Quality Prediction of Open Educational Resources A Metadata-based Approach

In the recent decade, online learning environments have accumulated millions of Open Educational Resources (OERs). However, for learners, finding relevant and high quality OERs is a complicated and time-consuming activity. Furthermore, metadata play a key role in offering high quality services such as recommendation and search. Metadata can also be used for automatic OER quality control as, in the light of the continuously increasing number of OERs, manual quality control is getting more and more difficult. In this work, we collected the metadata of 8,887 OERs to perform an exploratory data analysis to observe the effect of quality control on metadata quality. Subsequently, we propose an OER metadata scoring model, and build a metadata-based prediction model to anticipate the quality of OERs. Based on our data and model, we were able to detect high-quality OERs with the F1 score of 94.6%. © 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Metadata analysis of open educational resources

Open Educational Resources (OERs) are openly licensed educational materials that are widely used for learning. Nowadays, many online learning repositories provide millions of OERs. Therefore, it is exceedingly difficult for learners to find the most appropriate OER among these resources. Subsequently, the precise OER metadata is critical for providing high-quality services such as search and recommendation. Moreover, metadata facilitates the process of automatic OER quality control as the continuously increasing number of OERs makes manual quality control extremely difficult. This work uses the metadata of 8,887 OERs to perform an exploratory data analysis on OER metadata. Accordingly, this work proposes metadata-based scoring and prediction models to anticipate the quality of OERs. Based on the results, our analysis demonstrated that OER metadata and OER content qualities are closely related, as we could detect high-quality OERs with an accuracy of 94.6%. Our model was also evaluated on 884 educational videos from Youtube to show its applicability on other educational repositories

An OER Recommender System Supporting Accessibility Requirements

Open Educational Resources are becoming a significant source of learning that are widely used for various educational purposes and levels. Learners have diverse backgrounds and needs, especially when it comes to learners with accessibility requirements. Persons with disabilities have significantly lower employment rates partly due to the lack of access to education and vocational rehabilitation and training. It is not surprising therefore, that providing high quality OERs that facilitate the self-development towards specific jobs and skills on the labor market in the light of special preferences of learners with disabilities is difficult. In this paper, we introduce a personalized OER recommeder system that considers skills, occupations, and accessibility properties of learners to retrieve the most adequate and high-quality OERs. This is done by: 1) describing the profile of learners with disabilities, 2) collecting and analysing more than 1,500 OERs, 3) filtering OERs based on their accessibility features and predicted quality, and 4) providing personalised OER recommendations for learners according to their accessibility needs. As a result, the OERs retrieved by our method proved to satisfy more accessibility checks than other OERs. Moreover, we evaluated our results with five experts in educating people with visual and cognitive impairments. The evaluation showed that our recommendations are potentially helpful for learners with accessibility needs

CALLISTO Ontology

The CALLISTO ontology models the domain knowledge and geospatial semantics of the EU-funded CALLISTO project. CALLISTO aims to bridge the gap between Copernicus Data and Data and Information Access Service (DIAS) providers and users in different domains by providing AI solutions that effectively add value to large amounts of satellite data. The project focuses on the Earth Observations (EO) domain and its relationship with four Pilot Use Cases (PUCs): 1) Common Agricultural Policy (CAP) monitoring, 2) water quality assessment, 3) air quality assessment and journalism, and 4) land border surveillance. The CALLISTO ontology has been developed to handle this multi-domain data by offering a semantic representation of each domain and the connections between them. The ontology is reusing the CANDELA ontologies to represent geospatial data and satellite imagery. Meanwhile, additional concepts are introduced in the CALLISTO ontology to accurately capture the domain-specific definitions associated with the targeted PUCs. The objective behind developing the ontology is to convert the domain knowledge into a format that machines can understand and process, typically in the form of a Resource Description Framework (RDF). This transformation enables the application of data analytics techniques and the extraction of additional knowledge through automated inference. Applications such as geospatial question answering, geospatial data retrieval, and cross-domain semantic data-driven applications could use it as an underlying data source. The CALLISTO ontology is available in an RDF format; it is associated with Work Package WP6 within the CALLISTO project, which has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 101004152. For further details, please refer to deliverable D6.1: The CALLISTO ontologies and semantic indexing. The ontology and knowledge graph were developed by the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS). In collaboration with PUC members, the ontology was developed and evaluated. Following that, the task of incorporating data from PUC teams into the ontology resulted in the creation of the CALLISTO knowledge graph

CALLISTO Knowledge Graph

The CALLISTO knowledge graph represents various domains of knowledge addressed within the EU-funded CALLISTO project. CALLISTO aims to bridge the gap between Copernicus Data and Data and Information Access Service (DIAS) providers and users in different domains by providing AI solutions that effectively add value to large amounts of satellite data. The project focuses on the Earth Observations (EO) domain and its relationship with four Pilot Use Cases (PUCs): 1) Common Agricultural Policy (CAP) monitoring, 2) water quality assessment, 3) air quality assessment and journalism, and 4) land border surveillance. Each PUC addresses a particular domain and contributes multiple datasets to the project, which come in diverse formats (e.g., XML, JSON, CSV). The CALLISTO ontology has been developed to handle this multi-domain data by offering a semantic representation of each domain and the connections between them. The knowledge graph was then generated by mapping the PUCs datasets to the ontology using RDF Mapping Language (RML). Knowledge graphs aid in linking data from various domains, generating new knowledge, and inspecting recurring patterns that can be used in simulation and prediction models (i.e., using artificial intelligence and deep learning algorithms). Another benefit of employing KGs is the ability to link them to other Linked Open Data (LOD) for data integration and analytics. Applications such as geospatial question answering, geospatial data retrieval, and cross-domain semantic data-driven applications could use it as an underlying data source. The CALLISTO knowledge graph is available in an RDF format; it is associated with the CALLISTO ontology. For further details about the ontology, please refer to deliverable D6.1: The CALLISTO ontologies and semantic indexing. The ontology and knowledge graph were developed by the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS). In collaboration with PUC members, the ontology and knowledge graph were developed and evaluated

Ontology-based representation of learner profiles for accessible OpenCourseWare systems

The development of accessible web applications has gained significant attention over the past couple of years due to the widespread use of the Internet and the equality laws enforced by governments. Particularly in e-learning contexts, web accessibility plays an important role, as e-learning often requires to be inclusive, addressing all types of learners, including those with disabilities. However, there is still no comprehensive formal representation of learners with disabilities and their particular accessibility needs in e-learning contexts. We propose the use of ontologies to represent accessibility needs and preferences of learners in order to structure the knowledge and to access the information for recommendations and adaptations in e-learning contexts. In particular, we reused the concepts of the ACCESSIBLE ontology and extended them with concepts defined by the IMS Global Learning Consortium. We show how OpenCourseWare systems can be adapted based on this ontology to improve accessibility

Ontology-based representation for accessible OpenCourseWare systems

OpenCourseWare (OCW) systems have been established to provide open educational resources that are accessible by anyone, including learners with special accessibility needs and preferences. We need to find a formal and interoperable way to describe these preferences in order to use them in OCW systems and retrieve relevant educational resources. This formal representation should use standard accessibility definitions of OCW that can be reused by other OCW systems to represent accessibility concepts. In this article, we present an ontology to represent the accessibility needs of learners with respect to the IMS AfA specifications. The ontology definitions together with rule-based queries are used to retrieve relevant educational resources. Related to this, we developed a user interface component that enables users to create accessibility profiles representing their individual needs and preferences based on our ontology. We evaluated the approach with five examples profiles

Accessibility and Personalization in OpenCourseWare : An Inclusive Development Approach

OpenCourseWare (OCW) has become a desirable source for sharing free educational resources which means there will always be users with differing needs. It is therefore the responsibility of OCW platform developers to consider accessibility as one of their prioritized requirements to ensure ease of use for all, including those with disabilities. However, the main challenge when creating an accessible platform is the ability to address all the different types of barriers that might affect those with a wide range of physical, sensory and cognitive impairments. This article discusses accessibility and personalization strategies and their realisation in the SlideWiki platform, in order to facilitate the development of accessible OCW. Previously, accessibility was seen as a complementary feature that can be tackled in the implementation phase. However, a meaningful integration of accessibility features requires thoughtful consideration during all project phases with active involvement of related stakeholders. The evaluation results and lessons learned from the SlideWiki development process have the potential to assist in the development of other systems that aim for an inclusive approach. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

SlideWiki: towards a collaborative and accessible platform for slide presentations

OpenCourseWare platforms for educational resources have the potential to open new horizons for knowledge sharing and e-learning by reaching learners beyond the constraints of traditional learning systems. SlideWiki is a crowd-sourcing platform that aims to rethink the creation and sharing of knowledge by providing an environment where authors can collaborate, reuse, adapt and share slide contents for educational purposes. As an OpenCourseWare platform, SlideWiki intends to make Open Educational Resources more accessible for all users, including those with disabilities, within formal and informal learning settings. Moreover, the platform offers collaborative tools that enable authors and contributors to translate the slide content. To address the implementation, scalability, usability, and adoption of the platform, it has been designed and deployed in many different learning settings with large-scale trials across Europe. At the time of writing, 56 trials have taken place in different geographical and cultural regions, organizational units, and institutions, covering various teaching and learning scenarios. The experiences and feedback from the trials have influenced the redesign of SlideWiki in terms of accessibility and openness. This paper discusses the findings of the large-scale trials and how they influenced the technical redesign of the platform. It also shows how incorporating user feedback into the technical development process can improve accessibility and collaboration.</p

El extrañamiento cultural en espacios migratorios La juventud andaluza ante el reto de la multiculturalidad

In this article, the authors seek to defi ne the system of cultural elements that cause cultural shock between Andalusian youths and the immigrant population. The interpretation of foreign cultures serves as the basis for analysing the origin of the problem and offers scope for refl ection in the face of the need for a new perspective on ethnic communication. The results show that the perception of the other is based on negative stereotypes that prevent peaceful multicultural coexistence