1. Informatiseringscentrum Patterns in Usage Data Victor Maijer University of Amsterdam 2 June 2006, Vancouver

2. Informatiseringscentrum Overview -Introduction -Data Mining -Results -Sakai & DM -Conclusion

3. Informatiseringscentrum Introduction UvA founded in 1632 (Atheneum Illustre) 7 schools (faculty), 1518 study programmes 25.000 students, 3500 employees (2000 academic staff) Blackboard is our VLE since 1999, 13.000 users per day We run OSP and regard Sakai as a potential succesor of Blackbaord

4. Informatiseringscentrum Strategic Information  Stakeholders need strategic information in order to make decisions Stakeholders are:  Instructors  Administrators  Management  Support  Etc.

5. Informatiseringscentrum Data Warehouse  Provides an integrated and total view of learning/collaboration systems  Makes the systems current and historical information easily available for decision making  Makes decision-support transactions possible without hindering operational systems  Presents a flexible and interactive source of strategic information

6. Informatiseringscentrum Architecture

7. Informatiseringscentrum Info for Administrators & Management

8. Informatiseringscentrum Why I went mining I had data, a lot I did it before I wanted to do some fun stuff Official reason (the one I tell my boss): We needed strategic information about how our VLE evolved

9. Informatiseringscentrum What is Data Mining? Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Clustering is a data mining technique that applies when instances are to be divided into natural groups.

10. Informatiseringscentrum Example CourseDocuments ABBA36 BEATLES4 COLDPLAY30 DARKHORSES2 ELASTICA24 GroupMembersAverage Docs AABBA, COLDPLAY, ELASTICA 30 BBEATLES, DARKHORSES 3

11. Informatiseringscentrum Procedure Determine mining questions Determine source (tables) Verify by changing items via GUI Identify needed output formats for analysis Define SQL-queries Program scripts (Perl) Determine which clustering techniques you want to apply Analyze (cluster). ‘Weka’ is an excellent JAVA OS tool for Data Mining http://www.cs.waikato.ac.nz/ml/weka/

12. Informatiseringscentrum Domains clustered CourseSites and its content Users (instructors) Sessions (student)

13. Informatiseringscentrum Site clusters  Basic usage (content + announcements)  Extended usage ClusterSize(%)N A871547 B7122 C466 D243  Basic usage (content + announcements)  Extended usage

14. Informatiseringscentrum Content clusters ClusterSize(%)N A911636 B362 C357 D345

15. Informatiseringscentrum Instructor activity clusters ClusterSize(%)N A881443 B7115 C461 D115

16. Informatiseringscentrum Student session clusters ClusterSize(%)N A911294K B690K C232K

17. Informatiseringscentrum Extra Female students click significant more than male students and have significant longer sessions Any ideas?

18. Informatiseringscentrum Sakai & Data mining Our UvA Pilots were too small to analyze Content can be clustered Events are difficult to cluster (not enough logging compared to Blackboard

19. Informatiseringscentrum Implications Put rumours into perspective Differentiate to user groups –Support –Functionality

20. Informatiseringscentrum Conclusion Methods –Clustering can be used to discover usage patterns –You need appropiate hardware for preprocessing and clustering Results –Basic Usage (Documents & Announcements) –Duration of a session is a couple of minutes –Extended Usage grows but is limited Sakai needs more logging if it wants to compete with Blackboard A Sakai warehouse would be nice

21. Informatiseringscentrum Evolvement Users Usage 0