Model of Recommendation System for for Indexing and Retrieving the Learning Object based on Multiagent System

This paper proposes a multiagent system application model for indexing, retrieving and recommendation learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata standards. The searching mechanism covers several different learning object repositories and the same object can be described in these repositories by the use of different types of fields. Aiming to improve accuracy and coverage in terms of recovering a learning object and improve the signification of the results we propose an information retrieval model based on the multiagent system approach and an ontological model to describe the knowledge domain covered.Keywords: AI in education, multi-agent systems, learning objects, recommendation systems.

Learning object retrieval in heterogeneous environments

This paper presents a solution to the problem of the search and retrieval digital tagged content in heterogeneous learning object repositories through architecture for intelligent retrieval of educational content in heterogeneous environments (AIREH) framework. This architecture unifies the search and retrieval of objects, thus facilitating the personalised learning search process by filtering and properly classifying learning objects retrieved for an approach for semantic-aware learning content retrieval based on abstraction layers between the repositories and the search clients. The use of federated databases techniques by using an organisation of agents allows those agents to work in a coordinated manner to solve a common problem, allowing the agents to adapt to the constantly changing environment (users, content repositories, etc.). Combining a complete agent-based architecture that implements the concept of federated search along with IR technologies may help organising and sorting search results in a meaningful way for educational content

Hybrid Multiagent System for Automatic Object Learning Classification

The rapid evolution within the context of e-learning is closely linked to international efforts on the standardization of learning object metadata, which provides learners in a web-based educational system with ubiquitous access to multiple distributed repositories. This article presents a hybrid agent-based architecture that enables the recovery of learning objects tagged in Learning Object Metadata (LOM) and provides individualized help with selecting learning materials to make the most suitable choice among many alternatives

Exploring the Relevance of Europeana Digital Resources: Preliminary Ideas on Europeana Metadata Quality

Europeana is a European project aimed to become the modern “Alexandria Digital Library”, as it targets providing access to thousands of resources of European cultural heritage, contributed by more than fifteen hundred institutions such as museums, libraries, archives and cultural centers. This article aims to explore Europeana digital resources as open learning repositories in order to re-use digital resources to improve learning process in the domain of arts and cultural heritage. To carry out this purpose, we present results of metadata quality based on a study case associated to recommendations and suggestions that provide this type of initiatives in our educational context in order to improve the access of digital resources according to a specific knowledge areas

Multi-agent system for Knowledge-based recommendation of Learning Objects

Learning Object (LO) is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS) can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision

A Multi-agent System that Searches for Learning Objects in Heterogeneous Repositories

This paper presents the BRENHET application, which introduces a new concept in searching for educational resources by using a learning object paradigm that describes these resources. The application is composed of a complete agent-based architecture that implements the concept of federated search. It can search different repositories in parallel, and is based on abstraction layers between the repositories and the search clients

Searching for and positioning of contextualized learning objects

Learning object economies are marketplaces for the sharing and reuse of learning objects (LO). There are many motivations for stimulating the development of the LO economy. The main reason is the possibility of providing the right content, at the right time, to the right learner according to adequate quality standards in the context of a lifelong learning process; in fact, this is also the main objective of education. However, some barriers to the development of a LO economy, such as the granularity and editability of LO, must be overcome. Furthermore, some enablers, such as learning design generation and standards usage, must be promoted in order to enhance LO economy. For this article, we introduced the integration of distributed learning object repositories (DLOR) as sources of LO that could be placed in adaptive learning designs to assist teachers’ design work. Two main issues presented as a result: how to access distributed LO, and where to place the LO in the learning design. To address these issues, we introduced two processes: LORSE, a distributed LO searching process, and LOOK, a micro context-based positioning process, respectively. Using these processes, the teachers were able to reuse LO from different sources to semi-automatically generate an adaptive learning design without leaving their virtual environment. A layered evaluation yielded good results for the process of placing learning objects from controlled learning object repositories into a learning design, and permitting educators to define different open issues that must be covered when they use uncontrolled learning object repositories for this purpose. We verified the satisfaction users had with our solutio

An Enhanced Approach to Retrieve Learning Resources Over the Cloud

This study proposes AIREH (Architecture for Intelligent Retrieval of Educational content in Heterogeneous Environments) that is a model for the development of digital content retrieval based on the paradigm of virtual organizations of intelligent agents Learning objects have made it possible to create digital resources that can be reused in various didactic units. These resources are stored in repositories, and thus require a search process that allows them to be located and retrieved