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Presentation: Computer Supported Content Analysis: Challenges, research and developments

TitleComputer Supported Content Analysis: Challenges, research and developments
Authors
Issue Date2006
Citation
Public Seminar on Data Mining in Education - Content & Interaction Analysis of CSCL Discourse Data for Assessing Knowledge Building Outcomes, Hong Kong, China, 27 October 2006 How to Cite?
DescriptionThis 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)
Persistent Identifierhttp://hdl.handle.net/10722/43985

 

DC FieldValueLanguage
dc.contributor.authorHuang, R-
dc.contributor.authorLi, Y-
dc.date.accessioned2007-05-11T03:23:41Z-
dc.date.available2007-05-11T03:23:41Z-
dc.date.issued2006-
dc.identifier.citationPublic Seminar on Data Mining in Education - Content & Interaction Analysis of CSCL Discourse Data for Assessing Knowledge Building Outcomes, Hong Kong, China, 27 October 2006en
dc.identifier.urihttp://hdl.handle.net/10722/43985-
dc.descriptionThis 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.en
dc.descriptionCSCL 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.-
dc.descriptionVINCA 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)-
dc.description.sponsorshipCentre for Information Technology in Education, University of Hong Kongen
dc.format.extent621385 bytes-
dc.format.mimetypeapplication/pdf-
dc.languageeng-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleComputer Supported Content Analysis: Challenges, research and developmentsen
dc.typePresentationen
dc.description.naturepublished_or_final_versionen_HK

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