Transcript What is Data Mining?

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Patterns in Usage Data
Victor Maijer
University of Amsterdam
2 June 2006, Vancouver
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Overview
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Introduction
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Data Mining
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Results
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Sakai & DM
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Conclusion
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Introduction
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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
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Strategic Information
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Stakeholders need strategic information in order to make
decisions
Stakeholders are:
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Instructors
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Administrators
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Management
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Support
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Etc.
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Data Warehouse
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Provides an integrated and total view of
learning/collaboration systems
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Makes the systems current and historical information
easily available for decision making
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Makes decision-support transactions possible without
hindering operational systems
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Presents a flexible and interactive source of strategic
information
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Architecture
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Info for Administrators & Management
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Why I went mining
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I had data, a lot
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I did it before
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I wanted to do some fun stuff
Official reason (the one I tell my boss):
• We needed strategic information about how our VLE
evolved
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What is Data Mining?
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Data mining is the extraction of implicit, previously
unknown, and potentially useful information from data.
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Clustering is a data mining technique that applies when
instances are to be divided into natural groups.
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Example
Course
Documents
ABBA
36
BEATLES
4
COLDPLAY
30
DARKHORSES
2
ELASTICA
24
Group
Members
Average Docs
A
ABBA,
COLDPLAY,
ELASTICA
30
B
BEATLES,
DARKHORSES
3
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Procedure
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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/
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Domains clustered
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CourseSites and its content
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Users (instructors)
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Sessions (student)
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Site clusters
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Basic usage (content + announcements)
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Extended usage
Cluster
Size(%)
N
A
87
1547
B
7
122
C
4
66
D
2
43
20
140
120
15
100
DiscussionFora
80
Content
Announcement
60
Gradebook
Tests
10
Groups
40
5
20
0
Cluster A
Cluster B
Cluster C
Cluster D
0
Cluster A
Cluster B
Cluster C
Cluster D
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Content clusters
100
80
Test
60
Asignment
Document
External Link
40
Folder
20
0
Cluster A
Cluster B
Cluster C
Cluster D
Cluster
Size(%)
N
A
91
1636
B
3
62
C
3
57
D
3
45
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Instructor activity clusters
600
500
400
Announcements
Content
300
Dropbox
DiscussionBoard
200
Gradebook
Test
100
0
Cluster A
Cluster B
Cluster C
Cluster D
Cluster
Size(%)
N
A
88
1443
B
7
115
C
4
61
D
1
15
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Student session clusters
180
160
140
120
100
80
60
40
20
0
171,45
Clicks
Dur(min)
63,4
25,5
29,7
Cluster B
Cluster C
8,7 3,8
Cluster A
Cluster
Size(%)
N
A
91
1294K
B
6
90K
C
2
32K
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Extra
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Female students click significant more than male
students and have significant longer sessions
Any ideas?
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Sakai & Data mining
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Our UvA Pilots were too small to analyze
Content can be clustered
Events are difficult to cluster (not enough logging
compared to Blackboard
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Implications
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Put rumours into perspective
Differentiate to user groups
– Support
– Functionality
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Conclusion
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Methods
– Clustering can be used to discover usage patterns
– You need appropiate hardware for preprocessing and
clustering
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Results
– Basic Usage (Documents & Announcements)
– Duration of a session is a couple of minutes
– Extended Usage grows but is limited
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Sakai needs more logging if it wants to compete with
Blackboard
A Sakai warehouse would be nice
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Evolvement
Users
Usage
0