6 research outputs found

    Collaborative Authoring of Open Courseware with SlideWiki: A Case Study in Open Education

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    Producing or finding and reusing high-quality educational content online can be a laborious and costly process. With the open-source and open-access SlideWiki platform, the effort of producing and reusing highly-structured remixable educational content can be crowdsourced and therefore widely shared. SlideWiki employs crowdsourcing methods in order to support the open education community in authoring, sharing, reusing and remixing open courseware. This paper presents a case study of this platform carried out in the context of open education and informal learning and reports on the feedback received thus far from members of the open education community

    A Semantics-based User Interface Model for Content Annotation, Authoring and Exploration

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    The Semantic Web and Linked Data movements with the aim of creating, publishing and interconnecting machine readable information have gained traction in the last years. However, the majority of information still is contained in and exchanged using unstructured documents, such as Web pages, text documents, images and videos. This can also not be expected to change, since text, images and videos are the natural way in which humans interact with information. Semantic structuring of content on the other hand provides a wide range of advantages compared to unstructured information. Semantically-enriched documents facilitate information search and retrieval, presentation, integration, reusability, interoperability and personalization. Looking at the life-cycle of semantic content on the Web of Data, we see quite some progress on the backend side in storing structured content or for linking data and schemata. Nevertheless, the currently least developed aspect of the semantic content life-cycle is from our point of view the user-friendly manual and semi-automatic creation of rich semantic content. In this thesis, we propose a semantics-based user interface model, which aims to reduce the complexity of underlying technologies for semantic enrichment of content by Web users. By surveying existing tools and approaches for semantic content authoring, we extracted a set of guidelines for designing efficient and effective semantic authoring user interfaces. We applied these guidelines to devise a semantics-based user interface model called WYSIWYM (What You See Is What You Mean) which enables integrated authoring, visualization and exploration of unstructured and (semi-)structured content. To assess the applicability of our proposed WYSIWYM model, we incorporated the model into four real-world use cases comprising two general and two domain-specific applications. These use cases address four aspects of the WYSIWYM implementation: 1) Its integration into existing user interfaces, 2) Utilizing it for lightweight text analytics to incentivize users, 3) Dealing with crowdsourcing of semi-structured e-learning content, 4) Incorporating it for authoring of semantic medical prescriptions

    Hybrid human-AI driven open personalized education

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    Attaining those skills that match labor market demand is getting increasingly complicated as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Furthermore, people's interests in gaining knowledge pertaining to their personal life (e.g., hobbies and life-hacks) are also increasing dramatically in recent decades. In this situation, anticipating and addressing the learning needs are fundamental challenges to twenty-first century education. The need for such technologies has escalated due to the COVID-19 pandemic, where online education became a key player in all types of training programs. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open/free educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. Therefore, this thesis aims to contribute to the literature about the utilization of (open and free-online) educational resources toward goal-driven personalized informal learning, by developing a novel Human-AI based system, called eDoer. In this thesis, we discuss all the new knowledge that was created in order to complete the system development, which includes 1) prototype development and qualitative user validation, 2) decomposing the preliminary requirements into meaningful components, 3) implementation and validation of each component, and 4) a final requirement analysis followed by combining the implemented components in order develop and validate the planned system (eDoer). All in all, our proposed system 1) derives the skill requirements for a wide range of occupations (as skills and jobs are typical goals in informal learning) through an analysis of online job vacancy announcements, 2) decomposes skills into learning topics, 3) collects a variety of open/free online educational resources that address those topics, 4) checks the quality of those resources and topic relevance using our developed intelligent prediction models, 5) helps learners to set their learning goals, 6) recommends personalized learning pathways and learning content based on individual learning goals, and 7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by the pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal eDoer recommendations attain higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported as a statistically significant result

    SlideWiki - Towards a collaborative and accessible platform for slide presentations

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    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

    SlideWiki: towards a collaborative and accessible platform for slide presentations

    No full text
    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
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