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. Yanyan LI, School of Educational Technology, R&D Center for Knowledge Engineering, Beijing Normal University

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Analyzing Transcripts of Online Asynchronous Discussion Groups with VINCA Yanyan Li Knowledge Science & Engineering Institute, School of Education Technology, Beijing Normal University liyy1114@gmail.com

Outline Existing tools VINCA introduction Further development

Existing Tools Annotation aids ,[object Object]

Atlas-ti (http://www.atlasti.de). Dictionary-based content analysis ,[object Object]

LIWC (http://www.erlbaum.com)Development environments ,[object Object]

DIMAP (http://www.clres.com),[object Object]

Our goal Can we design and implement an integrated tool to assist identifying features of interaction and provide feedbacks to facilitate the collaboration in CSCL?

Implementing a Tool to Support Discussion Transcripts Analysis

Multidimensional Analysis How do the students talking with others? What are the students talking about? Who are talking to whom? Speech Topics Process Pattern Member Relationship Interaction Discourse Keywords in context Statistics for Social Network Analysis Semi-auto Coding

Vinca ‘s Features Learnable semi-automatic coding support Analyze text in Chinese (traditional and simplified) Utilize computational language & text mining technologies Support assessing for CKB Underlying technologies ,[object Object]

Association rules mining,[object Object]

2) Annotation Aids Edit Coding Scheme New, Modify, Delete Associate feature keywords to specific codes Annotation Support segment & merge *Automatic discover the code hint, highlight it and attach possible codes with confidence probability. During the process of coding, users are allowed to select the hint to mark the final coding. View Coding Result Suggested codes

3) Text Analysis Keywords retrieval & frequency counting Concordance *Discourse pattern discovery *Category analysis Similarity Computation *Text clustering Ontology-based assessing for CKB

Word Frequency (Keywords Retrieval) Select Data From KF From Speech File From Single Text File Exclusive List Special Common Tag Filter Picked List Find Keywords in Word List Export Keywords & User Keywords Distribution Import user’s lexicon Select exclusive list Select the words with specified tags

14 Concordance List of extracted keywords Concordance

Discourse Pattern Discovery Pattern configuration interface Frequent pattern mining results

Category Analysis Open Category File Search Para Synonyms in Interaction Text Based on Similarity Computation Category List Lexicon specified by tutors and auto-found synonyms

Text Clustering Select Data.. Get Keywords.. Set Option Export Result of Text Clustering

19 Assessing for CKB Group Performance Domain ontology Member Contribution Topic similarity between members

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

1. Analyzing Transcripts of Online Asynchronous Discussion Groups with VINCA Yanyan Li Knowledge Science & Engineering Institute, School of Education Technology, Beijing Normal University liyy1114@gmail.com

2. Outline Existing tools VINCA introduction Further development

7. Our goal Can we design and implement an integrated tool to assist identifying features of interaction and provide feedbacks to facilitate the collaboration in CSCL?

8. Implementing a Tool to Support Discussion Transcripts Analysis

9. Multidimensional Analysis How do the students talking with others? What are the students talking about? Who are talking to whom? Speech Topics Process Pattern Member Relationship Interaction Discourse Keywords in context Statistics for Social Network Analysis Semi-auto Coding

10. Tool Design Framework

11.

12. Domain ontology

13.

14. 2) Annotation Aids Edit Coding Scheme New, Modify, Delete Associate feature keywords to specific codes Annotation Support segment & merge *Automatic discover the code hint, highlight it and attach possible codes with confidence probability. During the process of coding, users are allowed to select the hint to mark the final coding. View Coding Result Suggested codes

15. 3) Text Analysis Keywords retrieval & frequency counting Concordance *Discourse pattern discovery *Category analysis Similarity Computation *Text clustering Ontology-based assessing for CKB

16. Word Frequency (Keywords Retrieval) Select Data From KF From Speech File From Single Text File Exclusive List Special Common Tag Filter Picked List Find Keywords in Word List Export Keywords & User Keywords Distribution Import user’s lexicon Select exclusive list Select the words with specified tags

17. 14 Concordance List of extracted keywords Concordance

18. Check the original text

19. Discourse Pattern Discovery Pattern configuration interface Frequent pattern mining results

20. Category Analysis Open Category File Search Para Synonyms in Interaction Text Based on Similarity Computation Category List Lexicon specified by tutors and auto-found synonyms

21. Text Clustering Select Data.. Get Keywords.. Set Option Export Result of Text Clustering

22. 19 Assessing for CKB Group Performance Domain ontology Member Contribution Topic similarity between members

23. 4) Data Export for SNA Export KF Data Export Relation Matrix Export Coding Result Export Coding Matrix Export Coding Frequency

24. Annotation aids Semi-automatic coding with enhanced precision Text analysis Category analysis for topic recognition Discourse Pattern discovery Visualization Topic space Social network Further Development

25. Thanks! Welcome Questions and Comments

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

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