904 research outputs found

    Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction Data

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    © 2019, Copyright © 2017 Taylor & Francis Group, LLC. Learning to collaborate effectively requires practice, awareness of group dynamics, and reflection; often it benefits from coaching by an expert facilitator. However, in physical spaces it is not always easy to provide teams with evidence to support collaboration. Emerging technology provides a promising opportunity to make collocated collaboration visible by harnessing data about interactions and then mining and visualizing it. These collocated collaboration analytics can help researchers, designers, and users to understand the complexity of collaboration and to find ways they can support collaboration. This article introduces and motivates a set of principles for mining collocated collaboration data and draws attention to trade-offs that may need to be negotiated en route. We integrate Data Science principles and techniques with the advances in interactive surface devices and sensing technologies. We draw on a 7-year research program that has involved the analysis of six group situations in collocated settings with more than 500 users and a variety of surface technologies, tasks, grouping structures, and domains. The contribution of the article includes the key insights and themes that we have identified and summarized in a set of principles and dilemmas that can inform design of future collocated collaboration analytics innovations

    Analysing, visualising and supporting collaborative learning using interactive tabletops

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    The key contribution of this thesis is a novel approach to design, implement and evaluate the conceptual and technological infrastructure that captures student’s activity at interactive tabletops and analyses these data through Interaction Data Analytics techniques to provide support to teachers by enhancing their awareness of student’s collaboration. To achieve the above, this thesis presents a series of carefully designed user studies to understand how to capture, analyse and distil indicators of collaborative learning. We perform this in three steps: the exploration of the feasibility of the approach, the construction of a novel solution and the execution of the conceptual proposal, both under controlled conditions and in the wild. A total of eight datasets were analysed for the studies that are described in this thesis. This work pioneered in a number of areas including the application of data mining techniques to study collaboration at the tabletop, a plug-in solution to add user-identification to a regular tabletop using a depth sensor and the first multi-tabletop classroom used to run authentic collaborative activities associated with the curricula. In summary, while the mechanisms, interfaces and studies presented in this thesis were mostly explored in the context of interactive tabletops, the findings are likely to be relevant to other forms of groupware and learning scenarios that can be implemented in real classrooms. Through the mechanisms, the studies conducted and our conceptual framework this thesis provides an important research foundation for the ways in which interactive tabletops, along with data mining and visualisation techniques, can be used to provide support to improve teacher’s understanding about student’s collaboration and learning in small groups

    Proceedings of the First International Workshop on Mashup Personal Learning Environments

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    Wild, F., Kalz, M., & Palmér, M. (Eds.) (2008). Proceedings of the First International Workshop on Mashup Personal Learning Environments (MUPPLE08). September, 17, 2008, Maastricht, The Netherlands: CEUR Workshop Proceedings, ISSN 1613-0073. Available at http://ceur-ws.org/Vol-388.The work on this publication has been sponsored by the TENCompetence Integrated Project (funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org]) and partly sponsored by the LTfLL project (funded by the European Commission's 7th Framework Programme, priority ISCT. Contract 212578 [http://www.ltfll-project.org

    Towards a Time Series Approach for the Classification and Evaluation of Collaborative Activities

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    The analysis and evaluation of computer-supported collaborative activities is a complex and tedious task. However, it is necessary in order to support collaborative scenarios, to scaffold the collaborative knowledge building and to evaluate the learning outcome. Various automated techniques have been proposed to minimize the workload of human evaluators and speed up the process. In this study, we propose a memory based learning model for the analysis, classification and evaluation of collaborative activities that makes use of time series techniques along with logfile analysis. We argue that the classification of collaborative sessions, with respect to their time series attributes, may be related to their qualitative aspects. Based on this rationale, we explore the use of the model under various settings. The results of the model are compared to assessments made by expert evaluators using a rating scheme. Correlation and error analyses are further conducted

    Multimodal learning and teaching corpora exchange: Lessons learned in five years by the Mulce project

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    In order to make replication possible for interaction analysis in online learning, the French project named Mulce (2007-2010) and its team worked on requirements for research data to be shareable. We defined a learning and teaching corpus (LETEC) as a package containing the data issued from an online course, the contextual information and metadata, necessary to make these data visible, shareable and reusable. These human, technical and ethical requirements are presented in this paper. We briefly present the structure of a corpus and the repository we developed to share these corpora. Related works are also described and we show how conditions evolved between 2006 and 2011. This leads us to report on how the Mulce project was faced with four particular challenges and to suggest acceptable solutions for computer scientists and researchers in the humanities: both concerned by data sharing in the Technology Enhanced Learning community

    Enhancing Free-text Interactions in a Communication Skills Learning Environment

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    Learning environments frequently use gamification to enhance user interactions.Virtual characters with whom players engage in simulated conversations often employ prescripted dialogues; however, free user inputs enable deeper immersion and higher-order cognition. In our learning environment, experts developed a scripted scenario as a sequence of potential actions, and we explore possibilities for enhancing interactions by enabling users to type free inputs that are matched to the pre-scripted statements using Natural Language Processing techniques. In this paper, we introduce a clustering mechanism that provides recommendations for fine-tuning the pre-scripted answers in order to better match user inputs

    Interaction analysis of dual-interaction CSCL environments

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