Collaborative Authoring of Adaptive Educational Hypermedia by Enriching a Semantic Wiki’s Output

This research is concerned with harnessing collaborative approaches for the authoring of Adaptive Educational Hypermedia (AEH) systems. It involves the enhancement of Semantic Wikis with pedagogy aware features to this end. There are many challenges in understanding how communities of interest can efficiently collaborate for learning content authoring, in introducing pedagogy to the developed knowledge models and in specifying user models for efficient delivery of AEH systems. The contribution of this work will be the development of a model of collaborative authoring which includes domain specification, content elicitation, and definition of pedagogic approach. The proposed model will be implemented in a prototype AEH authoring system that will be tested and evaluated in a formal education context

The Conference Review Process

This presentation is for students on the 3rd year ECS Multimedia course where students run their own conference, and submit and review papers. In this presentation we explain the academic review process, look at the structure of a review, and give some examples of positive and negative reviews

Where are your Manners? Sharing Best Community Practices in the Web 2.0

The Web 2.0 fosters the creation of communities by offering users a wide array of social software tools. While the success of these tools is based on their ability to support different interaction patterns among users by imposing as few limitations as possible, the communities they support are not free of rules (just think about the posting rules in a community forum or the editing rules in a thematic wiki). In this paper we propose a framework for the sharing of best community practices in the form of a (potentially rule-based) annotation layer that can be integrated with existing Web 2.0 community tools (with specific focus on wikis). This solution is characterized by minimal intrusiveness and plays nicely within the open spirit of the Web 2.0 by providing users with behavioral hints rather than by enforcing the strict adherence to a set of rules.Comment: ACM symposium on Applied Computing, Honolulu : \'Etats-Unis d'Am\'erique (2009

Social Web Communities

Blogs, Wikis, and Social Bookmark Tools have rapidly emerged onthe Web. The reasons for their immediate success are that people are happy to share information, and that these tools provide an infrastructure for doing so without requiring any specific skills. At the moment, there exists no foundational research for these systems, and they provide only very simple structures for organising knowledge. Individual users create their own structures, but these can currently not be exploited for knowledge sharing. The objective of the seminar was to provide theoretical foundations for upcoming Web 2.0 applications and to investigate further applications that go beyond bookmark- and file-sharing. The main research question can be summarized as follows: How will current and emerging resource sharing systems support users to leverage more knowledge and power from the information they share on Web 2.0 applications? Research areas like Semantic Web, Machine Learning, Information Retrieval, Information Extraction, Social Network Analysis, Natural Language Processing, Library and Information Sciences, and Hypermedia Systems have been working for a while on these questions. In the workshop, researchers from these areas came together to assess the state of the art and to set up a road map describing the next steps towards the next generation of social software

Personalization in cultural heritage: the road travelled and the one ahead

Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user (e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed

A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in users’ feedback. Second, web analytics data is analyzed to get an insight into users’ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in user’s interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts

SOLAR: Social Link Advanced recommendation system

In today’s information society, precise descriptions of the massive volume of online content available are crucial for responding to user needs adequately and efficiently. The Semantic Web Paradigm has recently advanced across many domains for the assignment of metadata to Internet content, in order to define it with explicit, machine-readable meaning. This content has become so extensive that it must be refined according to user preferences to avoid information overload. The current paper proposes a framework for the association of semantic data to webpage links based on a specific domain ontology, additionally permitting the user to express his opinion regarding his emotions about the content of the link. This data is further exploited to suggest additional links to the user, based on the semantic metadata and the level of user satisfaction with previously viewed content. A comprehensive evaluation of the tool has demonstrated a high level of user satisfaction with the features of the system.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the project SONAR (TSI-340000-2007-212), GODO2 (TSI-020100-2008-564) and SONAR2 (TSI-020100-2008-665), under the PIBES project of the Spanish Committee of Education & Science (TEC2006-12365-C02-01) and the MID-CBR project of the Spanish Committee of Education & Science (TIN2006-15140-C03-02)

Internet based molecular collaborative and publishing tools

The scientific electronic publishing model has hitherto been an Internet based delivery of electronic articles that are essentially replicas of their paper counterparts. They contain little in the way of added semantics that may better expose the science, assist the peer review process and facilitate follow on collaborations, even though the enabling technologies have been around for some time and are mature. This thesis will examine the evolution of chemical electronic publishing over the past 15 years. It will illustrate, which the help of two frameworks, how publishers should be exploiting technologies to improve the semantics of chemical journal articles, namely their value added features and relationships with other chemical resources on the Web. The first framework is an early exemplar of structured and scalable electronic publishing where a Web content management system and a molecular database are integrated. It employs a test bed of articles from several RSC journals and supporting molecular coordinate and connectivity information. The value of converting 3D molecular expressions in chemical file formats, such as the MOL file, into more generic 3D graphics formats, such as Web3D, is assessed. This exemplar highlights the use of metadata management for bidirectional hyperlink maintenance in electronic publishing. The second framework repurposes this metadata management concept into a Semantic Web application called SemanticEye. SemanticEye demonstrates how relationships between chemical electronic articles and other chemical resources are established. It adapts the successful semantic model used for digital music metadata management by popular applications such as iTunes. Globally unique identifiers enable relationships to be established between articles and other resources on the Web and SemanticEye implements two: the Document Object Identifier (DOI) for articles and the IUPAC International Chemical Identifier (InChI) for molecules. SemanticEye’s potential as a framework for seeding collaborations between researchers, who have hitherto never met, is explored using FOAF, the friend-of-a-friend Semantic Web standard for social networks