Acting with Technology: Activity Theory and Interaction Design

Although considerable research has concentrated on online campaigning, it is still unclear how politicians use different social media platforms in political communication. Focusing on the German fe...

https://www.tandfonline.com/doi/pdf/10.1080/10584609.2017.1334728

Election Campaigning on Social Media: Politicians, Audiences, and the Mediation of Political Communication on Facebook and Twitter

Within the (Computer-Supported) Collaborative Learning (CS)CL research community, there has been an extensive dialogue on theories and perspectives on learning from collaboration, approaches to scaffold (script) the collaborative process, and most recently research methodology. In contrast, the issue of assessment of collaborative learning has received much less attention. This article discusses how assessment of collaborative learning has been addressed, provides a perspective on what could be assessed, and highlights limitations of current approaches. Since assessment of collaborative learning is a demanding experience for teachers and students alike, they require adequate computer-supported and intelligent tools for monitoring and assessment. A roadmap for the role and application of intelligent tools for assessment of (CS)CL is presented.

https://epub.ub.uni-muenchen.de/12945/2/Assessment_of_computer_supported.pdf

Assessment of (Computer-Supported) Collaborative Learning

This paper describes an application of learning analytics that builds on an existing research program investigating how students contribute and attend to the messages of others in asynchronous online discussions. We first overview the E-Listening research program and then explain how this work was translated into analytics that students and instructors could use to reflect on their discussion participation. Two kinds of analytics were designed: some e mbedded in the learning environment to provide students with real-time information on their activity in-progress; and some extracted from the learning environment and presented to students in a separate digital space for reflection. In addition, we describe the design of an intervention though which use of the analytics can be introduced as an integral course activity. Findings from an initial implementation of the application indicated that the learning analytics intervention supported changes in students’ discussion participation. Five issues for future work on learning analytics in online discussions are presented. One, unintentional versus purposeful change; two, differing changes prompted by the same analytic; three, importance of theoretical buy-in and calculation transparency for perceived analytic value; four, affective components of students’ reactions; and five, support for students in the process of enacting analytics-driven changes.

/pdf/learning-analytics-for-online-discussions-embedded-and-2vfw1o6d1k.pdf

Learning Analytics for Online Discussions: Embedded and Extracted Approaches

The science of social network analysis has co-evolved with the development of online environments and computer-mediated communication. Unique and precise data available from computer and information systems have allowed network scientists to explore novel social phenomena and develop new methods. Additionally, advances in the structural analysis and visualization of computer-mediated social networks have informed developers and shaped the design of social media tools. This article reviews some examples of research that highlight the ways that social network analysis has evolved with online data. Examples include the international hyperlink network, political blogs and hyperlinks, social media, and multi-user virtual environments. The data available from online environments makes several important contributions to network science, including reliable network flow data, unique forms of relational data across a myriad of contexts, and dynamic data allowing for longitudinal analysis and the animation of social networks.

/pdf/social-networks-and-online-environments-when-science-and-md6q7yhoem.pdf

Social networks and online environments: when science and practice co-evolve

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.

/pdf/a-framework-for-conceptualizing-representing-and-analyzing-2xwplvn045.pdf

A framework for conceptualizing, representing, and analyzing distributed interaction

As more and more people use social media to communicate their view and perception of elections,
researchers have increasingly been collecting and analyzing data from social media platforms.
Our research focuses on social media communication related to the 2013 election of the
German parliament [translation: Bundestagswahl 2013]. We constructed several social media
datasets using data from Facebook and Twitter. First, we identified the most relevant candidates
(n=2,346) and checked whether they maintained social media accounts. The Facebook data was
collected in November 2013 for the period of January 2009 to October 2013. On Facebook we
identified 1,408 Facebook walls containing approximately 469,000 posts. Twitter data was collected
between June and December 2013 finishing with the constitution of the government. On
Twitter we identified 1,009 candidates and 76 other agents, for example, journalists. We estimated
the number of relevant tweets to exceed eight million for the period from July 27 to September
27 alone. In this document we summarize past research in the literature, discuss possibilities
for research with our data set, explain the data collection procedures, and provide a description
of the data and a discussion of issues for archiving and dissemination of social media data.

/pdf/social-media-monitoring-of-the-campaigns-for-the-2013-german-2np3vnymea.pdf

Ravikiran Vatrapu

Papers

A framework for conceptualizing, representing, and analyzing distributed interaction

Social Media Monitoring of the Campaigns for the 2013 German Bundestag Elections on Facebook and Twitter