Description Of Learning Objects Research Articles

Creating an Advanced Web Based Environment using Semantic Web

E-learning systems are of no help to the users if there are no powerful search engines and browsing tools to assist them. Most of the current web-based learning systems are closed systems where the courses and the learning material are fixed. The only thing that is dynamic is that the organization of the learning content is adapted to allow individualized learning environment. The learners of web-based e-learning systems belong to different categories based on their skills, background, preferences and learning styles. This paper focuses on personalized semantic search and recommending learning content that are appropriate to the learning environment. The semantic and personalized search of the learning content is based on comparison of the learner profile. The learner profile depends on re individual learning style of the user and learning objects’ metadata. This concept needs to be represented both in the learner profile as well as learning object description as certain data structures. Personalized recommendation of learning objects uses an approach to determine a more suitable relationship between learning objects and learning profiles. Thus, it may advise a learner with most suitable learning objects. Semantic learning objects search is based on the query expansion of the user query and by using the semantic similarity to retrieve semantic matched learning objects.

Creating an Advanced Web Based Environment using Semantic Web

E-learning systems are of no help to the users if there are no powerful search engines and browsing tools to assist them. Most of the current web-based learning systems are closed systems where the courses and the learning material are fixed. The only thing that is dynamic is that the organization of the learning content is adapted to allow individualized learning environment. The learners of web-based e-learning systems belong to different categories based on their skills, background, preferences and learning styles. This paper focuses on personalized semantic search and recommending learning content that are appropriate to the learning environment. The semantic and personalized search of the learning content is based on comparison of the learner profile. The learner profile depends on re individual learning style of the user and learning objects’ metadata. This concept needs to be represented both in the learner profile as well as learning object description as certain data structures. Personalized recommendation of learning objects uses an approach to determine a more suitable relationship between learning objects and learning profiles. Thus, it may advise a learner with most suitable learning objects. Semantic learning objects search is based on the query expansion of the user query and by using the semantic similarity to retrieve semantic matched learning objects.

Adaptation of Descriptive Metadata for Managing Educational Resources in the GREDOS Repository

In this paper the authors describe a Project entitled “Divulgación de Recursos Educativos Digitales (DIRED)” (Divulgation of Digital Educational Resources) addressed to promoting specific educational resources and mobile apps for educational proposals in order to manage them through the institutional repository of the Salamanca University (GREDOS). The authors present a proposal for describing learning objects based on pedagogical information, digital competences and learning styles. The authors also suggest educational information for classifying useful mobile apps. To achieve their suitable access and recovery, the authors focus on the use of Learning Object specific metadata in digital repositories such as LOM (Learning Object Metadata). The authors study the metadata mapping necessary to adapt from LOM to Qualified Dublin Core, because this is the standard used in the GREDOS repository built with a DSpace platform. Finally, the authors present their implementation of Learning Object Description in the GREDOS repository.

ARQUITECTURA DE BÚSQUEDA PARA REPOSITORIOS DE OBJETOS DE APRENDIZAJE

With the emergence of Repositories of Learning Objects, and for a more efficient use of them, different search engines have been developed that allow users to easily and effectively find the materials they need. There are many engines that are based on keywords, but these limit the search area, some based on ontologies and others like the one used, based on the metadata that describe learning objects. A very powerful engine is SOLR, this is based on the LUCENE library, which runs as a Web application in Java, allowing documents to be sent to be indexed via HTTP and consulted using HTTP GET requests. The experiments carried out with the tool implemented based on this engine demonstrate the efficiency and quality of the results with respect to the other engines.

Models of recognition procedures with logical correctors

The construction of correct recognition procedures based on incorrect elementary classifiers (fragments of learning object descriptions) is studied for problems with integer data. A number of special Boolean functions are considered as the correction functions. A family of correct sets of elementary classifiers (a family of correctors) is constructed at the learning stage. An original technique is proposed to construct this family. The technique helps find the most informative correctors without significant time expenditures. The results are given for the constructed recognition procedures tested on real problems. Earlier studies dealt with the simplest case when the elementary classifier was a separate feature value in the description of a learning object.

An international analysis of the extensions to the IEEE LOMv1.0 metadata standard

Metadata is an important element for achieving interoperability between Learning Objects systems; it facilitates the process of describing, searching, selecting and recovering of Learning Objects. The IEEE LOMv1.0 is a metadata standard for describing Learning Objects. Recently found evidence shows that the standard does not fulfill all requirements of its users, therefore they have extended it. In order to know the impact that extensions have into the interoperability, we made an international study where the use of the LOMv1.0 standard in forty-four works was analyzed. As a result we found fifteen types of extensions implemented to the standard.

An Integration of Methodological Resources into Learning Object Metadata Repository

The number of digital resources to be used and reused for learning (learning objects) is constantly increasing, therefore describing learning objects with metadata is important to enhance learning object search, retrieval, and usage. Learning objects can be considered not only as resources, providing learning content, but as methodological resources, including teachers' experiences, reflections, examples or instructions of usage of content objects, and descriptions of learning methods. However, existing standards and specifications for learning objects metadata are not intended for including methodological resources and learning method descriptions together with content objects. In this paper, we present the results of a study, carried among general school teachers, which present a view of teachers on methodological resources and their importance, and propose a new model of learning object metadata for the learning object repository to include methodological resources, descriptions of learning methods and their links with content objects.

Using Semantic Web to support Advanced Web-Based Environment

In the learning environments, users would be helpless without the assistance of powerful searching and browsing tools to find their way. Web-based e-learning systems are normally used by a wide variety of learners with different skills, background, preferences, and learning styles. In this paper, we perform the personalized semantic search and recommendation of learning contents on the learning Web-based environments to enhance the learning environment. Semantic and personalized search of learning content is based on a comparison of the learner profile, that is based on learning style, and the learning objects metadata. This approach needs to present both the learner profile and the learning object description as certain data structures. Personalized recommendation of learning objects uses an approach to determine a more suitable relationship between learning objects and learning profiles. Thus, it may advise a learner with most suitable learning objects. Semantic learning objects search is based on the query expansion of the user query and by using the semantic similarity to retrieve semantic matched learning objects.

Adaptive Hypermedia Systems for E-Learning

The domain of traditional hypermedia is revolutionized by the arrival of the concept of adaptation. Currently the domain of Adaptive Hypermedia Systems (AHS) is constantly growing. A major goal of current research is to provide a personalized educational experience that meets the needs specific to each learner (knowledge level, goals, motivation etc...). In this article we have studied the possibility of implementing traditional features of adaptive hypermedia in an open environment, and discussed the standards for describing learning objects and architectural models based on the use of ontologies as a prerequisite for such an adaptation.

An adjustable personalization of search and delivery of learning objects to learners

This paper describes a technical solution realizing an approach for personalized search of learning objects on the Web, which proposes a comparison of a learner (user) profile and learning object descriptions. This comparison is based not only on values of attributes of the learner profile and attributes of the learning object descriptions, but it also considers the importance of these attributes for the learner. In order to implement such a search we propose to develop ontological models of the learner and learning objects as well as approaches for determination and adjustment of the learner’s preferences using corresponding ontologies. For this purpose we have selected criteria of estimation of suitability of learning objects to the learner profile and introduced coefficients of importance of these criteria, as well as a method for adjusting these coefficients.

A relation metadata extension for SCORM Content Aggregation Model

To increase the interchangeability and reusability of learning objects, Advanced Distributed Learning Initiative suggested a set of metadata in SCORM Content Aggregation Model to describe learning objects and express relationships between learning objects. However, the suggested relations defined in the metadata of the SCORM CAM are limited. To resolve the problem, new relations were proposed by researchers. Unfortunately, some of the relations are redundant and even inappropriate. In addition, the usability of these relations has never been formally studied. Therefore, in this paper, we summarized and analyzed existing relations, removed duplicated relations, and developed a new relation metadata extension for SCORM CAM. Also, we surveyed 145 students in attempt to understand whether or not the proposed relations can increase their learning effectiveness. The results of the survey showed that learners agreed that the proposed relations are helpful.

A dialogue approach to learning object descriptions and semantic categories

Acquiring new knowledge through interactive learning mechanisms is a key ability for humanoid robots in a natural environment. Such learning mechanisms need to be performed autonomously, and through interaction with the environment or with other agents/humans. In this paper, we describe a dialogue approach and a dynamic object model for learning semantic categories, object descriptions, and new words acquisition for object learning and integration with visual perception for grounding objects in the real world. The presented system has been implemented and evaluated on the humanoid robot Armar III.

Enriching e‐learning metadata through digital library usage analysis

PurposeThe purpose of this research is to propose an evaluation framework for analyzing learning objects usage, with the aim of extracting useful information for improving the quality of the metadata used to describe the learning objects, but also for personalization purposes, including user models and adaptive itineraries.Design/methodology/approachThe paper presents experimental results from the log usage analysis during one academic semester of two different subjects, 350 students. The experiment examines raw server log data generated from the interactions of the students with the classroom learning objects, in order to find relevant information that can be used to improve the metadata used for describing both the learning objects and the learning process.FindingsPreliminary studies have been carried out in order to obtain an initial picture of the interactions between learners and the virtual campus, including both services and resources usage. These studies try to establish relationships between user profiles and their information and navigational behavior in the virtual campus, with the aim of promoting personalization and improving the understanding of what learning in virtual environments means.Research limitations/implicationsDuring the formal learning process, students use learning resources from the virtual classroom provided by the academic library, but they also seek information outside the virtual campus. All these usage data are not considered in the model proposed here. Further research is needed in order to obtain a complete view of the seeking information behavior of students for improving the users' profile and creating better personalized services.Practical implicationsThis paper suggests how a selection of fields used in the LOM standard could be used for enriching the description of learning objects, automatically in some cases, from the learning objects usage performed by an academic community.Originality/valueFrom libraries beginnings, they have been a “quiet storage place”. With the development of digital libraries, they become a meeting place where explicit and implicit recommendations about information sources can be shared among users. Social and learning process interactions, therefore, can be considered another knowledge source.

The RDA of Standards for a Healthy e-Learning System

Standards for learning technologies and their implementation have long been a topic of scholarly debate. The focus previously lay on research and development of common specifi cations and their usage in describing learning objects or learning designs. More recently, discussions arose around the socio-economic role of standards, whence business processes related to the creation and promotion of e-learning standards came under criticism. This is all the more important as especially at universities the big push towards standard-based e-learning is still missing. Despite the often declared benefits of e-learning standards, universities, unlike commercial learning and training providers or the military, have made little progress towards implementing standard-based editorial and production processes into their daily workfl ows. We would like to argue that standards are neither pedagogically nor economically neutral and that this is a barrier to full and cohesive implementation in daily workfl ows. With Casey et al. (2006a) we see the true challenge for e-learning not in the adoption of technology, but in managing structural and cultural change in institutions. The article will therefore take a holistic management approach of standards applied in universities with a view of their multi-faceted role as both producers and consumers of learning content as well as system developers.

Facilitating metadata creation - The MINE metadata wizard for IEEE LOM

One stumbling block to the establishment of distance learning programs is on the time consuming tasks to create high quality digital contents that can be used in instruction. In the literature, reusability of learning objects is addressed as a potential solution toward the sharing of high quality contents. To find suitable contents while one is designing the course material, the IEEE LOM provides a generic and standard mechanism to enable a common representation and vocabulary for the description of learning objects. However, it is unlikely that an individual will fill out the 77 items of metadata defined by the IEEE LOM. Nevertheless, it is possible to compute the relationship among these 77 items, and to provide an automatic metadata generation mechanism to reduce the load of a content creator. This paper proposed such a mechanism, by means of using user profiles, system extracted information, interactive questions, and influence rules. We present the mechanism, as well as a developed system, called the MINE Metadata Wizard, which is integrated with a SCORM-compliant authoring tool that we have developed. Interested readers are welcome to download the tool under http://www.mine. tku.edu.tw/.

Learning objects: resources for instruction.

Upon completion of this article, the reader will be able to describe learning objects (LOs) and discuss their use in clinical laboratory sciences instruction. Through a questionnaire, educators evaluated clinical laboratory sciences-related LOs for accessibility, usability and instructional qualities. LOs were presented on a password-accessed website. Evaluations were completed on the website. Nine educators participated in the evaluation. The LOs were made available to participants for use in their own instructional material. The evaluation measured educators' interest in and perceived usefulness of LOs in clinical laboratory sciences curriculum. On a scale of one to five with one equal to poor and five equal to excellent, participants rated LOs as accessible (4.68) and usable (4.61). Ninety-eight percent stated that they would use LOs in their curriculum. Fifty-seven percent stated that they could attribute improved learning performance on student exposure to LOs. LOs are useful, relevant, and time-saving resources to clinical laboratory sciences instruction.

Building Interoperable Learning Objects Using Reduced Learning Object Metadata

The new e-learning generation depends on Semantic Web technology to produce learning objects. As the production of these components is very costly, they should be produced and registered once, and reused and adapted in the same context or in other contexts as often as possible. To produce those components, developers should use learning standards to describe these objects in order to support interoperability. IEEE Learning Object Metadata (LOM) is the most dominant standard for describing learning objects, in which 76 different elements are used to describe the different aspects of e-learning. Nonetheless, it will still be time consuming to build these learning objects. This paper introduces a model for building Global Interoperable Learning Objects (GILO) for the e-learning community. This is achieved by using a reduced set of the LOM elements, and giving a unique global ID to the learning object. This will enable software agents to query these learning object repositories, to automatically deliver the required material to the e-learning consumer.

On the Convergence of Formal Ontologies and Standardized E-Learning

Current efforts to standardize e-learning resources are centered on the notion of a learning object as a piece of content that can be reused in diverse educational contexts. Several specifications for the description of learning objects — converging in the LOM standard — have appeared in recent years, providing a common foundation for interoperability and shared semantics. At the same time, the Semantic Web vision has resulted in a number of technologies grounded in the availability of shared, consensual knowledge representations called ontologies. As proposed by several authors, ontologies can be used to provide a richer, logics-based framework for the expression of learning object metadata, resulting in the convergence of both streams of research towards a common objective. In this article, we address the practicalities of the representation of LOM metadata instances into formal ontologies, discussing the main technical and organizational issues that must be addressed for an effective integration of both technologies, and sketching some illustrative examples using modern ontology languages and a large knowledge base.

The need for academic middleware to support advanced learning services

The increased significance of digital and human networks in academic institutions and the development of vocational and distance-learning methods and systems and their associated delivery services have modified the organizational and information structures of academic institutions. In this new scenario, management information systems, many learning resources in printed or digital format and network-related user services compete for acceptance in a rapidly changing system. There is a growing need to provide new services in order to function in a changing international market, to prepare for the expected student mobility and the increasing diversity of educational needs. There are two main research areas that are looking at the problem, each from their own perspective: the learning technology area and the network technology area. The former is working mainly on standards and best practices for the delivery of online and distance education. Efforts include the description of learning objects with metadata, the description of learners with profiles and the generic depiction of learning technology systems in terms of information systems architecture. In the latter research area, recent deployment issues have been of increasing concern. Inevitably, research is moving towards an area where major work is already underway. This is the part of the networking community that is working on the definition and deployment of middleware services. This paper argues for the introduction of academic middleware to speed up the delivery process of the new services and reduce the complexity of the managed systems.

Rule-based 3D Object Recognition from Range Images

Automatic learning of 3D object descriptions for the recognition of future instances of sensed objects is an important feature of vision systems. In this paper, we describe a clustering-based method for learning object description and a scheme for subsequent classification using this description. The description derived is an evidence rulebase that discriminates between the object classes. During classification, a sensed object is either recognized as one of the objects in the database or rejected as an unknown object. The rules fired by the object are used to eliminate hypotheses for which there is no evidence in the sensed object and to arrive at its identity and pose.