Learning Object Metadata Research Articles

A panoramic view on metadata application profiles of the last decade

This paper describes a study developed with the goal to understand the panorama of the metadata Application Profiles AP: i what AP have been developed so far; ii what type of institutions have developed these AP; iii what are the application domains of these AP; iv what are the Metadata Schemes MS used by these AP; v what application domains have been producing MS; vi what are the Syntax Encoding Schemes SES and the Vocabulary Encoding Schemes VES used by these AP; and finally vii if these AP have followed the Singapore Framework SF. We found i 74 AP; ii the AP are mostly developed by the scientific community, iii the 'Learning Objects' domain is the most intensive producer; iv Dublin Core metadata vocabularies are the most used and are being used in all domains of application and IEEE LOM is the second most used but only inside the 'Learning Objects' application domain; v the most intensive producer of MS is the domain of 'Libraries and Repositories'; vi 13 distinct SES and 90 distinct VES were used; vi five of the 74 AP found follow the SF.

Learning Object Design and Development in Folklore Education Using Web 2.0 Tools

This paper refers to the design and development of learning objects in folklore education using available Web 2.0 technologies, in order to effectively address teaching folk culture in a classroom. More specifically, the role of learning objects as enriching digital resources in e-books is presented along with their role as learning assets that can function independently in the learning process. Moreover, learning objects are classified based on their characteristics either as simple learning objects, namely learning assets, or as complex learning objects in which case they constitute learning resources. At the same time we make a reference to learning objects with educational learning object metadata in accordance with international standards. The paper focuses on a) the development of learning objects in folklore teaching using web 2.0 tools in the context of enriched e-book creation («digital school» framework), b) the implementation of worksheets as part of ICT-enhanced teaching scenarios in folklore teaching along with their interconnection with appropriate learning objects as enriching digital resources. DOI: 10.5901/mjss.2013.v4n11p104

The effect of users' tagging motivation on the enlargement of digital educational resources metadata

The emerging Web 2.0 applications have allowed new ways of characterizing digital educational resources, which moves from the expert-based descriptions relying on formal classification systems such as the IEEE Learning Object Metadata (LOM) to a less formal user-based tagging. This alternative way of characterizing digital educational resources is commonly referred to as social tagging, whereas the collection of tags created by the different users individually is referred to as folksonomy. As a result, a number of studies have been reported in the field of Technology-enhanced Learning (TeL) which provide evidence that social tagging has the potential to enlarge metadata descriptions, as well as the formal structured vocabularies with additional terms derived by the resulted folksonomy but more in depth studies are needed regarding this enlargement process. Thus, one issue to investigate further is the possible influence of users’ tagging motivation to the resulted enlarged metadata descriptions. In this paper we aim to investigate this issue by first proposing a methodology that aims to evaluate whether users’ tagging motivation can influence (a) the enlargement of educational resources possible descriptions compared to the anticipated creators’ descriptions and (b) the resulted folksonomy compared with formal structured vocabularies used by the creators of the educational resources and then, apply it to an existing LOR with more than 3,000 science education resources, 434 taggers and 14,707 social tags. Our experiments provided evidence that taggers with a specific type of tagging motivation can produce tags that are significantly different from formal metadata generated by the creators of the educational resources.

Providing semantic metadata to online learning resources on sustainable agriculture and farming: combining values and technical knowledge

Sustainable or organic agriculture aims at harmonizing the efficient production of food with the preservation of the environmental conditions for continuing production in a sustained way. As such, it embodies a set of environmental values that are currently taught and learnt worldwide in specific courses or as part of broader programs or curricula. The increased availability of online, open learning resources enables new possibilities for education in the domain of sustainable agriculture and farming. Concretely, portal projects related to sustainable agriculture have been deployed in the last years, using metadata as a mechanism to make resource search more effective in large, multilingual, and heterogeneous content databases. As environmental values are part of the learning needs targeted by those systems, it is required that both axiological descriptions (relating to values) and technical information (related to domain-specific issues) are expressed through metadata records, which requires a clear differentiation of both kinds of descriptions. This article reports on a knowledge modeling approach that fulfils that need and that can be used for the annotation of learning resources using standardized metadata schemas as IEEE LOM. The model has been prepared for the annotation of learning resources in a cross-national federation of organic agriculture repositories called Organic.Edunet, enabling the combination of both kinds of aspects in the formulation of searches. An analytic assessment of the expressive capabilities of the approach is provided using example resources.

A general model of learning design objects

Previous research on the development of learning objects has targeted either learners, as consumers of these objects, or instructors, as designers who reuse these objects in building new online courses. There is currently an urgent need for the sharing and reuse of both theoretical knowledge (literature reviews) and practical knowledge (best practice) in learning design. The primary aim of this paper is to develop a strategy for constructing a more powerful set of learning objects targeted at supporting instructors in designing their curricula. A key challenge in this work is the definition of a new class of learning design objects that combine two types of knowledge: (1) reusable knowledge, consisting of theoretical and practical information on education design, and (2) knowledge of reuse, which is necessary to describe the reusable knowledge using an extended learning object metadata language. In addition, we introduce a general model of learning design object repositories based on the Unified Modeling Language, and a learning design support framework is proposed based on the repository model. Finally, a first prototype is developed to provide a subjective evaluation of the new framework.

An Assistant for Loading Learning Object Metadata: An Ontology Based Approach

Introduction Nowadays the web is one of the most important sources of educational material where students and teachers have a large amount of information at their disposal. For this information retrieval process, people use search engines which, unfortunately in many cases, do not return the desired information or return too many web pages. Learning objects are intended to help with the storage, classification, and reuse of educational resources. A Learning Object (LO) is any digital resource that can be reused to support learning Editor: Janice Whatley (Wiley, 2002). LOs can be used by a student who wants to learn a subject or may be used by a teacher who wants to prepare materials for his/her class. LOs are described with metadata usually in the standard IEEE LOM (Learning Object Metadata: http://ltsc.ieee.org/wg12) and they are stored in different repositories. Examples of such repositories are FLOR (www.laclo.org), Ariadne (www.ariadne-eu.org) and OER Commons (www.oercommons.org). There other standards such as the Dublin Core Metadata Initiative, or DCMI (dublincore.org), which is an open organization supporting innovation in metadata design and best practices across the metadata ecology. The Dublin Core Metadata is not focused on educational metadata but it maintains a number of formal and informal relationships with different standards bodies and it is widely used. Other metadata standards related with educational metadata are: IMS Learning Object Metadata IMS LOM (http://www.imsglobal.org/metadata), Canadian Core Learning Resource Metadata Protocol CanCor (http://www.cancore.ca) and UK Learning Object Metadata Core UK LOM (http://www.cetis.ac.uk/profiles/uklomcore/uklomcore v0p2 may04.doc). We decided to use IEEE LOM because it is one of the most used in the community of learning objects. Users can retrieve LOs through searches in web repositories. Thus, the importance of high quality metadata is key for a successful retrieval. Recommender systems, based on metadata information and user profiles, arise to help people to retrieve the resources that are most appropriate to user's needs and preferences (Casali, Deco, Bender, & Gerling, 2012). Nevertheless, preparing learning resources with suitable metadata is labor-intensive and, consequently, there is a lack of quality information in these metadata. Michael Sonntag (2004) analyzes the importance of metadata for learning objects, as these resources may be reused often and possibly in different contexts. Some problems that he points are the lack of metadata, the diversity of standards, and the search engine support. Thus, the development of automatic/semi-automatic extraction systems seems to be a very important step towards solving this problem. Up to now, there are not many works on automatic metadata extraction. Each of the existent tools for metadata extraction has its own objectives, architecture and uses different techniques. For instance, some extractors systems can be seen in Alfano, Lenzitti, and Visalli (2007), Li, Dorai, and Farrell (2005), Motz et al. (2009), Pire, Espinase, Casali, and Deco (2011), and Tang (2007). In addition, there exist some metadata editors. For example, the Eureka project (http://eureka.ntic.org) is an initiative that provides a collective catalog of teaching and learning resources gathered by various organizations involved in the production of ITC educational resources. Eureka's shell is based on open source code. The data can be federated with other repositories built on a LOM application profile. Another project is Advanced Learning Object Hub Application ALOHA 1.3 (http://aloha.netera.ca/). The Learning Commons at the University of Calgary has done extensive research and development of tools and techniques to deal with workflow productivity issues related to content repurposing and metadata indexing. ALOHA's flexible interface is friendly for amateur users and customizable for the professional indexer. …

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.

Social data interoperability in educational repositories and federations

Several standard metadata notations exist for content items e.g., IEEE LOM or persons e.g., HR-XML. Additionally, a number of classes of metadata such as competence metadata, social metadata, etc. exist in ranges from strict vocabularies to folksonomies. For social metadata, however, several diverse schemata have been proposed, but no commonly agreed standard is available that describes the variety of interactions provided in socially powered platforms. In this paper, we propose a social metadata schema for repository federations containing educational resources ER which allows them to expose social data so they can be harvested and used by a variety of service providers. We base this schema on the analysis carried out in the context of the Open Discovery Space project, an EU funded initiative, which harvests information in the form of social metadata and encapsulates the user experience from several content providers' platforms and studies several solutions to their integration.

Socio-semantic Integration of Educational Resources - the Case of the mEducator Project

Research in technology-enhanced learning (TEL) throughout the last decade has largely focused on sharing and reusing educational resources and data. This effort has led to a fragmented landscape of competing metadata schemas, such as IEEE LOM or ADL SCORM, and interface mechanisms, such as OAI-PMH, SQI and REST-ful services in general. More recently, semantic technologies were taken into account to improve interoperability. However, so far Web-scale integration of resources is not facilitated, mainly due to the lack of take-up of shared principles, datasets and schemas. On the other hand, the Linked Data approach has emerged as the de facto standard for sharing data on the Web and is fundamentally based on established W3C standards. This paper presents results of the European Commission-funded project mEducator, which exploits Linked Data principles for (1) semantic integration and (2) social interconnecting of educational data, resources and actors. We describe a general approach to exploit the wealth of already existing educational data on the Web by allowing its exposure as Linked Data and by taking into account automated enrichment and interlinking techniques to provide a rich and well-interlinked graph for the educational domain. Additionally, the paper presents an evaluation of our work with respect to a set of socio- semantic dimensions. Experimental results demonstrate improved interoperability and retrievability of the resulting resource descriptions as part of an interlinked resource graph.

Automatic extraction of prerequisites and learning outcome from learning material

To provide an adaptive guidance to the student through learning object repositories, a system needs to have knowledge about the learner as well as material. This paper discusses identification of metadata that describes the pedagogical aspects of a document. Learning goals and prerequisites, if stored as part of metadata, can be utilised for intelligent recommendations. Manual annotation is a time consuming and expensive process. Correct instantiation of learning object metadata requires combined educational and technical skills. This paper proposes natural language processing-based automatic concept extraction and outlines rule-based approach for separation of prerequisite concepts and learning outcomes covered in learning document. The importance of automatic extraction of prerequisite terms is strengthened after evaluation of this work, since results show that concepts missed by authors are suggested by the system. To increase precision of retrieval, subject domain ontology is used.

A rule‐based system for hybrid search and delivery of learning objects to learners

PurposePresently, searching the internet for learning material relevant to ones own interest continues to be a time‐consuming task. Systems that can suggest learning material (learning objects) to a learner would reduce time spent searching for material, and enable the learner to spend more time for actual learning. The purpose of this paper is to present a system of “hybrid search and delivery of learning objects to learners”.Design/methodology/approachThis paper presents a system of “hybrid search and delivery of learning objects to learners” that combines the use of WordNet for semantic query expansion and an approach to personalized learning object delivery by suggesting relevant learning objects based on attributes specified in the learner's profile. The learning objects are related to the learner's attributes using the IEEE LOM and IMS LIP standards. The system includes a web crawler to collect learning objects from existing learning object repositories, such as NEEDS or SMETE.FindingsThe presented HSDLO system has the ability to accurately search and deliver learning objects of interest to a learner as well as adjust the learner's profile over time by evaluating the learner's preferences implicitly through the learning object selections.Research limitations/implicationsSince real LOM's from SMETE are not much populated, the system is tested with a limited set of attributes. The system is evaluated using a test bench rather than real learners.Originality/valueThe paper proposes a combination of three search techniques in one system as well an architectural solution which can be used for other types of online search engines.

An intelligent e‐learning software for learning to write correct Chinese characters on mobile devices

PurposeLearning Chinese is unquestionably very important and popular worldwide with the fast economic growth of China. To most foreigners and also local students, one of the major challenges in learning Chinese is to write Chinese characters in correct stroke sequences that are considered as significant in the Chinese culture. However, due to the potentially complicated structures of Chinese characters together with their stroke sequences, there are very few character recognition techniques that can effectively tackle the involved training task in an efficient and flexible manner. The purpose of this paper is to propose an intelligent and flexible e‐learning software based on learning objects to facilitate the learning of writing Chinese characters in correct stroke sequences.Design/methodology/approachThe paper adopts an incremental approach in designing the overall system architecture to emphasize on extendibility of the system. The basic features of the system including the evolution and pronunciation of each Chinese character can be embedded as a part of the learning object metadata to enhance students' understanding of Chinese characters. To demonstrate the feasibility of this proposal, a prototype of the proposed e‐learning software was built on smartphones such that students can learn anytime and anywhere.FindingsFrom the empirical evaluation of the e‐learning prototype for learning to write correct Chinese characters on mobile devices, it was found that foreign students can learn and practise the writing more effectively anytime and anywhere on their mobile devices after classes. Some initial positive feedback was collected. Furthermore, a more careful and thorough evaluation is planned to be conducted in relevant courses for foreign students in the upcoming Fall semester.Originality/valueThis proposal represents the first attempt to reduce the complexity while increasing the extendibility of the e‐learning software to learn Chinese through learning objects running on smartphones or mobile devices in general. More importantly, it opens up numerous opportunities for further investigations including possible integrations with other existing Chinese e‐learning systems.

Assessment Feedback supporting E-learning

With the rapidly development of distance learning and the XML (Extensible Markup Language) technology, metadata becomes an important item in an e-learning system. Today, many distance learning standards such as SCORM, AICC CMI, IEEE LTSC Learning Object Meta-data (LOM), and IMS Learning Resource Metadata XML Binding Specification, use metadata to tag learning materials, shareable content objects, and learning resources. However, most metadata is used to define learning materials and test problems. Few metadata is dedicated for assessment in learning. In this paper, we proposed an assessment metadata model for e-learning operations. With the support from the assessment metadata, we can collect information at the question cognition level, Item Difficulty Index, Item Discrimination Index, questionnaire style, and question style. The assessment analysis model provides individual questions, summary of test results, and analytical suggestions. The suggestions and results can tell teachers why a question is not suitable and how to correct it. Teachers can see the test result analysis and fix some problematic questions. With the cognition level analysis, teachers can correct their cognition level to avoid missing items in teaching. The mechanism developed also suggests to an e-learning system, to adaptive learning content and test to individual learners, as well as provides good advice to the teachers.

Mobile PointMe-based spatial haptic interaction with annotated media for learning purposes

In this paper, we propose a framework to facilitate handheld device-based PointMe interaction with annotated media content, where the user points his/her handheld device to the media presentation screen in order to access services/information from the annotated media. Each annotation is encoded into customized learning object metadata (LOM) format that provides access point for available information about a specific media on the screen. By incorporating a fusion of infra-red (IR) motion points and accelerometer data of the handheld device, the system determines the learner's PointMe location on the media screen. By using the pointed location spatial geometrical query is performed over the displayed annotated media in order to deliver learner's level-specific interactive learning content to the user's handheld device. The level customization is achieved by incorporating learner's age, progress, etc., parameters in the media content delivery process. We design intuitive learning scenarios by leveraging the PointMe interaction scheme, where learners are challenged to answer quiz questions in learning-based games. This real-world interaction technique with the annotated media and seamless virtual learning information acquisition process makes the system transparent to young learners and encourage them to engage and concentrate on their learning activities. We develop a prototype and perform experiments in a technology-augmented learning space to show the suitability of the proposed approach.

Supporting the Process of Developing and Managing LOM Application Profiles: The ASK-LOM-AP Tool

A number of international initiatives, such as the Open Educational Resources initiative, have recognized the potential value of sharing and reusing digital resources among educational communities. As a result, organizing, offering, and accessing these resources over the web have been key issues for both the research and the educational community. Within this framework, a popular way for describing digital educational resources is the IEEE Learning Object Metadata (LOM) Standard. On the other hand, it has been also recognized that it is not possible for a generic standard such as IEEE LOM to fully meet the specific requirements and accommodate the particular needs of different educational communities. Therefore, the practice of generating Application Profiles (APs) of the IEEE LOM has emerged and a number of different APs have been developed worldwide. However, despite the widespread development of APs it seems that only a limited number of software tools exist, to support the process of LOM Application Profiling. Within this context, in this paper we target addressing this problem by introducing a new web-based tool (ASK-LOM-AP) that aims to overcome the identified limitations of existing tools and facilitate its users to easily develop and manage LOM APs for different educational communities.

E-LORS: Uma Abordagem para Recomendação de Objetos de Aprendizagem

The use of recommender systems in order to improve human-computer interaction has increased significantly in applications as e-commerce and e-learning. Such systems aim at suggesting information and services that can be attractive to the users based on the automatic use of data that describe their preferences. In the educational area, the recommendation of relevant and interesting contents can be effective for catching the students' attention and driving higher motivation during the learning-teaching process. The ability to provide contents of their preferences considering the students' learning style may increase substantially the success of such effort. In this context, the goal of this work is to present the e-LORS system, an approach for recommendation of electronic content based on the relationship between learning styles and learning objects. In our methodology, the learning profiles are described according to discrete dimensions in order to support different perspectives concerning the students' preferences. Our recommendation methodology, in turn, uses these dimensions to filter the learning objects – described by the IEEE LOM standard – that are more suitable to the students.

The Education Data Warehouse: A Transformative Tool for Health Education Research

The Education Data Warehouse: A Transformative Tool for Health Education Research

A model for multi-label classification and ranking of learning objects

This paper describes an approach that uses multi-label classification methods for search tagged learning objects (LOs) by Learning Object Metadata (LOM), specifically the model offers a methodology that illustrates the task of multi-label mapping of LOs into types queries through an emergent multi-label space, and that can improve the first choice of learners or teachers. In order to build the model, the paper also proposes and preliminarily investigates the use of multi-label classification algorithm using only the LO features. As many LOs include textual material that can be indexed, and such indexes can also be used to filter the objects by matching them against user-provided keywords, we then did experiments using web classification with text features to compare the accuracy with the results from metadata (LO feature).

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.

Learners' perceptions on the importance of learning object metadata for relevance judgement

The purpose of this study was to investigate university students' perceptions on the importance of learning object metadata elements for judging the relevance of learning objects. In order to address this objective an online questionnaire survey was conducted using a clustered sampling technique. A total of 252 students responded to the questionnaire. Participants represented different disciplines (for example, from Humanities & Social Sciences to Sciences & Engineering), levels of familiarisation with the web (advanced and novice users) and educational level (postgraduates and undergraduates). The results showed that participants preferred content related and educational metadata elements as well as metadata elements about the date and the language of a learning object as opposed to technical metadata elements or metadata elements about the version of the learning object when judging the relevance of learning objects.