variability of LO units is large. First we formulate the problem and consider properties of the proposed model; and then we analyze a case study, implementation capabilities, and evaluate the approach for e-learning.