Learning Object Repositories Research Articles

Learning to Love the LOR: Implementing an Internal Learning Object Repository at a Large National Organization

This article is a case study that discusses the development and implementation of an internal learning object repository (LOR) for a large national organization with a distributed working environment. It describes the organizational environment and why an internal LOR was needed. It describes technical considerations, including the various document management systems (DMS) available, and why an open source DMS was selected to use as an internal LOR. Lastly, this article weighs the benefits and challenges of implementing a LOR across a distributed working environment and reflects on challenges associated with implementation.

Using a Library Learning Object Repository to Empower Teaching Excellence for Distance Students

In 2015, in response to the findings of an online learning needs assessment, two librarians and a Web developer began creating a library learning object repository. This repository would ensure that distance learners were receiving excellent library services, including library instruction. The team worked with the libraries’ digital media specialist to create an attractive interface and consulted with one of the library’s metadata specialists to create metadata that would help to make the repository easy to search and browse. Further plans include wide-scale sharing with the larger library community and continued growth in the number and types of objects in the repository.

Utilization of Learning Object Repositories for the Analysis of Didactic Curriculum.

Utilization of Learning Object Repositories for the Analysis of Didactic Curriculum.

BROAD-RSI \u2013 educational recommender system using social networks interactions and linked data

There are several educational resources distributed in different repositories that address to a wide range of subjects and different educational goals. The proper choice of these educational resources is a challenge. Recommendation systems may help users in this task. In order to generate personalized recommendations, it is important to identify information that will help to define user profile and assist in identifying his/her interests. The constant and ever-increasing use of social networks allows the identification of different information about profile, interests, preferences, style and behavior from the spontaneous interaction. This paper presents an infrastructure able to extract users’ profile and educational context, from the Facebook social network and recommend educational resources. The proposal is supported by Information Extraction Techniques and Semantic Web technologies for extraction, enrichment and definition of user’s profile and interests. The recommendation approach is based on learning objects repositories, linked data and video repositories. It takes advantage of user’s spent time at the web. The proposal evaluation was made from the development of a prototype, three proofs of concept and a case study. The evaluation showed users’ acceptance of extracted information about their educational interests, automatically generated from social network and enriched to find implicit interests. It was also validated the possibility of people recommendation, enabling the establishment of interest network, based on a specific subject, showing good partners to study and research.

The Integrated Environment for Learning Objects Design and Storing in Semantic Web

There is a variety of tools and environments for Learning Objects (LOs) design and delivery as well as learning object repositories (LOR) but the researchers could not find a repository that includes both functions: creation and storing of LOs. A number of different integrated learning systems are suggested for users that demonstrate the variety of e-learning methods and semantic capabilities. LO repository oer.ndma.lt/lor, that we are going to present, is very friendly and interoperable to use and assure LO design, search in semantic web, adaptation of the re-used objects and storing. There are no more existing LO repositories with the functionality presented by researchers. Transformation of closed education into open one without existence of well-structured, multifunctional and integrated environment becomes problematic. Authors will present an integrated environment for the LO design, search in semantic web, adaptation and storing of newly designed or re-designed LO. Measures will support the transformation of closed education into open and will assure effective design, re-usability and adaptation of LO in the integrated environment.

Students’ Approaches to E-Learning: Analyzing Credit/Noncredit and High/Low Performers

Aim/Purpose: This study examines differences in credit and noncredit users’ learning and usage of the Plant Sciences E-Library (PASSEL, http://passel.unl.edu), a large international, open-source multidisciplinary learning object repository. Background: Advances in online education are helping educators to meet the needs of formal academic credit students, as well as informal noncredit learners. Since online learning attracts learners with a wide variety of backgrounds and intentions, it is important understand learner behavior so that instructional resources can be designed to meet the diversity of learner motivations and needs. Methodology: This research uses both descriptive statistics and cluster analysis. The descriptive statistics address the research question of how credit learners differ from noncredit learners in using an international e-library of learning objects. Cluster analysis identifies high and low credit/noncredit students based on their quiz scores and follow-up descriptive statistics to (a) differentiate their usage patterns and (b) help describe possible learning approaches (deep, surface, and strategic). Contribution: This research is unique in its use of objective, web-tracking data and its novel use of clustering and descriptive analytic approaches to compare credit and noncredit learners’ online behavior of the same educational materials. It is also one of the first to begin to identify learning approaches of the noncredit learner. Findings: Results showed that credit users scored higher on quizzes and spent more time on the online quizzes and lessons than did noncredit learners, suggesting their academic orientation. Similarly, high credit scorers spent more time on individual lessons and quizzes than did the low scorers. The most striking difference among noncredit learners was in session times, with the low scorers spending more time in a session, suggesting more browsing behavior. Results were used to develop learner profiles for the four groups (high/low quiz scorers x credit/noncredit). Recommendations for Practitioners: These results provide preliminary insight for instructors or instructional designers. For example, low scoring credit students are spending a reasonable amount of time on a lesson but still score low on the quiz. Results suggest that they may need more online scaffolding or auto-generated guidance, such as the availability of relevant animations or the need to review certain parts of a lesson based on questions missed. Recommendation for Researchers: The study showed the value of objective, web-tracking data and novel use of clustering and descriptive analytic approaches to compare different types of learners. One conclusion of the study was that this web-tracking data be combined with student self-report data to provide more validation of results. Another conclusion was that demographic data from noncredit learners could be instrumental in further refining learning approaches for noncredit learners. Impact on Society: Learning object repositories, online courses, blended courses, and MOOCs often provide learners the option of moving freely among educational content, choosing not only topics of interest but also formats of material they feel will advance their learning. Since online learning is becoming more prolific and attracts learners with a wide variety of backgrounds and intentions, these results show the importance of understanding learner behavior so that e-learning instructional resources can be designed to meet the diversity of learner motivations and needs. Future Research: Future research should combine web-tracking data with student self-report to provide more validation of results. In addition, collection of demographic data and disaggregation of noncredit student usage motivations would help further refining learning approaches for this growing population of online users.

ANALYSIS OF THE SCIENCE LEARNING OBJECTS OF THE GREEK DIGITAL LEARNING OBJECT REPOSITORY FROM A LEARNING ACTIVITIES PERSPECTIVE

In recent years, learning objects have been playing a crucial role in the teaching process. However, research focused on the analysis of science learning objects is particularly limited. The present study aims to analyze the science learning objects of the Greek Digital Learning Object Repository that are intended for primary school from a learning activities perspective. A total of 178 learning objects were analyzed. The analysis of learning objects from the perspective of the learning activities (cognitive and metacognitive) they activate in the students was carried out in line with the analysis framework of Overman, Vermunt, Meijer, Bulte and Brekelmans (2013). The analysis of learning objects showed that they are dominated by low level cognitive learning activities, while the learning objects that activate high level cognitive learning activities are limited. Finally, no learning objects activating metacognitive learning activities in the students were detected.

Recommendations for Building Adaptive Cognition-based E-Learning

Adapted e-Learning systems try to adapt the learning material based on the student’s preferences. Course authors design their courses with their students’ styles and in mind, course delivery should match the student style, and student assessment should also be adapted to match each specific student’s learning style, while student portfolio helps identifying the student model. To the best of our knowledge, no clear recommendation for building community wide adapted and personalized e-learning systems. This paper presents recommendations to add adaptation and personalization to one of the most common open source Learning Management System (LMS), Moodle. The adaptation features are based on using learning styles, ontology, and cognitive Bloom Taxonomy in building and presentation of the e-learning material (Learning Objects). This is helpful to establish adaptable and cognition-based Learning Object repository and course development centers.

ESTIMACIÓN DE CALIDAD DE OBJETOS DE APRENDIZAJE EN REPOSITORIOS DE RECURSOS EDUCATIVOS ABIERTOS BASADA EN LAS INTERACCIONES DE LOS ESTUDIANTES

Los recursos educativos abiertos se han erigido como uno de los pilares fundamentales de la educacion abierta. Una de las principales barreras que esta obstaculizando su uso y adopcion es la carencia de mecanismos de control de calidad efectivos y sostenibles en los repositorios. Estrategias de evaluacion como la revision por pares no han resultado lo suficientemente escalables para afrontar el ritmo de creacion de materiales abiertos por parte de la comunidad. El presente estudio muestra una nueva estrategia basada en analiticas de aprendizaje para estimar la calidad de los objetos de aprendizaje en base a las interacciones que los estudiantes tienen con ellos en entornos abiertos. Se analizaron 146291 sesiones de interaccion de estudiantes con 256 objetos de aprendizaje distribuidos a traves de un repositorio abierto. Un total de 11 interacciones estudiante-objeto de aprendizaje fueron consideradas en el estudio. La calidad de los recursos fue medida empleando el instrumento estandar de evaluacion LORI (Learning Object Review Instrument). Para estudiar las relaciones entre las interacciones de los estudiantes con los objetos de aprendizaje y la calidad de los mismos y para construir una metrica de prediccion se utilizaron analisis de regresion lineal. Los resultados muestran que existe relacion entre las interacciones y la calidad, y que es posible estimar con un error moderado la calidad de los objetos de aprendizaje en base a las interacciones que los estudiantes tienen con ellos. Los resultados obtenidos senalan que la analitica de aprendizaje propuesta puede ser utilizada en repositorios de objetos de aprendizaje abiertos para detectar automaticamente recursos conflictivos o de baja calidad.

Towards a holistic model for quality of learning object repositories

PurposeThis paper aims to address the need to ensure the quality of metadata records describing learning resources. We propose improvements to a metadata-quality model, specifically for the compliance sub-feature of the functionality feature. Compliance is defined as adherence level of the learning object metadata content to the metadata standard used for its specification. The paper proposes metrics to assess the compliance, which are applied to a set of learning objects, showing their applicability and usefulness in activities related to resources management.Design/methodology/approachThe methodology considers a first stage of metrics refinement to obtain the indicator of the sub-feature compliance. The next stage is the proposal evaluation, where it is determined if metrics can be used as a conformity indicator of learning object metadata with a standard (metadata compliance). The usefulness of this indicator in the information retrieval area is approached through an assessment of learning objects where the quality level of its metadata and the ranking in which they are retrieved by a repository are correlated.FindingsThis study confirmed that the best results for metrics of standardization, completeness, congruence, coherence, correctness and understandability, which determine the compliance indicator, were obtained for learning objects whose metadata were better labelled. Moreover, it was found that the learning objects with the highest level of compliance indicator have better positions in the ranking when a repository retrieves them through an exact search based on metadata.Research limitations/implicationsIn this study, only a sub-feature of the quality model is detailed, specifically the compliance of learning object standard. Another limitation was the size of the learning objects set used in the experiment.Practical implicationsThis proposal is independent from any metadata standard and can be applied to improve processes associated with the management of learning objects in a repository-like retrieval and recommendation.Originality/valueThe originality and value of this proposal are related to quality of learning object metadata considered from a holistic point of view through six metrics. These metrics quantify both technical and pedagogical aspects through automatic evaluation and supported by experts. In addition, the applicability of the indicator in recovery systems is shown, by example to be incorporated as an additional criterion in the learning object ranking.

Federated semantic search using terminological thesauri for learning object discovery

PurposeThe purpose of this paper is to propose a framework and system to address the inability to discover new and authentic learning material and the lack of a single access point for search and browsing of remote learning object repositories (LORs).Design/methodology/approachThe authors develop a framework for keyword-based query expansion using SKOS domain terminologies and implement a federated search mechanism integrating various disparate LORs within a learning management system (LMS).FindingsThe authors show that the expanded query achieves improved information gain and it is applied for federated information access, by simultaneously searching within a number of repositories. Results can be seamlessly aggregated back within the LMS and the course context.Practical implicationsIt is possible to retrieve additional learning objects (LOs) and achieve a corresponding increase in recall, while maintaining precision. SKOS expansion behaves well in a scholarly setting, which, combined with federated search, can contribute toward LOs’ discovery at a balanced cost. The system can be easily integrated with other platforms as well, building on open standards and RESTful communication.Originality/valueTo the authors’ knowledge, this is the first time SKOS-based query expansion is applied in a federated setting, and for the discovery and alignment of learning objects residing within LORs. The results show that this approach can achieve considerable information gain and that it is possible to strike a balance between search effectiveness, query drift and performance.

Personalized adaptive interfaces for supporting recommendation from learning object repositories

Personalized adaptive interfaces for supporting recommendation from learning object repositories

Modelo por capas para evaluación de la calidad de Objetos de Aprendizaje en repositorios

Los Objetos de Aprendizaje (OAs) son recursos digitales utilizados para apoyar los procesos de enseñanza y aprendizaje almacenados en Repositorios de OAs (ROAS), facilitando su búsqueda y recuperación. Sin embargo, en la mayoría de los casos es difícil determinar la calidad de estos recursos, por ello en este trabajo se presenta un modelo por capas para la evaluación de la calidad de OAs que identifica las principales características que deben cumplir estos recursos y la forma de determinar su cumplimiento a través de la especificación de diferentes métricas. Los resultados de la aplicación de la propuesta en la evaluación de los OAs de la Federación de Repositorios de Objetos de Aprendizaje (FROAC) demostraron la existencia de problemas en la calidad de estos recursos educativos, la importancia de la aplicación del modelo para apoyar el proceso de entrega de mejores objetos a partir de las búsquedas y su utilidad como herramienta de gestión en los ROAS.

Prerequisites between learning objects: Automatic extraction based on a machine learning approach

One standing problem in the area of web-based e-learning is how to support instructional designers to effectively and efficiently retrieve learning materials, appropriate for their educational purposes. Learning materials can be retrieved from structured repositories, such as repositories of Learning Objects and Massive Open Online Courses; they could also come from unstructured sources, such as web hypertext pages. Platforms for distance education often implement algorithms for recommending specific educational resources and personalized learning paths to students. But choosing and sequencing the adequate learning materials to build adaptive courses may reveal to be quite a challenging task.In particular, establishing the prerequisite relationships among learning objects, in terms of prior requirements needed to understand and complete before making use of the subsequent contents, is a crucial step for faculty, instructional designers or automated systems whose goal is to adapt existing learning objects to delivery in new distance courses. Nevertheless, this information is often missing. In this paper, an innovative machine learning-based approach for the identification of prerequisites between text-based resources is proposed. A feature selection methodology allows us to consider the attributes that are most relevant to the predictive modeling problem. These features are extracted from both the input material and weak-taxonomies available on the web. Input data undergoes a Natural language process that makes finding patterns of interest more easy for the applied automated analysis. Finally, the prerequisite identification is cast to a binary statistical classification task. The accuracy of the approach is validated by means of experimental evaluations on real online coursers covering different subjects.

A hybrid AHP-GA method for metadata-based learning object evaluation

A wide variety of demand in e-learning and web-based learning caused a new approach in e-content presentation. In order to accomplish these demands, learning object repositories (LORs) were developed. LORs have many learning objects (LOs) that are used to produce different types of e-content. When there are many LOs in LORs, the evaluation and selection of them become a subjective and time-consuming process. Thus, selecting the most suitable and best qualified LO is considered as a multi-criteria decision-making (MCDM) problem. In this study, a hybrid analytic hierarchy process genetic algorithm (AHP-GA) method was developed for the evaluation of LOs from web-based Intelligent Learning Object Framework (Zonesa) LOR. This proposed hybrid system was used in a real case study and the results demonstrated that the proposed system can be used effectively by both users and machines to produce content by the help of LO metadata.

A comparison among approaches for recommending learning objects through collaborative filtering algorithms

PurposeThe purpose of this paper is to present the results of a comparison among three different approaches for recommending learning objects (LO) inside a repository. The comparison focuses not only on prediction errors but also on the coverage of each tested configuration.Design/methodology/approachThe authors compared the offline evaluation by using pure collaborative filtering (CF) algorithms with two other different combinations of pre-processed data. The first approach for pre-processing data consisted of clustering users according to their disciplines resemblance, while the second approach consisted of clustering LO according to their textual similarity regarding title and description. The three methods were compared with respect to the mean average error between predicted values and real values. Moreover, we evaluated the impact of the number of clusters and neighborhood size on the user-coverage.FindingsClustering LO has improved the prediction error measure with a small loss on user-coverage when compared to the pure CF approach. On the other hand, the approach of clustering users failed in both the error and in user-space coverage. It also became clear that the neighborhood size is the most relevant parameter to determine how large the coverage will be.Research limitationsThe methods proposed here were not yet evaluated in a real-world scenario, with real users opinions about the recommendations and their respective learning goals. Future work is still required to evaluate users opinions.Originality/valueThis research provides evidence toward new recommendation methods directed toward LO repositories.

A Hybrid Recommendation Model for Learning Object Repositories

Learning Objects (LOs) have emerged as a cornerstone approach for the development and distribution of educational content. These resources are distributed by Learning Object Repositories (LORs), which can make it easier for users to find suitable LOs by using Recommender Systems (Rss). This paper presents a hybrid recommendation model for LORs that combines content-based, demographic and context-aware techniques, along with the use of quality and popularity metrics. This article also describes how the model has been used to implement two RSs for two real LORs: ViSH and Europeana. Each of these RSs was evaluated in terms of accuracy, utility,usability and satisfaction perceived by end users. Besides, an A/B testing was performed in ViSH to compare the recommendations of the RS with random suggestions. The results showed that the RSs had a high user acceptance in terms of utility, usability and satisfaction, and that the RSs significantly exceeded the performance achieved by the random recommendations.

Engineering of educational programs through the application of intelligent technologies

One of the key tasks of the present stage of the education system development in Russia is to improve the practical orientation of specialists’ training for the modern labor market. On the agenda, there are the issues of modernization of educational programs in the direction of a closer relationship between education and professional standards, which is the main purpose of this study. Rapidly changing business needs, especially in the field of the booming ITindustry indicate the need for continuous improvement of mechanisms for the acquisition of new knowledge, abilities and skills of students, which in turn requires the development of special tools for the development of educational technologies. The article discusses the issues of flexible engineering education programs of higher education in accordance with the needs of the labour market, presented in professional standards. The methods of semantic modeling of informational and educational space, allowing to systematize the knowledge of the professional area in the form of conceptual models of ontologies and repositories of learning objects are offered as the methods of engineering. As the result of the correlation analysis of the categories of existing educational and professional standards, the mechanism to overcome the contradictions between the language of professional competences of educational standards and requirements of the labour functions of the professional standards is proposed. The paper describes the characteristics of the knowledge and skills of educational programs in the categories: scope, sphere and types of tasks of professional activities. The professional competences are differentiated due to profiles of training and types of professional tasks. The proposed universal algorithm for the development of educational programs of profile training on the base of the analysis of generalized labour functions and labour functions of professional standards can be useful to specialists, involved in designing of the basic professional educational programs on a wide range of directions and profiles of training

Science Practices in the Science Learning Objects of the Greek Digital Learning Object Repository

Science Practices in the Science Learning Objects of the Greek Digital Learning Object Repository Efthymia Saltidou, Michael Skoumios Abstract Students’ engagement in science practices is considered particularly important for understanding science ideas and concepts. However, research studying science practices that are engaged in science instructional material, and especially in learning objects, is rather limited. The present study aims to analyze the science learning objects of the Greek Digital Learning Object Repository that are intended for primary school with regard to the science practices engaged in them. The analysis of the learning objects used an analysis framework based on the science practices that have been proposed by the US National Research Council. (NRC, 2012). Data analysis detected the dimensions of science practices engaged in learning objects. The analysis also showed that only some dimensions of science practices are engaged in learning objects, while other dimensions of science practices, especially important for understanding science ideas and concepts, are missing. Full Text: PDF DOI: 10.15640/jehd.v6n4a3

A framework for developing sustainable e-learning programmes

A framework was created at the University of the West Indies to guide the development of 18 e-learning programmes. The framework is based on three principles for sustainable e-learning design: (1) stakeholder-centredness; (2) cost-effectiveness and (3) high operational efficiency. These principles give rise to nine framework elements: (1) a labour market-driven programming agenda; (2) a continuous improvement quality assurance system; (3) international programme standards; (4) a costing model; (5) course rationalisation; (6) a learning object repository; (7) template-based document preparation; (8) project management and (9) an electronic project work-space. The framework enabled the 18 programmes to be developed on time and on budget. A post implementation staff survey and student course evaluations indicate broad satisfaction with the framework and its implementation. The paper concludes with a reflection on the lessons learned during the development and implementation of the framework.