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Discovering
Collaborative Patterns
in eLearning from
Meta-code Subsequence
Name: Yip Chi Kin
Date: 15-01-2005
Motivation
․Collaborative eLearning
․Machine Learning Assess
․Temporal Data Mining
․Generalization Usage
eLearning
․Learning Style & Model
․Collaborative activity
․Courseware
․Technology
․Assessment
Temporal Data
․Asynchronousness
․Irregularity
Synchronous
․Huge Volume
Dataset
․Streaming Data
․Distributed analysis
․Heterogeneous data types
Temporal Mining
Prediction
Threshold selection
Correlation
Frequency Analysis
Regression
Anomaly Detection
Benchmarking
Causality Analysis
Bioinformatics
Periodic Pattern Mining
approach
Sequential Event Patterns
Temporal Association Finding
Clustering and Classification
Tracking Techniques
․Full Screen HyperCard
․Click tracking of user
․Timeout timestamp
․Referrer timestamp
․Full-loaded timestamp
․Machine hanging
․Diminish / Enlarge Platform Window
․Delay capture session code
Temporal Timestamp
Event ID
22345
22346
User ID
101
66
Timestamp
20030620160000
20030620160008
From
S21
PLI
Referrer
t12
ipi
22347
101
20030620160010
T12
cmi
S21, t12, … are HTML page codes
ipi, cmi, … are communicative sessions
Temporal Grouping
ID
223
224
User ID
101
66
Timestamp
20030620160000
20030620160008
Group_id
21
35
225
101
20030620160010
42
Members could be join to another group
Anytime in the eLearning Platform
Data Cleaning
․Hacking Problem
․Graphics Learning
․Session Errors
․Double Clicks
Duration
Duration(Page)
= Starting Timestamp(Page)  Ending Timestamp(Page)
= St  Et = Dt = Duration of each page
․Fuzzy rule:
if Dt  1 second then Dt = 0
Browse weight
Bc = s ( c Dt )
s = Number of students, where 1  s  163
t = Timestamp
c = Pagecode
Frequency
Ps =  Frequency(Page) of each student
fc = s Ps = Total frequence of page
․Fuzzy rule:
if Dt  3 seconds then fc = fc + 1
where s = Number of students, where 1  s  163
t = Timestamp
c = Pagecode
Weighting
Navigation of Page
where
α c Bc
Xc 
fc
Bc is Duration of Page Code
fc is Frequency of Page Code
c is Linguistic variable of each Events
Navigation Pages
Statistics
Raw Browsing data
After Logarithm
Normalization
․Unique Interval
Normalization of Page Code ( 0  wn  1 )
log( xn )  min x
wn 
max x  min x
where
xn is source value and wn is weight value
maxx macima and minx minima for all data
Events Coding
A = Concepts
B = Individual
C = Collaboration
D = Technique
E = eLearning
X = Idle Time
Theory, Courseware
Video, Skill
Information Granules
․Linguistic variables
․Fuzzy Reasoning
․Interval Valued
․Super Subsequence
Meta-Code
Code about code
Fuzzy
Rules
Linguistic
variables
Events Code
Tri-event
pattern
Interval
Valued
Information
Granulation
MetaCode
Discretization
Frequency & Time
Temporal Reasoning
Temporal
Database
Fuzzy Rules
Fuzzy
Rules
Granular Computing
Timestamp
Time Partitioning
Linguistic
Variables
Event Weight
Code Sequence
C D C B B A A C
Contiguous Sequence
…
Collaborative Window
Mutual event window size
B B AAAAA XXXXX AAAA
Group
Size
D C AAA B B B B DDA AA C C
AA AA C XA AAAAD DDDD
DD AA B AA B B B AA A C C C
C B AAXXXAAA C C B B B B
Individual Personalization Profile
…
…
…
…
…
DDDAA AAA
AD B B B ADD
AAAAA AAA
C DDD B B B B
C C DDD C C C
Communication Window
Collaborative Link
Increasing weights from
collaborative links
Member #1
Member #2
Synchronous Communication
Sc = ncWc
Member #3
Member #4
where
n is Numbers of Links
W is weight of events
Member #5
Synchronous
Communication
Synchronous Weight
Syne =  Sc
where
e is events
c is member of group
Asynchronous
Communication
Wc

Communication A  W 
c
Asynchronous Weight
c
nc
Asyne =  Ac
where
W is weight of events
n is Numbers of Asynchronous Communications
Capture Windows
Study period subsequence
6480000 sec
Capture direction
Effective communications
100000 to 1400000 sec
Minimum weighting
&
Maximum weighting
One day period
86400 sec
Points of Weight =  (Asyne + Syne)
Minimum Windowing
20
Group 1
Group 2
Group 3
Group 4
18
16
14
12
10
8
6
4
2
0
100000
300000
500000
700000
900000
1100000
1300000
Maximum Windowing
20
Group 1
Group 2
Group 3
Group 4
18
16
14
12
10
8
6
4
2
0
100000
300000
500000
700000
900000
1100000
1300000
Result Applications
• Curriculum Planning
Coursework (#5) studying period should be
more than 6 days
• Collaborative Assessment
Range of Collaboration benchmark is 77920
to 176497 points of weight
• Effective Communication
Necessary communication period is 4 days
Project Enhancement
․Huge Volume Implementation
(Apply special algorithms)
․Rewrite C++ Programs
(Generalization Usage)
․Data Organization
(One day  Members = 86400  163)
․Visualization of Patterns
MetaCode Modeling
Time interval = 1 second , weblog duration = 75 days ,
Code length of Personalization Profile = n = 75246060 = 6480000
MetaCode SubSequence
n
AAB B BDCAAAAC C C CA … AAAB BDDD
ABDCACA … ABD
MetaCode Events SubSequence
Tri-event Pattern
Mutual relationships of tri-event
pattern in sub-sequence
․Comparison of good/bad tri-event patterns
․Frequent sequential pattern finding (tri-event)
․Longest common subsequence
․Super Subsequence
․Sequential events prediction
․Sequence reconstruction
․Viterbi algorithm
(hamming distance + Transformational grammar )
Super Subsequence
set of tri-event: {000, 001, 010, 011, 100, 101, 110, 111}
concatenation : 000 001 010 011 100 101 110 111
Super Subsequence
010
110
011
000
Super Subsequence
0001110100
001
111
101
100
Further Research
․Automata and Computability
․Implementing Algorithms
․Fuzzy Linguistic Associative Rules
․Fuzzy Reasoning Partitioning
․Subsequence Matching
․MetaCode Grammar
Conclusions
․Assess Collaboration
․Granular Modeling
․Generalization Usage