Retrieval Of Learning Objects Research Articles

Multidimensional Concept Map Representation of the Learning Objects Ontology Model for Personalized Learning

<p>This work presents the creation and representation of an ontology model for the domain knowledge used for learning objects. The purpose of the developed ontology model is to define relations between learning objects that can be applied for their effective search and visualization. As the number of learning objects increases, the representation of the knowledge domain becomes challenging. In this paper, the authors propose the application of multidimensional concept maps (MCMs) for domain knowledge representation. The definition of different attributes used in the ontology model allow for defining the different dimensions needed for MCM ontology visualization. In order to achieve integration of the defined ontology model and MCMs, a software tool named Ontology-based system for learning objects retrieval (OBSLO) was developed. OBSLO’s role is to dynamically generate MCMs given the defined ontology with its relations and attributes, while also providing a content delivery environment and working space for learners. Proposed OBSLO architecture with integrated ontology model and MCMs was evaluated and compared to the learning management system where ontology and MCMs were not used. It was shown that learners using OBSLO showed better success rate in learning and positive level of satisfaction.</p> <p> </p>

Smart Learning Objects Retrieval for E-Learning with Contextual Recommendation based on Collaborative Filtering

Smart Learning Objects Retrieval for E-Learning with Contextual Recommendation based on Collaborative Filtering

Learning Object Retrieval and Aggregation Based on Learning Styles

The main goal of this article is to develop a Management System for Merging Learning Objects (msMLO), which offers an approach that retrieves learning objects (LOs) based on students’ learning styles and term-based queries, which produces a new outcome with a better score. The msMLO faces the task of retrieving LOs via two steps: The first step ranks LOs using a unified learning style model and creates better LOs by merging the top-ranked LOs. The second step maps LOs onto a hierarchy of concepts to avoid duplicated topics. An experiment was conducted to evaluate this approach in an applied computing course. A total of 84 students were randomly split into four groups. The experimental results demonstrated that the msMLO is a promising approach that provides useful LOs based on students’ learning styles and the merging process for reusing stored LOs. Furthermore, this approach improves overall student learning performance and reduces the number of LOs reviewed.

Affinity-based learning object retrieval in an e-learning environment using evolutionary learner profile

With the abundance of learning objects (LOs) available across the web, there arises a demand for retrieving the LOs that exactly suit the learners’ requirements. In order to achieve this, the learner profile (LP) should exactly mimic the subject specific requirements of the learner as well as evolve over the learning cycle. Moreover, each LO should project itself in such a way that the learning management system is able to find it as a suitable candidate for a specific learner requirement. This paper proposes a novel method that fetches appropriate LOs for the learner by mapping his/her learner profile with those of the LOs. The LOs thus retrieved are then re-ranked according to their affinity towards the particular learner’s requirements and then presented to the learner. The experimental results demonstrated the effectiveness of the proposed method in retrieving appropriate learning content for learners.

A recommendation module to help teachers build courses through the Moodle Learning Management System

In traditional e-learning, teachers design sets of Learning Objects (LOs) and organize their sequencing; the material implementing the LOs could be either built anew or adopted from elsewhere (e.g. from standard-compliant repositories) and reused. This task is applicable also when the teacher works in a system for personalized e-learning. In this case, the burden actually increases: for instance, the LOs may need adaptation to the system, through additional metadata. This paper presents a module that gives some support to the operations of retrieving, analyzing, and importing LOs from a set of standard Learning Objects Repositories, acting as a recommending system. In particular, it is designed to support the teacher in the phases of (i) retrieval of LOs, through a keyword-based search mechanism applied to the selected repositories; (ii) analysis of the returned LOs, whose information is enriched by a concept of relevance metric, based on both the results of the searching operation and the data related to the previous use of the LOs in the courses managed by the Learning Management System; and (iii) LO importation into the course under construction.

M-Learning through Semantically Annotated Learning Objects and Ontologies

Number of mobile subscriptions has increased tremendously due to rapid development of mobile technologies. The performance and accessibility of the e-learning process can be enhanced through mobile devices which is called m-learning. M-learning makes learning resources available anywhere and anytime, provide strong search capabilities, and offers easy interaction features to the learners. M-learning also points the opportunity for interoperability than existing e-learning system. The integration of semantic web in m-learning can improve the efficiency of searching for learning objects and reduce the time and cost of learning process. Semantic web can be integrated with the help of ontologies and learning objects in semantic web. They offer rich medium to assist m-learning via semantic annotated learning objects and shared repositories. Two types of ontologies, such as learning object content ontology and learning object structure ontology are used in this system. These ontologies facilitate the reuse, sharing and retrieval of relevant learning objects which are the backbone of m-learning.

Learning Objects Retrieval Algorithm Using Semantic Annotation and New Matching Score

Learning objects retrieval is important for a variety of information needs and applications such as collection management, summary and analysis. Especially, retrieval of learning objects from the big collection of textual database has become a very active field. Since the learning information is stored in textual format in most of the time, the retrieval of learning objects from textual database faces two major challenges such as, large data handling, and effectiveness. If these two challenges are solved, the performance and its applicability will be improved significantly. Accordingly, the first challenges of large data processing will be handled through the semantic annotations method. In the semantic annotation method, the text document was converted to semantic annotation data using concept-based modeling. With the help of existing work, the large data will be converted to annotation object so that the matching process with the input query will be reduced and the complexity of handling big data will be also reduced. The second challenge of effectiveness will be solved using new matching score to obtain better retrieval effectiveness. Here, query will be matched with the annotated object using matching score that will be devised newly and it will be applied for learning object retrieval. The experimentation will be carried out utilizing the learning objects given in IEEE digital library and the performance of the proposed technique is evaluated using precision, recall and F-measure and also, comparative analysis will be performed to prove the better performance of the proposed technique. The analysis from the experimentations showed that the proposed approach has obtained a maximum precision of 0.98 and average precision of 0.95.

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.

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.

Linked open data for learning object discovery in adaptive e-learning systems

This paper proposes a new approach to automatic retrieval of learning objects (LOs) from local or external LO repositories via linked open data (LOD) principles. The approach dynamically selects the most appropriate LOs for an individual learning package in an adaptive e-learning system based on the use of LO metadata, learner profiles, ontology, and LOD principles. The approach has been designed to interlink the domain ontology with external open knowledge in the LOD cloud. SPARQL endpoints for datasets in the LOD cloud are also provided for instructors and learners to discover their desired LOs. Moreover, commonly known vocabularies such as Dublin core (DC), IEEE learning object metadata (IEEE LOM), web ontology language (OWL), and resource description framework (RDF) are employed to represent metadata and to link it with external LO repositories as well as DBpedia, the central hub of the LOD cloud. By using these techniques, the LOs and external knowledge can be exchangeable, shareable, and interoperable, resulting in an enhanced access to better learning resources. Based on the proposed approach, a prototype system has been developed and evaluated. It has been discovered that the system has yielded positive effects in terms of the learners’ satisfaction.

An Object Oriented Approach to Improve the Precision of Learning Object Retrieval in a Self Learning Environment

With the advancements in the WWW and ICT, the e-learning domain has developed very fast. Even many educational institutions these days have shifted their focus towards the e-learning and mobile learning environments. However, from the quality of learning point of view, which is measured in terms of “active learning” taking place, the e-learning environment lags behind the traditional classroom based learning. One of the reasons for that is the availability of large volumes of static and unorganized contents over the e-learning environment which makes it difficult for learners to precisely identify the contents matching with their requirements. With the modern day e-learning environments providing the digital contents to their learners in the form of Learning Objects (LOs), creation of such LOs by proper composition along with meaningful metadata will help the learners to retrieve them precisely. The focus of this work is to address the issues related to imparting active learning over an e-learning environment through LOs. This paper proposes a new method to compose, share, reuse, and manage objects based on the principles of the Object Oriented Paradigm (OOP). The Learning Object Composition and Presentation System (LOCPS) developed as a part of this work has shown better results in precisely retrieving the objects matching with the learner requirements.

An Adaptive System for Retrieval and Composition of Learning Objects

This paper proposes a new approach to automatic retrieval and composition of Learning Objects (LOs) in an Adaptive Educational Hypermedia System (AEHS) using multidimensional learner characteristics to enhance learning effectiveness. The approach focuses on adaptive techniques in four components of AEHS: Learning Paths, LO Retrieval, LO Sequencing, and Examination Difficulty Levels. This approach has been designed to enable the adaptation of rules to become generic. Hence, the application to various domains is possible. The approach dynamically selects, sequences, and composes LOs into an individual learning package based on the use of domain ontology, learner profiles, and LO metadata. The Sharable Content Object Reference Model is employed to represent LO metadata and learning packages in order to support LO sharing. The IMS Learner Information Package Specification is used to represent learner profiles. A preliminary evaluation of the developed system indicates the system’s effectiveness in terms of learners’ satisfaction.

Adaptive Object Re-Ranking Mechanism for Ubiquitous Learning Environment

Ubiquitous Learning (U-Learning), as an emerging learning paradigm, makes it possible for learners to carry out the learning activities at any places and at anytime. With the advantages of the devices, learners can obtain a variety of supplementary materials from the Internet. In the scope of distance learning, LOR (Learning Object Repository) stands for managing and sharing of learning related materials (known as learning objects). However, some challenges may raise while performing these activities. For instance, a huge amount of learning objects may appear while learners utilize the search service provided by LOR. Learners have to spend time on collecting relevant resources for specific purposes. This situation may discourage the reusability of learning objects especially in a ubiquitous environment. In this paper, based on systematic re-examination of reuse scenarios, an adaptive mechanism, as a resource discovery and search middleware, was proposed to assist learners in obtaining possible objects under ubiquitous environment. Achievement of the proposed mechanism can produce search results adaptive to specific situations in order of similarity degree based on the mixed information. We try to filter out some irrelevant results by using the past usage history, current geographical information and input query, so as to enhance the efficiency of learning objects retrieval in a ubiquitous environment. As a pilot test, Apple iPhone was utilized to be the major client testbed.

계층적으로 구조화된 이러닝 시스템을 위한 질의 처리 기법

본 논문에서는 대표적인 학습 객체 메타데이터 형식들을 통합하여 상호운용성을 제공하면서 대학 강좌를 위한 학습 객체의 메타데이터를 기술할 때 사용되는 개념과 개념들 간의 의미적 관계를 정의하는 온톨로지를 설계한다. 그리고 서로 다른 형식을 이용해 학습 객체의 메타데이터를 기술하는 여러 지역 저장소에 대해 효율적인 학습 객체 검색이 가능하도록 계층적으로 구조화된 이러닝 시스템을 구성하고 추론에 기반한 질의 처리 기법을 제안한다. 본 논문에서 제안한 계층적으로 구조화된 이러닝 시스템에서 학습 객체 온톨로지를 이용한 질의 처리 기법을 적용하면 사용자 질의에 직접적으로 관련이 있는 학습 객체와 함께 의미적 연관성이 추론된 학습 객체도 검색되어 보다 정확하고 만족도 높은 검색 서비스를 제공할 수 있다. In this paper, we design an ontology which provides interoperability by integrating typical metadata specifications and defines concepts and semantic relations between concepts that are used to describe metadata for learning objects in university courses. And we organize a hierarchical structured e-Learning system for efficient retrieval of learning objects on many local storages that use different specifications to describe metadata and propose a query processing method based on inferences. The proposed e-Learning system can provide more accurate and satisfactory retrieval service by using the designed ontology because both learning objects that be directly connected to user queries and deduced learning objects that be semantically connected to them are retrieved.

OLearner – An Ontology Based Learning Content Management System to Support Semantic Search and Contribution of Learning Objects

E-Learning is a fast, just-in-time, learning process, which is becoming popular in distributed and dynamic environments such as the World Wide Web. The core of E-Learning is Learning Content Management System (LCMS). Traditional LCMS supports search of learning objects based on keywords. The conventional approach provides relevant as well as non-relevant results as they do not consider user's context. Also, precise search of non-textual resources such as audio-visual or multimedia based resources is not possible as these are not simple document files where keywords could be searched. The learner has to spend large amount of time in screening the learning resource to determine the best suiting his/her current learning requirements. In this paper an ontology based LCMS has been discussed. Ontology is a formal description of concepts in a real-world domain and helps to store the semantic information of that domain. It is more expressive than any other standard database model and facilitates knowledge sharing and reuse, i.e. a common understanding of various contents that reaches across people and applications. Developed LCMS incorporates semantic capabilities with the help of ontologies to enable semantic based contribution and retrieval of learning objects. Two very useful ontologies are created to describe the learner profile and the learning content. These ontologies have been used to classify the learning material, organize topics of a course and store the information pertaining to learner's profile. The proposed OLearner LCMS facilitates a resource contributor to contribute the resource to the OLearner portal with the metadata corresponding to the learning content and learner profile ontologies. These ontologies are further useful for a learner in searching a more precise and appropriate learning object.

Taming the Metadata Beast: ILOX

We propose a framework for organizing multiple metadata specifications in a container that can be handled as a whole. This framework, named Information for Learning Object eXchange (ILOX), is developed as part of the IMS Learning Object Discovery & Exchange (LODE) specification that aims to facilitate the discovery and retrieval of learning objects stored across more than one collection. While thus far ILOX has been demonstrated to resolve a number of challenges specific to the e-learning domain, it is a generic framework that can be profiled to organize metadata about any type of digital content.

Semantic Retrieval of Learning Objects with Schema Matching

This article covers semantic methods and techniques based on semantic web technology (Berners-Lee et al., 2001) for content management in e-Learning platforms. The technique, and its prototypal implementation, involves the semantic search of learning objects (defned by David Wiley as “any digital resource that can be reused to support learning” (Wiley, 2002)) contained in SCORM (Sharable Content Objects Reference Model) “packages” (ADL) – a reference standard that ensures interoperability between different learning management systems (LMS). This technique integrates semantic techniques, combined with pattern matching techniques, to search for relevant learning objects for a given domain ontology used for queries, such as an ontology that describes the reference domain of a course of study. This correspondence is obtained through a matching operation, which returns a value of similarity between the ontology query (in OWL (Smith et al., 2004)) and learning objects such as those contained within repositories of online courses.

ONTOLOGY-BASED LEARNING OBJECTS SEARCH AND COURSES GENERATION

Learning objects paradigm is widely adopted in e-learning environments. Learning objects management can be improved using semantic technologies from ontology engineering and the semantic web. In this article we use a semantic model of the repository to improve both learning objects retrieval and composition. The use of domain knowledge enables automatic reasoning and makes the system able to import new domain models and use them to interrogate the repository (Arrigoni Neri 2005). Learning objects composition is one of the main challenges in e-learning management systems and can be improved exploiting ontological reasoning. The building of a course can be carried out in two phases, in the first phase we compose concept level entities to obtain an outline of the course, then we fill the outline with actual resources from the repository. Both phases can use ontology-based models to capture specific domain knowledge (Arrigoni Neri 2006). In order to provide an intuitive and expressive ontology representation, we briefly propose a graphical syntax for the well-known ontology web language (OWL).

Competencies management and learning organizational memory

PurposeThe purpose of this paper is to present the MEMORAe project, the goal of which is to offer an alternative to the loss of competencies and knowledge in an organization.Design/methodology/approachWithin the project MEMORAe, interest was focused on the capitalization of knowledge and competencies in the context of an organization. The E‐MEMORAe environment was developed based on the concept of learning organizational memory. This environment is dedicated to be used by a semantic learning organization as support for competency‐based training. It is evaluated in this context.FindingsIn the E‐MEMORAe environment, learning content is indexed by knowledge and competencies organized by means of ontologies. Learners can acquire thise knowledge and these competencies by doing different tasks, accessing different contents. In the memory, competencies are defined via the knowledge they enable to be put into practice.Practical implicationsIt is known that some industrial communities of practice are interested in the use of E‐MEMORAe.Originality/valueWithin the MEMORAe project, an ontology‐based learning organizational memory is proposed as support for learning object retrieval by competency for competency based learning. Using such a memory enables and goes beyond organizational knowledge management. Knowledge and competencies are defined and structured to facilitate their access and their learning. This latter is also made possible thanks to the resources that they index.

Multiagent system for learning objects retrieval with context attributes

Educational standards that include content metadata description for materials have great potential for managing e-learning information and content units and facilitate their interoperability and reutilisation. These open semantic and distance learning aspects contribute new and important possibilities for online education systems. The Learning Objects (LOs) paradigm focuses this new gap on the management and exchange of educational materials. This paper presents an application approach to educational content recommendation based on Learning Objects. It describes an architecture based on a multi-agent system with a bio-inspired algorithm rooted in self-organisation theory. It supports the retrieval, search, selection and composition of these LOs.