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Version: acceptedVersion × Publisher: Piscataway, NJ : IEEE ×

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    A Data-Driven Approach for Analyzing Healthcare Services Extracted from Clinical Records
    (Piscataway, NJ : IEEE, 2020) Scurti, Manuel; Menasalvas-Ruiz, Ernestina; Vidal, Maria-Esther; Torrente, Maria; Vogiatzis, Dimitrios; Paliouras, George; Provencio, Mariano; Rodríguez-González, Alejandro; Seco de Herrera, Alba García; Rodríguez González, Alejandro; Santosh, K.C.; Temesgen, Zelalem; Soda, Paolo
    Cancer remains one of the major public health challenges worldwide. After cardiovascular diseases, cancer is one of the first causes of death and morbidity in Europe, with more than 4 million new cases and 1.9 million deaths per year. The suboptimal management of cancer patients during treatment and subsequent follows up are major obstacles in achieving better outcomes of the patients and especially regarding cost and quality of life In this paper, we present an initial data-driven approach to analyze the resources and services that are used more frequently by lung-cancer patients with the aim of identifying where the care process can be improved by paying a special attention on services before diagnosis to being able to identify possible lung-cancer patients before they are diagnosed and by reducing the length of stay in the hospital. Our approach has been built by analyzing the clinical notes of those oncological patients to extract this information and their relationships with other variables of the patient. Although the approach shown in this manuscript is very preliminary, it shows that quite interesting outcomes can be derived from further analysis. © 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.
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    Quality Prediction of Open Educational Resources A Metadata-based Approach
    (Piscataway, NJ : IEEE, 2020) Tavakoli, Mohammadreza; Elias, Mirette; Kismihók, Gábor; Auer, Sören; Chang, Maiga; Sampson, Demetrios G.; Huang, Ronghuai; Hooshyar, Danial; Chen, Nian-Shing; Kinshuk; Pedaste, Margus
    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.
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    Accessibility and Personalization in OpenCourseWare : An Inclusive Development Approach
    (Piscataway, NJ : IEEE, 2020) Elias, Mirette; Ruckhaus, Edna; Draffan, E.A.; James, Abi; Suárez-Figueroa, Mari Carmen; Lohmann, Steffen; Khiat, Abderrahmane; Auer, Sören; Chang, Maiga; Sampson, Demetrios G.; Huang, Ronghuai; Hooshyar, Danial; Chen, Nian-Shing; Kinshuk; Pedaste, Margus
    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.