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

Quality evaluation of open educational resources

Open Educational Resources (OER) are free and open-licensed educational materials widely used for learning. OER quality assessment has become essential to support learners and teachers in finding high-quality OERs, and to enable online learning repositories to improve their OERs. In this work, we establish a set of evaluation metrics that assess OER quality in OER authoring tools. These metrics provide guidance to OER content authors to create high-quality content. The metrics were implemented and evaluated within SlideWiki, a collaborative OpenCourseWare platform that provides educational materials in presentation slides format. To evaluate the relevance of the metrics, a questionnaire is conducted among OER expert users. The evaluation results indicate that the metrics address relevant quality aspects and can be used to determine the overall OER quality

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

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

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

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

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