Paper Info
Title
Aggregating Of Learning Object Units Derived From A Generative Learning Object
Abstract
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.
Year
Venue
Keywords
2009
INFORMATICS IN EDUCATION
learning object (LO), generative learning object (GLO), granularity level of LO, aggregating model, sequencing model
Field
DocType
Volume
Graph theory,Metaprogramming,Computer science,Learning object,Formal specification,Theoretical computer science,Artificial intelligence,Granularity,Process capability index,Semantics,Generative model
Journal
8
Issue
ISSN
Citations 
2
1648-5831
2
PageRank 
References 
Authors
0.40
3
2
Name
Order
Citations
PageRank
Vytautas Stuikys110217.07
Ilona Brauklyte220.40