Knowledge Cartography for Open Sensemaking Communities

Knowledge Cartography is the discipline of visually mapping the conceptual structure of ideas, such as the connections between issues, concepts, answers, arguments and evidence. The cognitive process of externalising one's understanding clarifies one's own grasp of the situation, as well as communicating it to others as a network that invites their contributions. This sensemaking activity lies at the heart of the Open Educational Resources movement's objectives. The aim of this paper is to describe the usage patterns of Compendium, a knowledge mapping tool from the OpenLearn OER project, using quantitative data from interaction logs and qualitative data from knowledge maps, forums and blog postings. This work explains nine roles played by maps in OpenLearn, and discusses some of the benefits and adoption obstacles, which motivate our ongoing work

Exploring user types and what users seek in an open content based educational resource

The OpenLearn site is an example of an Open Educational Resource (OER) providing units for free study and for re-use under the Creative Commons license. The primary focus of the site is content but it also offers social tools such as forums, personal journals, presence, and videoconferencing. The content can also support interactivity such as quizzes, opportunities for reflection, and interactive diagrams. This paper discusses desirable attributes for a learning environment suited to OERs and considers OpenLearn in the light of the four types of learning focus suggested by Bransford et al (2002) namely: learner, knowledge, community and assessment centred. A study of user views of OpenLearn is reviewed using cluster analysis to identify possible user types. The needs of these user types are then considered with a focus on assessment issues and possible responses suggested in the case of OpenLearn to help bring in assessment to informal learning resources

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Towards a killer app for the Semantic Web

Killer apps are highly transformative technologies that create new markets and widespread patterns of behaviour. IT generally, and the Web in particular, has benefited from killer apps to create new networks of users and increase its value. The Semantic Web community on the other hand is still awaiting a killer app that proves the superiority of its technologies. There are certain features that distinguish killer apps from other ordinary applications. This paper examines those features in the context of the Semantic Web, in the hope that a better understanding of the characteristics of killer apps might encourage their consideration when developing Semantic Web applications