CITERS Workshop<br />Organized by<br />Jingyan Lu (The University of Hong Kong), <br />Yanyan Li (Beijing Normal Universit...
Purpose of the workshop<br />Sharing<br />Communicating<br />Collaborating<br />
Schedule<br />Session one; Analysis of non-constrained CSCL data (80 Min)<br />Session two: Design of CSCL environments to...
Sequential Discourse Analysis of CSCL Data<br />Jingyan Lu<br />The University of Hong Kong<br />CITERS 2010<br />
Domain Background <br />Cognitive and social aspects of learning<br />Cognitive processes of learning are embedded in the ...
Method background<br />CSCL discourse data seldom analyzed statistically because:<br />Outcomes are discrete<br />Time ser...
Methods: Data Source<br />Secondary students online discussion on Knowledge Forum (KF)<br />40 students from one class<br ...
Issues<br />Whether discourse moves and participant structures can predict types of justification during online collaborat...
Discourse Moves<br />Cognitive dimension of argumentation<br />Social dimension of Collaborative argumentation<br />Partic...
Discourse moves<br /><ul><li>Discourse moves comprise the statements, questions, answers and requests constituting exchang...
Participants structure<br />Was defined as social relationships through which  students engage in classroom interaction (P...
Coding schema<br />Social dimension of argumentation<br />Discourse moves: claim, evaluation, questions, information<br />...
Analytical method<br />Multivariate<br />Multi-level logit<br />Autoregression<br />πiy= 1 / {1 + exp[–(0y + eiy+vyViy+...
Results<br />
Discussion<br />Theoretical implication: Connections between cognitive and social dimensions of collaborative learning<br ...
Challenges<br />Data sources<br />Coding<br />Interpretations<br />
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Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data

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5 March 2010 (Friday) | 09:00 - 12:30 | http://citers2010.cite.hku.hk/abstract/69 | Dr. Jingyan LU, Research Assistant Professor, Faculty of Education, HKU

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Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data

  1. 1. CITERS Workshop<br />Organized by<br />Jingyan Lu (The University of Hong Kong), <br />Yanyan Li (Beijing Normal University)<br />Jing Leng (The University of Hong Kong)<br />Multiple methods and techniques in analyzing CSCL data<br />
  2. 2. Purpose of the workshop<br />Sharing<br />Communicating<br />Collaborating<br />
  3. 3. Schedule<br />Session one; Analysis of non-constrained CSCL data (80 Min)<br />Session two: Design of CSCL environments to provide built-in CSCL pedagogical support and the analysis of data from such platforms <br />Session three: Visualization of CSCL data analysis<br />
  4. 4. Sequential Discourse Analysis of CSCL Data<br />Jingyan Lu<br />The University of Hong Kong<br />CITERS 2010<br />
  5. 5. Domain Background <br />Cognitive and social aspects of learning<br />Cognitive processes of learning are embedded in the socio-cultural activities through which students participate in mutually constituting social relationships (Rogoff, 1998). <br />Collaborative argumentation as a form of learning involves both cognitive (quality of argumentation) and social aspects (discourse moves and participant structures). <br />Methods required to understand the collaborative argumentation<br />Relations among different dimensions<br />Progress of discourse<br />
  6. 6. Method background<br />CSCL discourse data seldom analyzed statistically because:<br />Outcomes are discrete<br />Time series relations<br />Multiple outcomes<br />Is there a statistical analysis can overcome the above problems?<br />
  7. 7. Methods: Data Source<br />Secondary students online discussion on Knowledge Forum (KF)<br />40 students from one class<br />Two topics<br />136 notes<br />
  8. 8. Issues<br />Whether discourse moves and participant structures can predict types of justification during online collaborative argumentation<br />What types of methods can help us do so?<br />
  9. 9. Discourse Moves<br />Cognitive dimension of argumentation<br />Social dimension of Collaborative argumentation<br />Participant Structure<br />Model of the Study<br />
  10. 10. Discourse moves<br /><ul><li>Discourse moves comprise the statements, questions, answers and requests constituting exchanges among groups of learners (Tapper, 1996) who are in turn embedded in social networks (called participation structures) which emerge through their discourse moves.</li></ul>They are units of analysis used to investigate the effects of socio-cultural processes on the cognitive process of justification in collaborative argumentation<br />Discourse moves used by learners to justify arguments in early notes affect the discourse moves used by learners to justify arguments in later notes. <br />
  11. 11. Participants structure<br />Was defined as social relationships through which students engage in classroom interaction (Phillips, 1972)<br />SNA measures<br />Popularity (indegree and betweenness)<br />Gregariousness (outdegree)<br />
  12. 12. Coding schema<br />Social dimension of argumentation<br />Discourse moves: claim, evaluation, questions, information<br />Participant structure<br />Cognitive dimension of argumentation: Evidence vs. explanation<br />
  13. 13. Analytical method<br />Multivariate<br />Multi-level logit<br />Autoregression<br />πiy= 1 / {1 + exp[–(0y + eiy+vyViy+wyWiy+ xyXiy + zyZiy)]}<br />
  14. 14. Results<br />
  15. 15. Discussion<br />Theoretical implication: Connections between cognitive and social dimensions of collaborative learning<br />Methodological implication<br />Using SNA to characterize participant structure<br />Connect discourse analysis with statistical methods<br />
  16. 16. Challenges<br />Data sources<br />Coding<br />Interpretations<br />

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