Ravikiran Vatrapu

A Framework for Conceptualizing, Representing, and Analyzing Distributed Interaction

The relationship between interaction and learning is a central concern of the learning sciences, and analysis of interaction has emerged as a major theme within the current literature on computer-supported collaborative learning. The nature of technology-mediated interaction poses analytic challenges. Interaction may be distributed across actors, space, and time, and vary from synchronous, quasi-synchronous, and asynchronous, even within one data set. Often multiple media are involved and the data comes in a variety of formats. As a consequence, there are multiple analytic artifacts to inspect and the interaction may not be apparent upon inspection, being distributed across these artifacts. To address these problems as they were encountered in several studies in our own laboratory, we developed a framework for conceptualizing and representing distributed interaction. The framework assumes an analytic concern with uncovering or characterizing the organization of interaction in sequential records of events. The framework includes a media independent characterization of the most fundamental unit of interaction, which we call uptake. Uptake is present when a participant takes aspects of prior events as having relevance for ongoing activity. Uptake can be refined into interactional relationships of argumentation, information sharing, transactivity, and so forth for specific analytic objectives. Faced with the myriad of ways in which uptake can manifest in practice, we represent data using graphs of relationships between events that capture the potential ways in which one act can be contingent upon another. These contingency graphs serve as abstract transcripts that document in one representation interaction that is distributed across multiple media. This paper summarizes the requirements that motivate the framework, and discusses the theoretical foundations on which it is based. It then presents the framework and its application in detail, with examples from our work to illustrate how we have used it to support both ideographic and nomothetic research, using qualitative and quantitative methods. The paper concludes with a discussion of the framework’s potential role in supporting dialogue between various analytic concerns and methods represented in CSCL.

Culture and computers: a review of the concept of culture and implications for intercultural collaborative online learning

Our research is aimed at a systematic investigation of phenomena in the nexus of culture, technology and learning. The basic premise of our research is that social affordances of technologies might vary along cultural dimensions. In this paper we present a brief overview of the concept of culture. We then discuss empirical findings demonstrating cultural effects on social behavior, communication and cognition and draw implications to online collaborative learning. In the last part of this paper, we present a selective review of research in cross-cultural human computer interaction.

An Abstract Transcript Notation for Analyzing Interactional Construction of Meaning in Online Learning

This work is based on the premise that the interactional construction of meaning is as important in online settings as it is face-to-face, especially in collaborative learning. Yet most studies of online learning use quantitative methods that assign meaning to contributions in isolation and aggregate over many sessions, obscuring the situated procedures by which participants accomplish learning through the affordances of online media. Methods for studying the interactional construction of meaning are available, but have largely been developed for brief episodes of face-to-face data, and need to be adapted to online learning where media resources, time scale, and synchronicity differ. In order to resolve this tradeoff, we have prototyped an abstract transcript notation to support sequential and interactional analysis of distributed and asynchronous interactions. The paper describes applications to data derived from asynchronous interaction of dyads and small groups

Re-framing HCI through local and indigenous perspectives

This one-day workshop aims to present different local and indigenous perspectives from all over the world in order to lead into an international dialogue on re-framing concepts and models in HCI/Interaction Design. The target audience is HCI researchers and practitioners who have experience with working with culture and HCI. The expected outcome of the workshop is a) network building among the participants, b) a shortlist of papers that can be basis for a proposal for a special issue of the UAIS journal, and c) identify opportunities to develop a funded network or research proposal.

Generating predictive models of learner community dynamics

In this paper we present a framework for learner modelling that combines latent semantic analysis and social network analysis of online discourse. The framework is supported by newly developed software, known as the Knowledge, Interaction, and Social Student Modelling Explorer (KISSME), that employs highly interactive visualizations of content-aware interactions among learners. Our goal is to develop, use and refine KISSME to generate and test predictive models of learner interactions to optimise learning.

Representational Effects in Asynchronous Collaboration: A Research Paradigm and Initial Analysis

Researchers have argued that tools for online learning should provide representational support for the conceptual structure of a problem area in order to address issues of coherence and convergence and more effectively support collaborative knowledge construction. The study described in this paper sets out to investigate the merits of knowledge representations and of two alternative ways they may be related to discussion tools: embedded or linked. Analyses conducted to date suggest intriguing process and outcome differences to be investigated in future analyses. The paper also offers a methodological contribution: a paradigm for practical experimental study of asynchronous collaboration. Prior research has focused on face-to-face and synchronous collaboration due to the pragmatic problems of conducting asynchronous studies. It is crucial to understand how to support collaborative knowledge construction in asynchronous settings prevalent in online learning.

Information foraging in E-voting

In this paper, we present a case study of human-information interaction in the online realm of politics. The case study consists of a participant observed while searching and browsing the internet for campaign information in a mock-voting situation while taking notes that were to be shared with others. Interaction analysis of the case study data consisted of applying Information Foraging Theory to understand participant specific behaviors in searching and browsing. Case study results show skewed time allocation to activities, a tradeoff between enrichment vs. exploitation of search results, and issues with lack of scent, low value perception, and value depletion of information. Potential implications for voter-centered design of e-voting portals are discussed and future work is outlined.

Integrating micro-level interactions with social network analysis in tie strength research: the edge-centered approach

A social tie is a target for ongoing, high-level scientific debate. Measuring the tie strength in social networks has been an important topic for academic studies since Mark Granovetters seminal papers in 1970s. However, it is still a problematic issue mainly for two reasons: 1) existing tie strength measurements may not reflect the true social connections of individuals accurately enough, and 2) many different methods to gather data from social media are not applicable anymore due to different data openness issues. In addition, we have only little empirical knowledge of the actual tie strengthening process in online social networks. Therefore, we suggest a new approach to tie strength research, which focuses on studying communication patterns (edges) more rather than actors (nodes) in a social network. In this paper we build a social network analysis-based approach to enable the evaluation of tie strength based on reciprocal interaction from publicly available Facebook data, and suggest that this approach could work as a basis for further tie strength studies. Our approach makes use of weak tie theory, and enables researchers to study micro-level interactions (i.e. discussions, messages, relationships) with large-scale social network analysis (SNA). This study provides a way to find relevant actors from publicly available data in the context of tie strengthening process, and answers how to take this stream of research closer to computational social science.