A linked dataset of medical educational resources

Reusable educational resources became increasingly important for enhancing learning and teaching experiences, particularly in the medical domain where resources are particularly expensive to produce. While interoperability across educational resources metadata repositories is yet limited to the heterogeneity of metadata standards and interface mechanisms with a lack of shared or aligned controlled vocabularies, Linked Data (LD) principles, based on W3C standards and supported through a wide range of tools, open up opportunities to alleviate such problems. We introduce the “mEducator Linked Educational Resources” dataset, which offers a range of open educational resources for the medical domain, exposed through LD principles. Data have been generated through a combination of manual curation and semi‐automated harvesting techniques, and state‐of‐the‐art enrichment and clustering techniques were deployed in order to classify and categorize data, toward improved reusability and access. Data are currently used by a range of educational applications and is accessible for third parties and developers, for instance through the LinkedUp Catalog and other registries, to facilitate further take‐up and applications

Using Linked Data in Learning Analytics

Learning Analytics has a lot to do with data, and the way to make sense of raw data in terms of the learner’s experience, behaviour and knowledge. In this article, we argue about the need for a closer relationship between the field of Learning Analytics and the one of Linked Data, which in our view constitutes an ideal data management layer for Learning Analytics. Based on our experience with organising the “Using Linked Data in Learning Analytics” tutorial at the Learning Analytics and Knowledge conference, we discuss the existing trends in the use of linked data and semantic web technologies, in general in education and in learning analytics specifically. We find that the emerging connections between the two fields are still, at the time of writing, much less prominent than one would expect considering the complementary nature of the considered technologies and practices. We therefore argue that specific efforts, somehow materialised through the tutorial and the work in the LinkedUp support action, are needed to ensure the realisation of the potential cross-benefits that combining Learning Analytics and Linked Data research could bring.LinkedU

The Semantically Rich Learning Environments: A Systematic Literature Review

Purpose: The research is intended to extract repetitive themes in the field of semantic-rich learning and to express the basic opportunities and challenges therein. Method: The method applied was to review the articles published in the WOS database, during the years 2000 to 2020 by using the paradigm funnel technique; moreover the Nvivo software was used for document analysis and theme extraction. Findings: In the study, it was found that establishing access to appropriate educational content, proper analysis and representation of knowledge, human capabilities enhancement, personalization of learning, and improving the quality of assessment, are the most important positive effects of using STs in learning; Also, in this study, nine themes and seven major challenges in the field of semantic-rich learning were identified. Conclusion: personalization and adaptation, and the development of various ontologies, are the most cited themes; and access to learning content and concerns about the design and development of learning systems are the most important challenges facing semantic-rich learning environments. We believe that in order to overcome the enumerated challenges, the combination of STs with other emerging cognitive and communication technologies, such as IoT, is necessary and could be the subject of future research in this field

Um modelo semântico para compartilhamento de recursos educacionais.

A representação precisa do conteúdo dos recursos educacionais torna mais fácil e rápido os processos de busca, seleção e reusabilidade destes recursos. Esta pesquisa discute problemas relacionados à representação de recursos educacionais, como: recursos com requisitos específicos, dependência entre recursos e estruturas heterogêneas para classificação dos recursos. Neste contexto, foi conduzido um estudo preliminar para caracterizar a ocorrência destes problemas no domínio da Ciência da Computação. Os resultados do estudo demonstraram a ocorrência e relevância destes problemas. Nesta perspectiva, este trabalho propõe um Modelo Conceitual para representação de recursos educacionais visando seu compartilhamento. Este modelo tem como diferencial mecanismos que permitem a definição de diferentes estruturas de curso e uma melhor caracterização do contexto dos recursos educacionais por meio da declaração explícita de requisitos específicos e dependência entre recurso. Para avaliação deste trabalho, foram realizados três processos envolvendo o Modelo Conceitual e o sistema de compartilhamento construído a partir deste modelo. O primeiro envolveu especialistas que responderam um questionário para indicar sua confiança na capacidade do modelo como solução para os problemas abordados na pesquisa. O segundo estudo buscou avaliar o processo de inserção de recursos no sistema de compartilhamento e a capacidade do Modelo Conceitual em representar recursos educacionais vindo de cursos disponibilizados na Web. Finalmente, o terceiro estudo buscou avaliar o acesso aos recursos do sistema de compartilhamento através da implementação de aplicações Web que fazem uso dos serviços de consulta. Os resultados mostram que o modelo é capaz de representar com maior precisão o contexto de criação dos recursos educacionais, contribuindo como solução para os problemas discutidos.The accurate representation of educational resources content makes the process of searching, selection and reusability much more easy and fast. This work discusses Open Issues related to representation of educational resources: i) resources with specific requirements, ii) dependence between resources and iii) heterogeneous structures for resources classification. In this context, this work conducted a preliminary study to characterize the occurrence of these problems in the field of computer science. The results showed that these problems are present and relevant in various areas of computing. Hence, this work introduce a new Conceptual Model to represent educational resources. This model allows the definition of different course structures and a better characterization of educational resources contexts by explicit declaration of specific requirementsand resources collections. To evaluate the proposed approach, three evaluation processes were conducted with the Conceptual Model and the sharing system that is based on this model. In the first study, a survey with teachers was conducted to verify their confidence in the effectiveness of this model. The second study aimed to evaluate the educational resources insertion process and the effectiveness of the Conceptual Model in the representation of educationa resources. Finally, the third study evaluated the access to educaional resources in the sharing system through the development of Web applications that use the query services. The obtained results show that the proposed model is capable of representing educational resources context and contributes in solving the discussed problems

Supporting the Discoverability of Open Educational Resources: on the Scent of a Hidden Treasury

Open Educational Resources (OERs), now available in large numbers, have a considerable potential to improve many aspects of society, yet one of the factors limiting this positive impact is the difficulty to discover them. This thesis investigates and proposes strategies to better support educators in discovering OERs. The literature suggests that the effectiveness of existing search systems, including for OER discovery, could be improved by supporting users, such as teachers, in carrying out more exploratory search activities closer to their existing methods of working. Hence, a preliminary taxonomy of OER-related search tasks was produced, based on an analysis of the literature, interpreted through Information Foraging Theory. This taxonomy was empirically evaluated to preliminarily identify a set of search tasks that involve finding other OERs similar to one that has already been identified, a process that is generally referred to as Query By Example (QBE). Following the Design Science Research methodology, three prototypes to support as well as to refine those tasks were iteratively designed, implemented, and evaluated involving an increasing number of educators in usability oriented studies. The resulting high-level and domain-oriented blended search/recommendation strategy transparently replicates Google searches in specialized networks, and identifies similar resources with a QBE strategy. It makes use of a domain-oriented similarity metric based on shared alignments to educational standards, and clusters results in expandable classes of comparable degrees of similarity. The summative evaluation shows that educators do appreciate this strategy because it is exploratory and – balancing similarity and diversity – it supports their high-level tasks, such as lesson planning and personalization of education. Finally, potential barriers and opportunities for the uptake of OER discovery tools were investigated in a structured interview study with experts from the OER field. Identified issues included how to work across multiple OER portals, variability in the use of metadata and how to align with the working practices of teachers. The findings of the thesis can be used to inform the research and development of methods and tools for OER discovery as well as their deployment to serve the needs of educators

Using Knowledge Anchors to Facilitate User Exploration of Data Graphs

YesThis paper investigates how to facilitate users’ exploration through data graphs for knowledge expansion. Our work focuses on knowledge utility – increasing users’ domain knowledge while exploring a data graph. We introduce a novel exploration support mechanism underpinned by the subsumption theory of meaningful learning, which postulates that new knowledge is grasped by starting from familiar concepts in the graph which serve as knowledge anchors from where links to new knowledge are made. A core algorithmic component for operationalising the subsumption theory for meaningful learning to generate exploration paths for knowledge expansion is the automatic identification of knowledge anchors in a data graph (KADG). We present several metrics for identifying KADG which are evaluated against familiar concepts in human cognitive structures. A subsumption algorithm that utilises KADG for generating exploration paths for knowledge expansion is presented, and applied in the context of a Semantic data browser in a music domain. The resultant exploration paths are evaluated in a task-driven experimental user study compared to free data graph exploration. The findings show that exploration paths, based on subsumption and using knowledge anchors, lead to significantly higher increase in the users’ conceptual knowledge and better usability than free exploration of data graphs. The work opens a new avenue in semantic data exploration which investigates the link between learning and knowledge exploration. This extends the value of exploration and enables broader applications of data graphs in systems where the end users are not experts in the specific domain

A multi-fold assessment framework for virtualized collaborative and social learning scenarios

Proposem un procés de virtualització de sessions col·laboratives en directe a partir de fòrums de discussió i xats web amb l'objectiu de produir recursos d'aprenentatge en línia interactius per a ser utilitzats pels alumnes i generar un efecte positiu en la participació de l'alumne. Per tal de millorar encara més la implicació de l'aprenentatge, vam dotar al nostre procés de virtualització d'un marc d'avaluació múltiple que proporciona la consciència efectiva i la retroalimentació constructiva als alumnes de la col·laboració original amb interaccions entre els membres del grup. La investigació presentada es centra en l'avaluació electrònica d'aprenentatge col·laboratiu i social i s'estén amb analítiques d'aprenentatge i tècniques d'anàlisi de xarxa social que són capaces d'analitzar i representar les interaccions cognitives i socials amb sessions de col·laboració en viu subjacents.Proponemos un proceso de virtualización de sesiones colaborativas en directo a partir de foros de discusión y chats web con el objetivo de producir recursos de aprendizaje en línea interactivos para ser utilizados por los alumnos y generar un efecto positivo en la participación del alumno. Con el fin de mejorar aún más la implicación del aprendizaje, dotamos a nuestro proceso de virtualización de un marco de evaluación múltiple que proporciona la conciencia efectiva y la retroalimentación constructiva a los alumnos de la colaboración original con interacciones entre los miembros del grupo. La investigación presentada se centra en la evaluación electrónica de aprendizaje colaborativo y social y se extiende con analíticas de aprendizaje y técnicas de análisis de red social que son capaces de analizar y representar las interacciones cognitivas y sociales con sesiones de colaboración en vivo subyacentes.We propose a virtualization process of live collaborative sessions from Web discussion forums and chats with the aim to produce interactive and attractive online learning resources to be used by learners, thus having a positive effect in learner engagement. In order to enhance further learning engagement, we endow our virtualization process with a multifold assessment framework that provides effective awareness and constructive feedback to learners from the original collaborative interactions among group members. The research presented focuses on e-assessment of collaborative and social learning and extends it with Learning Analytics and Social Network Analysis techniques that are able to analyse and represent cognitive and social interactions underlying live collaborative sessions