Active class discovery and learning for networked data

With the recent explosion of social network applications, active learning has increasingly become an important paradigm for classifying networked data. While existing research has shown promising results by exploiting network properties to improve the active learning performance, they are all based on a static setting where the number and the type of classes underlying the networked data remain stable and unchanged. For most social network applications, the dynamic change of users and their evolving relationships, along with the emergence of new social events, often result in new classes that need to be immediately discovered and labeled for classification. This paper proposes a novel approach called ADLNET for active class discovery and learning with networked data. Our proposed method uses the Dirichlet process defined over class distributions to enable active discovery of new classes, and explicitly models label correlations in the utility function of active learning. Experimental results on two real-world networked data sets demonstrate that our proposed approach outperforms other state-of-the-art methods

Learning with Graphs using Kernels from Propagated Information

Traditional machine learning approaches are designed to learn from independent vector-valued data points. The assumption that instances are independent, however, is not always true. On the contrary, there are numerous domains where data points are cross-linked, for example social networks, where persons are linked by friendship relations. These relations among data points make traditional machine learning diffcult and often insuffcient. Furthermore, data points themselves can have complex structure, for example molecules or proteins constructed from various bindings of different atoms. Networked and structured data are naturally represented by graphs, and for learning we aimto exploit their structure to improve upon non-graph-based methods. However, graphs encountered in real-world applications often come with rich additional information. This naturally implies many challenges for representation and learning: node information is likely to be incomplete leading to partially labeled graphs, information can be aggregated from multiple sources and can therefore be uncertain, or additional information on nodes and edges can be derived from complex sensor measurements, thus being naturally continuous. Although learning with graphs is an active research area, learning with structured data, substantially modeling structural similarities of graphs, mostly assumes fully labeled graphs of reasonable sizes with discrete and certain node and edge information, and learning with networked data, naturally dealing with missing information and huge graphs, mostly assumes homophily and forgets about structural similarity. To close these gaps, we present a novel paradigm for learning with graphs, that exploits the intermediate results of iterative information propagation schemes on graphs. Originally developed for within-network relational and semi-supervised learning, these propagation schemes have two desirable properties: they capture structural information and they can naturally adapt to the aforementioned issues of real-world graph data. Additionally, information propagation can be efficiently realized by random walks leading to fast, flexible, and scalable feature and kernel computations. Further, by considering intermediate random walk distributions, we can model structural similarity for learning with structured and networked data. We develop several approaches based on this paradigm. In particular, we introduce propagation kernels for learning on the graph level and coinciding walk kernels and Markov logic sets for learning on the node level. Finally, we present two application domains where kernels from propagated information successfully tackle real-world problems

Using pattern languages to mediate theory–praxis conversations in design for networked learning

Educational design for networked learning is becoming more complex but also more inclusive, with teachers and learners playing more active roles in the design of tasks and of the learning environment. This paper connects emerging research on the use of design patterns and pattern languages with a conception of educational design as a conversation between theory and praxis. We illustrate the argument by drawing on recent empirical research and literature reviews from the field of networked learning

Ritual performances and collective intelligence: theoretical frameworks for analyising activity patterns in Cloudworks

This paper provides an overview of emerging activity patterns on Cloudworks, a specialised site for sharing and debating ideas as well as resources on teaching, learning and scholarship in education. It provides an overview of activities such as 'flash debates', 'blended workshops' and 'open reviews' and seeks to situate dialogic interchanges and structures of involvement within the following theoretical frameworks: a) Goffman's notions of 'face-work' and 'ritual performance�; and b) and secondly, notions of collective intelligence. The paper argues that these perspectives can offer a unique contribution to the study and analysis of sociality (Bouman et al, 2007) bounded in the context of technologically mediated networked learning, with wider implications for understanding matters of participation, self-representation, reflection and expansion in education

Why (and How) Networks Should Run Themselves

The proliferation of networked devices, systems, and applications that we depend on every day makes managing networks more important than ever. The increasing security, availability, and performance demands of these applications suggest that these increasingly difficult network management problems be solved in real time, across a complex web of interacting protocols and systems. Alas, just as the importance of network management has increased, the network has grown so complex that it is seemingly unmanageable. In this new era, network management requires a fundamentally new approach. Instead of optimizations based on closed-form analysis of individual protocols, network operators need data-driven, machine-learning-based models of end-to-end and application performance based on high-level policy goals and a holistic view of the underlying components. Instead of anomaly detection algorithms that operate on offline analysis of network traces, operators need classification and detection algorithms that can make real-time, closed-loop decisions. Networks should learn to drive themselves. This paper explores this concept, discussing how we might attain this ambitious goal by more closely coupling measurement with real-time control and by relying on learning for inference and prediction about a networked application or system, as opposed to closed-form analysis of individual protocols

“Fairy rings” of participation: the invisible network influencing participation in online communities

Individuals participate in many different ways in online communities. There is an extensive body of research describing participation as a key metaphor in communities of practice and stressing that participatory mobility is influenced by underground multidirectional activities, directed away from the notion of periphery to the centre practices and taking the shape of expansive swarming and multidirectional pulsations. This article describes an ongoing observational study proposing a model that attempts to determine how users participate in online communities and what influences them to alter the way in which they participate. We performed daily observations on user participatory behaviour in 50 online communities using public domain – anonymous data available in the communities. The specific communities were selected because they are related to learning and support learning activities within their networks. The data observations collected were analysed using Compendium, a hypermedia knowledge mapping and sense-making tool, to represent and structure the data, make complex cross data queries, test hypotheses and build representation of real examples to support our claims. Initial findings indicate that users connect, participate, contribute and collaborate on a shared objective, transferring information and pooling knowledge within and between communities in four different modes. During their online journey, users switched between modes of participation or even remained in one specific mode, implying that the way in which users participate in an online community is not just related to the mode of participation and the level of engagement with the community but it is also due to hidden reasons or motivations, an invisible network of interactions of elements that affect the willingness of the user to participate. This layer is not immediately evident in the user actions but can be inferred by analysing user reactions. It is argued that user participation in online communities occurs in two layers; the “visible” layer of participation with the different modes; and the “invisible” layer of element interactions, similar to formations observed in nature when a radically spreading underground network of fungi activity results in a ring or arc formation of mushrooms, also known as a “fairy ring”. These underground multidirectional activities influence participation and participatory mobility. Following an open scientific inquiry approach and an open research paradigm we plan to share these observations with a wider audience of practitioners, researchers and theorists for all to test or contest our arguments, and to enrich, question, or support our model

Networked Learning in Context: What does e-learning offer students working independently, and what do they bring to it?

About the book:The chapters in this book, written by authors around the globe, provide detailed analysis and discussion of the changes distance education is going through as a result of technology. The authors originally presented their findings at the 21st World Conference of the International Council for Open and Distance Education (ICDE). The book is organized into three sections: Issues: provides a focus on issues that currently face those applying technology in distance education contexts. Teaching and learning with technology: offers a range of perspectives and case studies on current experience of technology's role in distance education. Innovative approaches: presents a series of examples of applications that are advancing the use of technology in distance education. The book will be of interest to all educators who seek to make use of new and emerging technologies to enrich their students's learning

The Impact of Using a Distance Learning Network on Building Teachers' Communities of Practice in Egypt

This research is a case study of using a distance learning network for teachers’ professional development in Egypt. It aims to investigate the impact of using Egypt’s National Network for Distance Training (NNDT) in developing teachers’ knowledge and on building communities of practice. It explores the role of professional development experts and teachers’ participation within the network. In addition, it draws attention to the teachers’ level of engagement in professional development programmes and, therefore, it reveals their modes of participation within the network. Moreover, it identifies the role of the technology in facilitating communication and collaboration between participants. Finally, this paper offers a number of recommendations that aim to develop the network capabilities to become a more ‘effective’ means for teachers’ professional development in Egypt

Networked Living: a new approach to teaching introductory ICT

The course T175 Networked Living is a 300 hour, multiple media, distance learning course offered by the UK Open University. The first presentation of the course, in 2005, attracted over 1600 students. T175 introduces students to general concepts of information and communication technology in a range of contexts, including: communication and identity; entertainment and information; and health, transport and government. It is an introductory (level 1) course for a variety of bachelors’ degrees, including the BSc programmes in: Information and Communication Technology; IT and Computing; and Technology; as well as the BEng engineering programme. The course was designed with a focus on retention of students and preparing them for further study. Student workload and pacing was carefully planned and there is a significant study skills component. The course uses a range of media, including: text, audio, computer animation and other software, and a website. Active learning is encouraged by means of activities, online quizzes, animations, spreadsheets and a learning journal. Continuous assessment is carried out via a mix of multiple-choice assignments (to test factual and numerical skills) and written assignments (which include elementary research into new topics). The course culminates with a written end-of-course assessment. This includes a major reflective component, as well as more traditional questions designed to test knowledge and understanding