The Dynamics of Cyberspace: Examining and Modelling Online Social Structure

It has been proposed that online social structures represent new forms of organizing which are fundamentally different from traditional social structures. However, while there is a growing body of empirical research that considers behavioral aspects of online activity, research on online social structure structural remains largely anecdotal. This work consists of three papers that combine previous studies of traditional social structures, empirical analysis of longitudinal data from a sample of Internet listservs, and computational modeling to examine the dynamics of social structure development in networked environments. The first paper (Title: When is a Group not a Group: An Empirical Examination of Metaphors for Online Social Structure) empirically examines the appropriateness of metaphors which have been used in popular and academic discussions of online social structure. The structural features implied by the metaphors are compared with data from a random sample of e-mail based Internet listservs. The results indicate that the most commonly applied metaphor (\u27small group\u27) does not accurately represent the membership and communication features observed in these online social structures. Furthermore, there is evidence that the characterization of online structures in these terms has significantly biased the selection of cases and stories in the current literature. The empirical results also suggest that the metaphor of\u27 voluntary associations\u27 is more accurate and hence is better foundation for theorizing about online social structure. In the second paper (Title: Membership Size, Communication Activity, and Sustainability: The Internal Dynamics of Networked Social Structures) presents a resource-based theory of social structures. This model implies that structural features, such as size and communication activity, play both positive and negative roles in the sustainability of a social structure. Prior work has argued that networked communication technologies will significantly reduce the negative impact of size and communication activity, resulting in fundamentally different social structures. However, analysis of the longitudinal data from the e-mail based Internet listservs indicates that size and communication activity continue to have both positive and negative effects. This suggests that while the use of networked communication technologies may alter the form of communication, balancing the positive and negative impacts of membership size and communication activity remains a fundamental problem underlying the development of sustainable social structures. The third paper (Title: Communication Cost, Attitude Change and Membership Maintenance: A Model of Technology and Social Structure Development) integrates processes of individual belief change and member movement in a dynamic model of online social structure development. Contributed messages create a composite signal, providing members with information about the benefits of membership. This information changes members\u27 beliefs about the structure and affects their willingness to remain members. The processes of communication, individual belief change, and membership maintenance form a cycle that underlies the development of the collective. Communication costs, a feature of the communication infrastructure, affect a social structure\u27s development by moderating the process of member belief change. A dynamic, multi-agent computational model of social structure development was implemented, calibrated, and validated using the listserv data. Analysis of the model implies that reduced communication costs, as are expected in networked environments, slow down the development process, resulting in online social structures which have more (and more diverse) members while being less stable than traditional face-to-face associations

Dialogue as Data in Learning Analytics for Productive Educational Dialogue

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers

Collaborative Filtering via Group-Structured Dictionary Learning

Structured sparse coding and the related structured dictionary learning problems are novel research areas in machine learning. In this paper we present a new application of structured dictionary learning for collaborative filtering based recommender systems. Our extensive numerical experiments demonstrate that the presented technique outperforms its state-of-the-art competitors and has several advantages over approaches that do not put structured constraints on the dictionary elements.Comment: A compressed version of the paper has been accepted for publication at the 10th International Conference on Latent Variable Analysis and Source Separation (LVA/ICA 2012