Fostering Open Sensemaking Communities by Combining Knowledge Maps and Videoconferencing

In this paper, our aim is to investigate the role of Compendium maps for both learners and educators to share and debate interpretations in FlashMeetingTM (FM) videoconferences in the context of OpenLearn, an online environment for open learning. This work is based on a qualitative study of knowledge maps and web videoconferencing interactions, and quantitative data presented in diagnostic reports about both tools. Our theoretical approach is based on the sensemaking concept and an existing framework for three learning scenarios. Our findings describe four applications of knowledge maps in videoconferencing: (i) Mind Maps for a FM virtual lecture (transmission scenario); (ii) Learning Path Map which integrates a FM conference (studio scenario); (iii) Concept Maps during a peer-to-peer event (negotiation scenario) and (iv) Web Maps for a FM replay (assessment scenario)

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

Network Model Selection for Task-Focused Attributed Network Inference

Networks are models representing relationships between entities. Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). Whether given or inferred, choosing the best representation affects subsequent tasks and questions on the network. This work focuses on model selection to evaluate network representations from data, focusing on fundamental predictive tasks on networks. We present a modular methodology using general, interpretable network models, task neighborhood functions found across domains, and several criteria for robust model selection. We demonstrate our methodology on three online user activity datasets and show that network model selection for the appropriate network task vs. an alternate task increases performance by an order of magnitude in our experiments

On Classification with Bags, Groups and Sets

Many classification problems can be difficult to formulate directly in terms of the traditional supervised setting, where both training and test samples are individual feature vectors. There are cases in which samples are better described by sets of feature vectors, that labels are only available for sets rather than individual samples, or, if individual labels are available, that these are not independent. To better deal with such problems, several extensions of supervised learning have been proposed, where either training and/or test objects are sets of feature vectors. However, having been proposed rather independently of each other, their mutual similarities and differences have hitherto not been mapped out. In this work, we provide an overview of such learning scenarios, propose a taxonomy to illustrate the relationships between them, and discuss directions for further research in these areas

Transfer Learning across Networks for Collective Classification

This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances are assumed to be independent and identically distributed, how to effectively transfer knowledge across different information networks has not been well studied, mainly because networks may have their distinct node features and link relationships between nodes. In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks. The proposed algorithm discovers these latent features by constructing label propagation matrices in the source and target networks, and mapping them into a shared latent feature space. The latent features capture common structure patterns shared by two networks, and serve as domain-independent features to be transferred between networks. Together with domain-dependent node features, we thereafter propose an iterative classification algorithm that leverages label correlations to predict node labels in the target network. Experiments on real-world networks demonstrate that our proposed algorithm can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.Comment: Published in the proceedings of IEEE ICDM 201

OpenLearn and knowledge maps for language learning

This chapter presents new methodologies designed to facilitate language acquisition in open learning communities via open educational resources and knowledge mapping. It specifically focuses on the OpenLearn project developed by the Open University. This offers a virtual learning environment based on Moodle platform with free educational materials and knowledge media tools such as the instant messaging MSG, the video webconference FlashMeeting and the knowledge mapping software tool Compendium. In this work, these technologies and mapping techniques are introduced in order to promote open language learning. Ways in which teachers and students can make use of these OpenLearn tools and resources are discussed and some benefits fully described

Improving tag recommendation using social networks

In this paper we address the task of recommending additional tags to partially annotated media objects, in our case images. We propose an extendable framework that can recommend tags using a combination of different personalised and collective contexts. We combine information from four contexts: (1) all the photos in the system, (2) a user's own photos, (3) the photos of a user's social contacts, and (4) the photos posted in the groups of which a user is a member. Variants of methods (1) and (2) have been proposed in previous work, but the use of (3) and (4) is novel. For each of the contexts we use the same probabilistic model and Borda Count based aggregation approach to generate recommendations from different contexts into a unified ranking of recommended tags. We evaluate our system using a large set of real-world data from Flickr. We show that by using personalised contexts we can significantly improve tag recommendation compared to using collective knowledge alone. We also analyse our experimental results to explore the capabilities of our system with respect to a user's social behaviour

Visual mapping approaches for considering the strategic rationale for the implementation of OER in higher education institutions

Open educational resources (OER) have become a significant part of the general discourse around higher education and a number of institutions and governments have implemented initiatives to develop and use OER on the basis that they will help transform educational practice within and between higher educational institutions (HEIs). Nevertheless there has also been considerable comment and concern by many involved in higher education that OER are not sustainable financially and unlikely to be truly transformative of policy and practices in higher education. This paper reviews the existing published evidence and argues that all institutions need to properly consider whether and how OER fit in to their strategic plans and that this can usefully be done through the help of visual methods. Visual methods such as paper or computer based mapping techniques enable users to capture as much information as possible through a mediated conversation around the holistic representation of their collective views. This need for undertaking strategic reviews is mainly illustrated through the work of the EADTU led Multilingual Open Resources for Independent Learning (MORIL) project where workshop participants from HEIs used Kurt Lewin’s Force Field Framework to examine both intra institutional and inter institutional factors that were driving or restraining them in the implementation of OER. A major outcome of this work is that OER are another valued factor in the evolution of higher education policy and practice and that progress will be evolutionary rather than revolutionary