Anotação semântica para recomendação de conteúdos educacionais

Orientador: Julio Cesar dos ReisDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Sistemas de apoio à aprendizagem exploram diversos recursos multimídia para considerar individualidades do aluno bem com diferentes estilos de aprendizagem. Todavia, a crescente quantidade de conteúdos educacionais disponíveis em diferentes formatos e de maneira fragmentada di?culta o acesso e compreensão dos conceitos em estudo. Embora a literatura tenha proposto abordagens para explorar técnicas de recomendação que permitem representação explícita de semântica por meio de artefatos como ontologias, essa linha não foi totalmente explorada e ainda requer muitos esforços de pesquisa. Esta pesquisa objetiva conceber um método de recomendação de conteúdo educacional explorando o uso de anotações semânticas sobre transcrições textuais de videoaulas. As anotações servem como metadados que expressam o signi?cado de trechos das aulas. A técnica de recomendação, como principal contribuição esperada, fundamenta-se nas anotações disponíveis para de?nir estratégias de ranking de conteúdos disponíveis a partir da proximidade semântica dos conceitos combinadas com técnicas de aprendizagem de máquina. A contribuição envolve o desenvolvimento de protótipos funcionais de software para validação experimental com base em conteúdos de videoaulas reais e deve destacar as principais vantagens e limitações da abordagem. Os resultados obtidos permitirão o acesso à recomendações mais adequadas para melhorar o processo de aprendizagem apresentando a possibilidade de uma experiência mais satisfatória pelos alunosAbstract: Learning support systems explore several audio-visual resources to consider individual needs and learning styles aiming to stimulate learning experiences. However, the large amount of online educational content in di?erent formats and the possibility of making them available in a fragmented way turns di?cult the tasks of accessing these resources and understanding the concepts under study. Although literature has proposed approachestoexploreexplicitsemanticrepresentationthroughartifactssuchasontologies in learning support systems, this research line still requires further investigation e?orts. In this MS.c. dissertation, we propose a method for recommending educational content by exploring the use of semantic annotations over textual transcriptions from video lectures. Our investigation addresses the di?culties in extracting entities from natural language texts in subtitles of videos. Our work studies how to re?ne concepts in a domain ontology to support semantic annotation of video lecture subtitles. We report on the design of a video lecture recommendation system which explores the extracted semantic annotations. Our solution explored semantically annotated videos with an ontology in the Computer Science domain. Obtained results indicate our recommendation mechanism is suited to ?lter relevant video content in di?erent use scenariosMestradoCiência da ComputaçãoMestre em Ciência da Computação2017/02325-5; 2018/00313-2FAPES

A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

People increasingly use videos on the Web as a source for learning. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning for the period from 2016 to 2021. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends of design guidelines to produce learning video

Web2Touch 2019: Semantic Technologies for Smart Information Sharing and Web Collaboration

This foreword introduces a summary of themes and papers of the Web2Touch (W2T) 2019 Track at the 28th IEEE WETICE Conference held in Capri, June 2019. W2T 2019 includes ten full papers and one short paper. They all address relevant issues in the field of information sharing for collaboration, including, big data analytics, knowledge engineering, linked open data, applications of smart Web technologies, and smart care. The papers are a portfolio of hot issues in research and applications of semantics, smart technologies (e.g., IoT, sensors, devices for tele-monitoring, and smart contents management) with crucial topics, such as big data analysis, knowledge representation, smart enterprise management, among the others. This track shows how cooperative technologies based on knowledge representation, intelligent tools, and enhanced Web engineering can enhance collaborative work through smart service design and delivery, so it contributes to radically change the role of the semantic Web and applications

Multimedia resource discovery

This chapter examines the challenges and opportunities of Multimedia Information Retrieval and corresponding search engine applications. Computer technology has changed our access to information tremendously: We used to search authors or titles (which we had to know) in library cards in order to locate relevant books; now we can issue keyword searches within the full text of whole book repositories in order to identify authors, titles and locations of relevant books. What about the corresponding challenge of finding multimedia by fragments, examples and excerpts? Rather than asking for a music piece by artist and title, can we hum its tune to find it? Can doctors submit scans of a patient to identify medically similar images of diagnosed cases in a database? Can your mobile phone take a picture of a statue and tell you about its artist and significance via a service that it sends this picture to? In an attempt to answer some of these questions we get to know basic concepts of multimedia resource discovery technologies for a number of different query and document types: piggy-back text search, i.e., reducing the multimedia to pseudo text documents; automated annotation of visual components; content-based retrieval where the query is an image; and fingerprinting to match near duplicates. Some of the research challenges are given by the semantic gap between the simple pixel properties computers can readily index and high-level human concepts; related to this is an inherent technological limitation of automated annotation of images from pixels alone. Other challenges are given by polysemy, i.e., the many meanings and interpretations that are inherent in visual material and the corresponding wide range of a user’s information need. This chapter demonstrates how these challenges can be tackled by automated processing and machine learning and by utilising the skills of the user, for example through browsing or through a process that is called relevance feedback, thus putting the user at centre stage. The latter is made easier by “added value” technologies, exemplified here by summaries of complex multimedia objects such as TV news, information visualisation techniques for document clusters, visual search by example, and methods to create browsable structures within the collection

Deliverable D2.3 Specification of Web mining process for hypervideo concept identification

This deliverable presents a state-of-art and requirements analysis report for the web mining process as part of the WP2 of the LinkedTV project. The deliverable is divided into two subject areas: a) Named Entity Recognition (NER) and b) retrieval of additional content. The introduction gives an outline of the workflow of the work package, with a subsection devoted to relations with other work packages. The state-of-art review is focused on prospective techniques for LinkedTV. In the NER domain, the main focus is on knowledge-based approaches, which facilitate disambiguation of identified entities using linked open data. As part of the NER requirement analysis, the first tools developed are described and evaluated (NERD, SemiTags and THD). The area of linked additional content is broader and requires a more thorough analysis. A balanced overview of techniques for dealing with the various knowledge sources (semantic web resources, web APIs and completely unstructured resources from a white list of web sites) is presented. The requirements analysis comes out of the RBB and Sound and Vision LinkedTV scenarios

Artificial Intelligence methodologies to early predict student outcome and enrich learning material

L'abstract è presente nell'allegato / the abstract is in the attachmen

Language technologies for a multilingual Europe

This volume of the series “Translation and Multilingual Natural Language Processing” includes most of the papers presented at the Workshop “Language Technology for a Multilingual Europe”, held at the University of Hamburg on September 27, 2011 in the framework of the conference GSCL 2011 with the topic “Multilingual Resources and Multilingual Applications”, along with several additional contributions. In addition to an overview article on Machine Translation and two contributions on the European initiatives META-NET and Multilingual Web, the volume includes six full research articles. Our intention with this workshop was to bring together various groups concerned with the umbrella topics of multilingualism and language technology, especially multilingual technologies. This encompassed, on the one hand, representatives from research and development in the field of language technologies, and, on the other hand, users from diverse areas such as, among others, industry, administration and funding agencies. The Workshop “Language Technology for a Multilingual Europe” was co-organised by the two GSCL working groups “Text Technology” and “Machine Translation” (http://gscl.info) as well as by META-NET (http://www.meta-net.eu)

MAC-REALM: A video content feature extraction and modelling framework

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A consequence of the ‘data deluge’ is the exponential increase in digital video footage, while the ability to find relevant video clips diminishes. Traditional text based search engines are no longer optimal for searching, as they cannot provide a granular search of the content inside video footage. To be able to search the video in a content based manner, the content features of the video need to be extracted and modelled into a content model, which can then act as a searchable proxy for the video content. This thesis focuses on the extraction of syntactic and semantic content features and content modelling, using machine driven processes, with either little or no user interaction. Our abstract framework design extracts syntactic and semantic content features and compiles them into an integrated content model. The framework integrates a four plane strategy that consists of a pre-processing plane that removes redundant data and filters the media to improve the feature extraction properties of the media; a syntactic feature extraction plane that extracts low level syntactic feature and mid-level syntactic features that have semantic attributes; a semantic relationship analysis and linkage plane, where the spatial and temporal relationships of all the content features are defined, and finally a content modelling stage where the syntactic and semantic content features are integrated into a content model. Each of the four planes can be split into three layers namely, the content layer, where the content to be processed is stored; the application layer, where the content is converted into content descriptions, and the MPEG-7 layer, where content descriptions are serialised. Using MPEG-7 standards to produce the content model will provide wide-ranging interoperability, while facilitating granular multi-content type searches. The framework is aiming to ‘bridge’ the semantic gap, by integrating the syntactic and semantic content features from extraction through to modelling. The design of the framework has been implemented into a prototype called MAC-REALM, which has been tested and evaluated for its effectiveness to extract and model content features. Conclusions are drawn about the research output as a whole and whether they have met the objectives. Finally, future work is presented on how concept detection and crowd sourcing can be used with MAC-REALM