The main goal of LORNET is to build new knowledge in computer science and cognitive science to help design and develop the architectures, the tools and the methods to maximize the usability, the efficiency and the usefulness of a network of learning objects repositories (LOR) for education, training and knowledge management.
Six objectives were established to reach this main goal:
- To design and implement interoperability architectures and metadata protocols to help establish the structure and the operations of a LOR network.
- To develop clustering methods and editors to construct and operate complex resources from simple ones, integrating learning objects, features and actors involved in a distributed learning or knowledge management system.
- To represent objects at different levels of granularity and abstraction, using ontologies and multi-agent techniques to enable content repurposing and adaptive assistance to users.
- To adapt, expand and extract knowledge and resource mining techniques and tools to fully exploit the contents of learning objects repositories and describe them for interoperability and reusability purposes.
- To define metadata and design protocols, as well as search and delivery tools, for advanced multimedia and virtual reality objects, providing quality service on the networks for their development, integration and use in repositories.
- To develop an integrated, accessible and flexible operating system that supports knowledge management activities for multiple users. This sophisticated solution includes a wide array of tools, applications, functionalities and resources to develop and support learning object repositories networks.
A Theme Structure for Knowledge Evolution
Each objective corresponds to a research area, or theme, of the proposed program, where researchers will build new knowledge and technical innovation through targeted research and development.
- Theme 1 - Interoperability and Metadata Protocols
- Theme 2 - Modelling, Clustering and Coordinating Learning Objects
- Theme 3 - Active and Adaptive Learning Objects
- Theme 4 - Knowledge Extraction and Learning Object Mining
- Theme 5 - Creation, Search and Delivery of Advanced Multimedia Learning Objects
- Theme 6 - Telelearning Operations Systems (TELOS)
LORNET Project Themes (Courtesy of LORNET Research Network)
Theme 4 - Knowledge Extraction and Learning Object Mining
The Pattern Analysis and Machine Intelligence research group will be working on Theme 4 of the LORNET project under the leadership of Dr. Mohamed Kamel.
The efficient use of learning objects repositories requires the development of relevant tools to locate, extract and disseminate knowledge embedded in learning object repositories. These tools will help provide the appropriate contexts and structures.
Moreover, they will facilitate interactions and favour efficient delivery, navigation and retrieval. Theme 4 aims to explore the possibilities of applying dynamic pattern discovery and resource-mining techniques to learning object repositories and related information sources, such as the usage history and user information, in order to identify hidden patterns.
This theme addresses problems such as the representation and the extraction of learning object repository contents, be it phrases, semantics, graphics or metadata. It also addresses the organization and clustering techniques to extract common knowledge and classify the elements of a collection of learning objects. It also deals with cases where classification and clustering approaches cannot be applied to knowledge discovery (i.e. training units which became unavailable or contain an insufficient number of samples) requiring other approaches such as reinforcement learning agents. The outcome generated by theme is the cornerstone to more sophisticated knowledge tools and management.