On Online Collaboration and Construction of Shared Knowledge: Assessing Mediation Capability in Computer Supported Argument Visualization Tools

Collaborative Computer-Supported Argument Visualization (CCSAV) has often been proposed as an alternative over more conventional, mainstream platforms for online discussion (e.g., online forums and wikis). CCSAV tools require users to contribute to the creation of a joint artifact (argument map) instead of contributing to a conversation. In this paper we assess empirically the effects of this fundamental design choice and show that the absence of conversational affordances and socially salient information in representation-centric tools is detrimental to the users' collaboration experience. We report empirical findings from a study in which subjects using different collaborative platforms (a forum, an argumentation platform, and a socially augmented argumentation tool) were asked to discuss and predict the price of a commodity. By comparing users' experience across several metrics we found evidence that the collaborative performance decreases gradually when we remove conversational interaction and other types of socially salient information. We interpret these findings through theories developed in conversational analysis (common ground theory) and communities of practice and discuss design implications. In particular, we propose balancing the trade-off between knowledge reification and participation in representation-centric tools with the provision of social feedback and functionalities supporting meaning negotiation

Epistemic and social scripts in computer-supported collaborative learning

Collaborative learning in computer-supported learning environments typically means that learners work on tasks together, discussing their individual perspectives via text-based media or videoconferencing, and consequently acquire knowledge. Collaborative learning, however, is often sub-optimal with respect to how learners work on the concepts that are supposed to be learned and how learners interact with each other. One possibility to improve collaborative learning environments is to conceptualize epistemic scripts, which specify how learners work on a given task, and social scripts, which structure how learners interact with each other. In this contribution, two studies will be reported that investigated the effects of epistemic and social scripts in a text-based computer-supported learning environment and in a videoconferencing learning environment in order to foster the individual acquisition of knowledge. In each study the factors ‘epistemic script’ and ‘social script’ have been independently varied in a 2×2-factorial design. 182 university students of Educational Science participated in these two studies. Results of both studies show that social scripts can be substantially beneficial with respect to the individual acquisition of knowledge, whereas epistemic scripts apparently do not to lead to the expected effects

Learning analytics as a "middle space"

Learning Analytics, an emerging field concerned with analyzing the vast data "given off" by learners in technology supported settings to inform educational theory and practice, has from its inception taken a multidisciplinary approach that integrates studies of learning with technological capabilities. In this introduction to the Proceedings of the Third International Learning Analytics & Knowledge Conference, we discuss how Learning Analytics must function in the "middle space" where learning and analytic concerns meet. Dialogue in this middle space involves diverse stakeholders from multiple disciplines with various conceptions of the agency and nature of learning. We hold that a singularly unified field is not possible nor even desirable if we are to leverage the potential of this diversity, but progress is possible if we support "productive multivocality" between the diverse voices involved, facilitated by appropriate use of boundary objects. We summarize the submitted papers and contents of these Proceedings to characterize the voices and topics involved in the multivocal discourse of Learning Analytics

LAK 2013 : third international conference on learning analytics and knowledge : Leuven, Belgium, April 08-12, 2013

Welcome to the third edition of the Learning Analytics and Knowledge conference. This year, the medieval and, at the same time, modern city of Leuven, Belgium is the venue where researchers and practitioners of this exciting field come together to discuss current status and future trends. Similar to Leuven, Learning Analytics is an old and new field at the same time. Old, because it deals with a problem that exists since Plato's times: how to improve the way students learn. New, because the tools used to achieve this goal, like Big Data and natural language processing, were not feasible merely 10 years ago. Leuven is also the home of beautiful centuries old buildings filled with young, smart and active students. In Learning Analytics, we can also find established researchers in the fields of Educational Research and Technology-Enhanced Learning, collaborating with a large contingent of new and promising researchers that could be called Learning Data Scientists

Interrelation between Pedagogical Design and Learning Interaction Patterns in different Virtual Learning Environments

Different virtual learning environments offer different affordances and pedagogical design for learning interactions which results in difference learning interaction patterns. With the emergence of a new era in VLE (virtual learning environments) a new set of affordances is needed to support the appropriate learning interactions.We argue that there is a strong interrelation between the pedagogical design and learning interaction patterns in a given VLE which is influenced by the affordances of that VLE. In order to create a set of affordances that support learning interactions within the DLE, there is a need of analysis of already existing learning interaction affordances across different platforms. \ua9 2014 Springer International Publishing