Knowledge Extraction from Textual Resources through Semantic Web Tools and Advanced Machine Learning Algorithms for Applications in Various Domains

Nowadays there is a tremendous amount of unstructured data, often represented by texts, which is created and stored in variety of forms in many domains such as patients' health records, social networks comments, scientific publications, and so on. This volume of data represents an invaluable source of knowledge, but unfortunately it is challenging its mining for machines. At the same time, novel tools as well as advanced methodologies have been introduced in several domains, improving the efficacy and the efficiency of data-based services. Following this trend, this thesis shows how to parse data from text with Semantic Web based tools, feed data into Machine Learning methodologies, and produce services or resources to facilitate the execution of some tasks. More precisely, the use of Semantic Web technologies powered by Machine Learning algorithms has been investigated in the Healthcare and E-Learning domains through not yet experimented methodologies. Furthermore, this thesis investigates the use of some state-of-the-art tools to move data from texts to graphs for representing the knowledge contained in scientific literature. Finally, the use of a Semantic Web ontology and novel heuristics to detect insights from biological data in form of graph are presented. The thesis contributes to the scientific literature in terms of results and resources. Most of the material presented in this thesis derives from research papers published in international journals or conference proceedings

Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education

Visualization of analytic provenance for sensemaking

Sensemaking is an iterative and dynamic process, in which people collect data relevant to their tasks, analyze the collected information to produce new knowledge, and possibly inform further actions. During the sensemaking process, it is difficult for the human’s working memory to keep track of the progress and to synthesize a large number of individual findings and derived hypotheses, thus limits the performance. Analytic provenance captures both the data exploration process and and its accompanied reasoning, potentially addresses these information overload and disorientation problems. Visualization can help recall, revisit and reproduce the sensemaking process through visual representations of provenance data. More interesting and challenging, analytic provenance has the potential to facilitate the ongoing sensemaking process rather than providing only post hoc support. This thesis addresses the challenge of how to design interactive visualizations of analytic provenance data to support such an iterative and dynamic sensemaking. Its original contribution includes four visualizations that help users explore complex temporal and reasoning relationships hidden in the sensemaking problems, using both automatically and manually captured provenance. First SchemaLine, a timeline visualization, enables users to construct and refine narratives from their annotations. Second, TimeSets extends SchemaLine to explore more complex relationships by visualizing both temporal and categorical information simultaneously. Third, SensePath captures and visualizes user actions to enable analysts to gain a deep understanding of the user’s sensemaking process. Fourth, SenseMap visualization prevents users from getting lost, synthesizes new relationship from captured information, and consolidates their understanding of the sensemaking problem. All of these four visualizations are developed using a user-centered design approach and evaluated empirically to explore how they help target users make sense of their real tasks. In summary, this thesis contributes novel and validated interactive visualizations of analytic provenance data that enable users to perform effective sensemaking

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks

Artificial intelligence in higher education industry : just a brief introduction to complexity of an issue of future challenges

Purpose: The article was written for review purposes in order to bring the definition of artificial intelligence closer and briefly present the possibilities of its use in management and economic sciences, as well as in higher education. Design/methodology/approach: In order to obtain the desired information, the author conducted a research of the scientific papers on the relationship between higher education and artificial intelligence and extracted the most important conclusions and theories. Findings: The review of the literature allowed the author to determine that there are many applications for artificial intelligence in higher education, but it should be noted that it should always be under human control and verification. Originality/value: Apart from a brief attempt at the definition of AI and its use in higher education, the author also presents a critical perspective and possible threats, as well as proposes solutions that can regulate the ways of using artificial intelligence not only in higher education, but also in other areas of industry and social life

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

DECONSTRUCTING MADRASAH TSANAWIYAH ENGLISH LEARNING MATERIALS BASED ON MICROLEARNING

Millennials, especially Madrasah Tsanawiyah students, that very familiar with digital media and technology, need a well-constructed learning materials that can provide information in a bite-size and accessible form, which is called microlearning. This study aims to analyse the use of microlearning in the existing learning materials, to develop and describe deconstruction in microlearning in English learning materials for Madrasah Tsanawiyah (MTs). This study uses qualitative approach with content analysis method to analyze 22 learning materials from class 7th – 9th consist of PowerPoint, YouTube video, learning video, and e-module. The result of this study found that most Madrasah Tsanawiyah English learning materials were not based on microlearning. From the analysis, it shows the learning materials for Madrasah Tsanawiyah students fulfil almost all of the 28 indicators were based on microlearning per-learning materials but this not indicate the learning material is microlearning-based. In addition, for the deconstruction, 36 basic competences were analysed and it was found the 392 microlearning-based learning object materials formed out of 392 instructional methods. It can be concluded that although learning materials are suitable for Madrasah Tsanawiyah students and some of them, such as PowerPoint, E-module, and video can be considered as microlearning object learning materials, they are not arranged in such a way as to allow them to be used as microlearning-based Madrasah Tsanawiyah learning materials. Generasi milenial, khususnya siswa Madrasah Tsanawiyah, yang sangat akrab dengan media dan teknologi digital, membutuhkan materi pembelajaran yang dibangun dengan baik yang dapat memberikan informasi dalam bentuk yang mudah diakses dan form yang dapat diakses, yang disebut microlearning. Penelitian ini bertujuan untuk menganalisis penggunaan microlearning pada materi pembelajaran yang ada, untuk mengembangkan dan mendeskripsikan dekonstruksi dalam microlearning dalam materi pembelajaran bahasa Inggris untuk Madrasah Tsanawiyah (MTs). Penelitian ini menggunakan pendekatan kualitatif dengan metode analisis konten untuk menganalisis 22 materi pembelajaran dari kelas 7 – 9 yang terdiri dari PowerPoint, video YouTube, video pembelajaran, dan e-modul. Hasil penelitian ini menemukan bahwa sebagian besar materi pembelajaran bahasa Inggris Madrasah Tsanawiyah tidak berbasis microlearning. Dari analisis tersebut menunjukkan materi pembelajaran siswa Madrasah Tsanawiyah memenuhi hampir semua dari 28 indikator tersebut didasarkan pada materi pembelajaran mikro per-pembelajaran namun hal ini tidak menunjukkan materi pembelajaran berbasis microlearning. Selain itu, untuk dekonstruksi, 36 kompetensi dasar dianalisis dan ditemukan 392 materi objek pembelajaran berbasis pembelajaran mikro yang terbentuk dari 392 metode instruksional. Dapat disimpulkan bahwa meskipun materi pembelajaran cocok untuk siswa Madrasah Tsanawiyah dan beberapa diantaranya, seperti PowerPoint, E-modul, dan video dapat dianggap sebagai materi pembelajaran objek pembelajaran mikro, namun tidak disusun sedemikian rupa sehingga memungkinkan mereka untuk digunakan sebagai bahan pembelajaran Madrasah Tsanawiyah berbasis microlearning

LifeLogging: personal big data

We have recently observed a convergence of technologies to foster the emergence of lifelogging as a mainstream activity. Computer storage has become significantly cheaper, and advancements in sensing technology allows for the efficient sensing of personal activities, locations and the environment. This is best seen in the growing popularity of the quantified self movement, in which life activities are tracked using wearable sensors in the hope of better understanding human performance in a variety of tasks. This review aims to provide a comprehensive summary of lifelogging, to cover its research history, current technologies, and applications. Thus far, most of the lifelogging research has focused predominantly on visual lifelogging in order to capture life details of life activities, hence we maintain this focus in this review. However, we also reflect on the challenges lifelogging poses to an information retrieval scientist. This review is a suitable reference for those seeking a information retrieval scientist’s perspective on lifelogging and the quantified self