4,806 research outputs found

    Computer Supported Content Analysis: Challenges, research and developments

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    CSCL has become a pedagogy of choice for many who believe that collaborative inquiry based learning is more effective in nurturing the kind of abilities needed for knowledge work in the 21st century. However, CSCL may not necessarily lead to effective learning. In the past decade or so, the volume of publications that provide data-driven insight for identifying patterns of cognitive engagement and facilitation based on CSCL discourse analysis has been far lower than the volume of CSCL discourse accumulated. A major part of the challenge in CSCL research is the difficulties in conducting systematic content analysis of the discourse data, which is crucial for understanding students’ learning progress. In this seminar, we will introduce some strategies for content and interaction analysis of CSCL discourse, the tools that the research team has built and the preliminary findings from applying the tools to the analysis of two sets of CSCL discourse as an illustration of how data mining can contribute to the assessment of knowledge building outcomes.This is a seminar organized to report on the research outcomes of work conducted under the HKU Strategic Research Theme on Information Technology, within the area of Applying Data Mining Techniques to Novel Applications. This seminar presents the work in progress by a collaborative team comprising researchers from the Centre for Knowledge Science & Engineering Research, Beijing Normal University (CKSER) at Beijing Normal University and the Centre for Information Technology in Education (CITE) at the University of Hong Kong. Their research have centred on using content and interaction analysis to identify patterns of cognitive engagement and facilitation in computer supported collaborative learning (CSCL) contexts and the contribution of data-mining to building models of students’ developmental trajectory in knowledge building.VINCA stands for Visual INtelligent Content Analyzer, which is the content analysis tool jointly developed by CITE, HKU and CKSER, BNU. Currently, it includes the following functions: Data preparation to convert Knowledge Forum® discourse in html to database format, Keywords retrieval, Manual coding support, Linguistic database and tools for continuously improvable support to domain ontology, mapping of keywords, Learnable semi-automatic semantic coding, Content analysis augmented social network analysis, Novelty and similarity analysis, Influence degree analysis (semantic) of specified note(s) or person(s)published_or_final_versionCentre for Information Technology in Education, University of Hong Kon

    Indicators for advances in knowledge building - Application of content analysis tools to two sets of CSCL discourse data from two comparable classes

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    Authored by Professor Nancy Law & the Learning Community Project TeamThis is a seminar organized to report on the research outcomes of work conducted under the HKU Strategic Research Theme on Information Technology, within the area of Applying Data Mining Techniques to Novel Applications. This seminar presents the work in progress by a collaborative team comprising researchers from the Centre for Knowledge Science & Engineering Research, Beijing Normal University (CKSER) at Beijing Normal University and the Centre for Information Technology in Education (CITE) at the University of Hong Kong. Their research have centred on using content and interaction analysis to identify patterns of cognitive engagement and facilitation in computer supported collaborative learning (CSCL) contexts and the contribution of data-mining to building models of students’ developmental trajectory in knowledge building.CSCL has become a pedagogy of choice for many who believe that collaborative inquiry based learning is more effective in nurturing the kind of abilities needed for knowledge work in the 21st century. However, CSCL may not necessarily lead to effective learning. In the past decade or so, the volume of publications that provide data-driven insight for identifying patterns of cognitive engagement and facilitation based on CSCL discourse analysis has been far lower than the volume of CSCL discourse accumulated. A major part of the challenge in CSCL research is the difficulties in conducting systematic content analysis of the discourse data, which is crucial for understanding students’ learning progress. In this seminar, we will introduce some strategies for content and interaction analysis of CSCL discourse, the tools that the research team has built and the preliminary findings from applying the tools to the analysis of two sets of CSCL discourse as an illustration of how data mining can contribute to the assessment of knowledge building outcomes.VINCA stands for Visual INtelligent Content Analyzer, which is the content analysis tool jointly developed by CITE, HKU and CKSER, BNU. Currently, it includes the following functions: Data preparation to convert Knowledge Forum® discourse in html to database format, Keywords retrieval, Manual coding support, Linguistic database and tools for continuously improvable support to domain ontology, mapping of keywords, Learnable semi-automatic semantic coding, Content analysis augmented social network analysis, Novelty and similarity analysis, Influence degree analysis (semantic) of specified note(s) or person(s)published_or_final_versionCentre for Information Technology in Education, University of Hong Kon

    Emerging and scripted roles in computer-supported collaborative learning

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    Emerging and scripted roles pose an intriguing approach to analysing and facilitating CSCL. The concept of emerging roles provides a perspective on how learners structure and self-regulate their CSCL processes. Emerging roles appear to be dynamic over longer periods of time in relation to learners’ advancing knowledge, but are often unequally distributed in ad hoc CSCL settings, e.g. a learner being the ‘typist’ and another being the ‘thinker’. Empirical findings show that learners benefit from structuring or scripting CSCL. Scripts can specify roles and facilitate role rotation for learners to equally engage in relevant learning roles and activities. Scripted roles can, however, collide with emerging roles and therefore need to be carefully attuned to the advancing capabilities of the learners

    A framework to analyze argumentative knowledge construction in computer-supported collaborative learning

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    Computer-supported collaborative learning (CSCL) is often based on written argumentative discourse of learners, who discuss their perspectives on a problem with the goal to acquire knowledge. Lately, CSCL research focuses on the facilitation of specific processes of argumentative knowledge construction, e.g., with computer-supported collaboration scripts. In order to refine process-oriented instructional support, such as scripts, we need to measure the influence of scripts on specific processes of argumentative knowledge construction. In this article, we propose a multi-dimensional approach to analyze argumentative knowledge construction in CSCL from sampling and segmentation of the discourse corpora to the analysis of four process dimensions (participation, epistemic, argumentative, social mode)

    Collaborative trails in e-learning environments

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    This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

    Analyzing collaborative learning processes automatically

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    In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

    From multiple perspectives to shared understanding

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    The aim of this study was to explore how learners operating in a small group reach shared understanding as they work out joint research questions and build a theoretical framework and to identify the resources and tools they used in the process. The learners’ own interpretations of their group activities and learning were also taken into account. The data, consisting of group discussions and the documents produced by the group, were subjected to a qualitative content analysis. The group members employed a variety of resources and tools to exchange their individual perspectives and achieve shared understanding. Summaries of relevant literature laid a foundation for the group’s theoretical discussions. Reflective comparisons between their book knowledge and their personal experiences of online interaction and collaboration were frequent, suggesting that such juxtapositions may have enhanced their learning by intertwining the content to be mastered and the activities entailed by this particular content
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