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依據群體模組監控之網路群體學習系統Group model monitor on network group learning system
國立中央大學資訊工程研究所
指導教授 : 陳國棟教授學生 : 區國良
2
Outline Introduction Related Researches System overview Approaches for group model monitor on
network group learning Constructing the Group learning feature space
Member-roles Communication network analysis
Communication relationships Analyze causal relationships between group status
and group performance Experiments and results Conclusion
3
Outline Introduction Related Researches System overview Approaches for group model monitor on
network group learning Constructing the Group learning feature space
Member-roles Communication network analysis
Communication relationships Analyze causal relationships between group status
and group performance Experiments and results Conclusion
4
Introduction Background and Motivation
Conventional group learning and web group learning Web based learning lost characteristics of peer
pressure and peer support web group learning Educational and social science researchers have
developed many theories on managing group process and group learning
Computation power can be used to track student’s learning behavior , online analysis and monitor web learning
5
Introduction Theory framework
The conditions mediating the relationship between cooperation and achievement (Johnson and Johnson, 1991)
OUTCOME
SOCIAL
SKILLS
PROMOTIVEINTERACTION
POSITIVEINTERDEPENDENCE
Web Group communication
relationships
Web Group communication
relationships
Web Group learning
behaviors
Web Group learning
behaviors
Goal and Reward interdependence
Goal and Reward interdependence
Task, role, and resource
interdependence
Task, role, and resource
interdependence
6
Introduction Data source In a web learning environment, all the learning activities
are acted on web server All group leaning behaviors are recorded in web logs All group interactions are recorded in web logs
We can use the logs to analyze and monitor the group learning by computation capabilities
Students Students
Group interactions
Learning behaviors
Learning behaviors
Learning performance
Web Server
7
Introduction Research Goals Extract the causal relationships between group status and
group performance based on theories in social science Constructing tools for teachers based on the relationship found
Monitor groups by leaning behaviors Monitor group by communication relationships
Students Students
Web Server
Group interactions
Learning behaviors
Learning behaviors
Learning performance
MonitorMonitor
MonitorMonitorMonitorMonitor
Extract the relationshipsExtract the relationships
8
Introduction Issues
To accomplish above research goals, three issues must be tackled: Transfer the data and information from the view of
data log schema to the view of a teacher Constructing feature spaces, rules, events from the
teachers’ point of view Find out the relationship between group feature
space and group performance Identify and define group feature space (communication
pattern, existence of roles) Causal relationship network
Build group model monitor based on the relationships
9
Outline Introduction Related Researches System overview Approaches for group model monitor on
network group learning Constructing the Group learning feature space
Member-roles Communication network analysis
Communication relationships Analyze causal relationships between group status
and group performance Experiments and results Conclusion
10
Related Research CSCL
Computer supported collaborative learning (CSCL) Computer support intentional learning environment (CSILE)
Developed by Scardamalia and Bereiter, Ontario , Canada, 1986 Workspace, Node (discussion) Helps students to develop thinking skills
Innovative technology for collaborative learning (ITCOLE) / Future learning environment (FLE2)
Developed by Leinonen, Helsinki, Finland, 1998 Using innovative learning technology Support work space for collaboration
WebCT A commercial product for higher education on web (2221 colleges, 79
countries) Support teachers create and manage web courses
Do not support teachers to monitor communication relationships on web
Do not support teachers to monitor member-roles on web
11
Related Research Social network
Social network analysis 中央研究院
中研院資科所與社會學所,使用圖論的 Strongly Connected Components 來分析國中生人際網路。
UCI - UCINET Developed by Linton C. Freeman, UMC, for commercial use in social network
analysis CMU - KrackPlot
Developed by David Krackhardt, CMU for commercial use in social network analysis
AT&T - GraphViz (Information Visualization Research) A graphical monitor tool for a connected graph
INSNA (International Network for Social Network Analysis) INSNA support several solution and tools for social network analysis
Do not support teachers to monitor communication relationships on web
Do not support teachers to monitor member-roles on web
12
Related Research Member-roles
Role influence on learning performance analysis Leaderships analysis (Keedy, 1999)
Belbin’s role theory (Belbin, 1981)
9 functional member-roles : All positive functional roles
Benne and Sheat’s member-roles 27 functional member-roles Included positive and negative functional roles
Do not support teachers to monitor communication relationships on web
Do not support teachers to monitor member-roles on web
13
Outline Introduction Related Researches System overview Approaches for group model monitor on network
group learning Constructing the Group learning feature space
Member-roles Communication network analysis
Communication relationships Analyze causal relationships between group status and
group performance Experiments and results Conclusion
14
System overview System architecture
System overview
Email / on-line notification module
Synchronous communication module
Asynchronous communication module
Scheduler / Calendar module
Resource sharing module
Group Portfolio module
Task assignment module
Heterogeneous grouping module Assessment modules On-line monitor
modules
Students
Teachers
Monitor and management modules
Project modules
Interaction modules
15
System overview System architecture
The tools for assisting teachers to monitor and promote groups to learn on web group learning
Group learning behavior in web logs
Group learning interaction in web logs
Group Learning status extractorGroup Learning status extractor
Learning featuresLearning features Communication relationships
Communication relationships
Group profiles
The relationships between learning status and learning
performance extractor
The relationships between learning status and learning
performance extractor
Group learning performance The causal relationships between
learning status and learning performance supports teachers on-
line promoting groups to learn
Learning status on-line monitorLearning status on-line monitor
Project gradesProject grades
Individual grades
Individual grades
Resource sharing
frequency
Resource sharing
frequency
Drop out rate
Drop out rate On-line notification
16
Outline Introduction Related Researches System overview Approaches for group model monitor on network
group learning Constructing the Group learning feature space
Member-roles Communication network analysis
Communication relationships Analyze causal relationships between group status and
group performance Experiments and results Conclusion
17
Approaches for group model monitor on network group learning
Overview
Methodologies Overview
Social science theories
Computer science
techniques
Educational theories
Group learning monitor
Data mining
Machine learning
Information retrieval
Social network
Role theory
Group learning
Positive social interdependence
18
Approaches for group model monitor on network group learning
Overview
Methodologies flow for analyzing the causal relationships between group learning status and group learning performance
Statistical analysis methods for evaluating the significant between group learning status
and group learning performance
Statistical analysis methods for evaluating the significant between group learning status
and group learning performance
Data mining and machine learning techniques for evaluating the causal relationship between
group learning status and group learning performance
Data mining and machine learning techniques for evaluating the causal relationship between
group learning status and group learning performance
significant Abort analyzing
p < 0.01
p >= 0.01
Group learning status & group learning
performance
The causal relationships between group learning
features on group learning performance
19
Approaches for group model monitor on network group learning
Outline
Constructing the group learning features spaces
Communication network analysis Causal relationships analysis for extracting
the causal relationships between group learning status and group learning performance
20
Approaches for group model monitor on network group learning
Outline
Constructing the group learning features spaces
Communication network analysis Causal relationships analysis for extracting
the causal relationships between group learning status and group learning performance
21
Constructing the group learning features spaces
Learning behavior on-line monitor
Group learning behavior in web logs Member-roles
Feature space generator Learning features
Feature space and member-roles on-line monitor
Member-roles extractor
StudentsTopics and abstracts
Teachers
From communication relationships monitor
22
Constructing the group learning features spaces
Group learning feature space
Level-2
Level-3
Level-1
Learning behaviors in
web logs
…
Seldom reply in discussion
Seldom reply in discussion
AND
…
Low-level learning feature query and filter
Low-level learning feature query and filter
Login frequentlyLogin frequently Prefer reading in discussion
Prefer reading in discussion
Read countRead countReply countReply countLogin countLogin count
Fellow-travelerFellow-traveler
Teachers’ view
Data view
23
Constructing the group learning features spaces
Group learning feature spaceFeature id Feature Name Total Degrees Range Abnormal
1.1 Login count 5 0 – MAX --
1.2 Homework grades 5 0 –100 --
1.3 Gender 2 1 – 2 --1.4 Reply count 5 0 – MAX --
1.5 Read count 5 0 – MAX --
… … … …
Feature id Feature Name Combination id Degree Abnormal
2.1 Login frequently 1.1 5 N2.2 Login seldom 1.1 1 Y2.3 Homework success 1.2 5 N2.4 Seldom reply in discussion 1.4 1 Y
2.5 Prefer reading in discussion 1.5 5 N
… … … … …
Feature id Feature Name Combination Operations Abnormal
3.1 Fellow-traveler 2.1,2.4,2.5 AND, AND Y
…
Level 1
Level 2
Level 3
24
Approaches for group model monitor on network group learning
Outline
Constructing the group learning features spaces
Communication network analysis Causal relationships analysis for extracting
the causal relationships between group learning status and group learning performance
25
Communication network analysis
Communication relationships on-line monitor
Group learning communicaitons
in web logs
Sub-group Communication
patterns
Communication relationships analyzer
Interaction content analyzer Topics and abstracts
Students
Communication relationships on-line
monitor
Teachers
To learning behavior monitor
Group Communication
patterns
Graph elements
26
Communication network analysis
Extracting the topics and abstracts IBM Intelligent Miner for text An example of topic extracting:
Dear teammates:I am sorry to be late for the on-line conference of our group
this morning. I have a question and need a favor from you. In the chapter 4, page 45, teachers have illustrated last week. Can anybody kindly tell me the purpose of a member function in an object of the object oriented programming language?
Michael Chen
27
Communication network analysis
Ranking score of topic extracting
Category List Ranking Score
Question for Chapter 1 0.421165
Question for Chapter 2 0.200785
Question for Chapter 3 0.212877
Question for Chapter 4 0.554322
Inquiry for system and environment
0.287286
Gossips discussion 0.336911
… …
28
Communication network analysis
The group communication relationships were represented in Group Learning Communication Network (GLCN) Communication patterns
Millsons’ communication system (Milson, 1973) Subgroup and sub-center
Wasserman’s p* (Wasserman and Faust, 1994)
Graph elements Graph algorithms (Lau, 1989)
Assigned roles’ communication flow Teachers assigned roles
29
Communication network analysis
Communication patterns Milsons’ communication patterns (Milson, 1973)
Represent the group communication relationships
30
Communication network analysis
The communication pattern extractor
31
Communication network analysis
Sub-graph and sub-center Wasserman’s p* elements (Wasserman and Faust, 1994)
Represent the communication relationships among 2-3 students (sub-group)
Reciprocal 2-in-star
2-mixed-star
2-out-star
Transitive Cyclic
32
Communication network analysis
Graph elements (Lau, 1989)
Bridge, cut-point, leaf, flow, circle Assigned roles
Leader, co-leader, reporter, members
D A
B
CF
EG
H
514
23
103
7
5
I
J242
Cut-point
Leaf
bridge
circle
leader
Co-leader
reporter
member
flow
33
Approaches for group model monitor on network group learning
Outline
Constructing the group learning features spaces
Communication network analysis Causal relationships analysis for extracting
the causal relationships between group learning status and group learning performance
34
Causal relationships analysis
Causal relationships between learning status and learning performance extractor
Communication relationships
Member-roles
Causal relationships extractor
Group learning performance
Project gradesProject grades
Individual grades
Individual grades
Resource sharing frequency
Resource sharing frequency
Drop out rate
Drop out rate
Causal relationships between learning
status and learning performance
Bayesian Belief Network
Bayesian Belief NetworkDecision
TreeDecision
TreeAssociation
RuleAssociation
Rule
Statistical analysis
Statistical analysis
35
Causal relationships analysis
Association rules (J.W. Han, 1996)
A1 ^ A2 ^ … ^ Am → B1 ^ B2 ^ …Bn
where Ai(for i {1,…,m}) and Bj(for i {1,…,m})
For example:
Gender=M AND Age=D -> Login_at_mid_night (78%)
36
Causal relationships analysis
Bayesian belief network (M. Ramoni, and P. Sebastiani, 1997)
Extract the causal relationships between status and performance
37
Causal relationships analysis
Decision Tree : C5.0 (J.R. Quinlan, 1993)
Extract the partial rules of causal relationships between status and performance
Leader_Flow
Leader_Flow
D Leaf number
Leaf number
Cut-point number
Cut-point number
2-in-star2-in-star
Flow-minimum
Flow-minimum
C
A
B
E
<=0
>0
<=1
>1
<=20
>20
<=3
>3
19.4/8.02.6/1.2
2.8/0.8
4.4
2.7
>15
<15
38
Outline Introduction Related Researches System overview Approaches for group model monitor on
network group learning Constructing the Group learning feature space
Member-roles Communication network analysis
Communication relationships Analyze causal relationships between group status
and group performance Experiments and results Conclusion
39
Experiments and Results overview
Participants, environment and collected data
Group learning behaviors analysis Communication relationships analysis Causal relationships between group learning
status and group learning performance analysis Learning behavior Communication relationships Member-roles
40
Experiments and Results overview
Participants, environment and collected data
Group learning behaviors analysis Communication relationship analysis Causal relationships between group learning
status and group learning performance analysis Learning behavior Communication relationships Member-roles
41
Experiments and ResultsParticipants, environment and collected data
Participants 計算機網路概論 7 teachers, 5 TAs, and 706 students (high school teachers) 459 male (65%) , 247 female (35%) 1999 , Jul. 1 to Sep. 1 heterogeneous grouping : by Thinking style (Sternberg, 1997)
Interface and Environment Server : NT4.0, IIS 5.0 ,ASP, Oracle DBMS Client : Web browsers Curriculums are put on Video CDs, Books
Collected data Web logs during 3 months 9118 interactions during 3 months Examination : includes the mid-exam and final-exam
discrete grades A-E (E grade represents drop-out individuals) Group project grade : a group project of web page constructing
discrete grades A-E (E grade represents drop-out groups)
42
Experiments and Results overview
Participants, environment and collected data
Group learning behaviors analysis Communication relationships analysis Causal relationships between group learning
status and group learning performance analysis Learning behavior Communication relationships Member-roles
43
Experiments and ResultsGroup learning behaviors analysis
Input : 243,500 web logs (345 actions/person)
Tools : group learning features space generator Output : group learning feature space and
member-roles 52 learning features are generated Factor analysis into 6 groups of learning features
Online discussion, working on task, competition, reading resource, uploading resource, updating resource
11 member-roles are detected
44
Experiments and Results overview
Participants, environment and collected data
Group learning behaviors analysis Communication relationships analysis Causal relationships between group learning
status and group learning performance analysis Learning behavior Communication relationships Member-roles
45
Experiments and ResultsCommunication relationships analysis
Input : 9118 interactions Tools : IBM Intelligent Miner for Text, GLCN
extractor Output : interaction topics, abstracts , and GLCN
6 patterns are extracted Topics : 25 categories of topics (200 for training) Abstract : 9118 abstract sentence Accuracy of topics and abstract extracting:Feedback Topics Abstract
Good 73.0 %96.6 %
55.0 %73.3 %
Acceptable 23.6 % 18.3 %
Mistake 3.3 % 26.67 %
46
Experiments and ResultsCommunication relationships analysis
ANOVA analysis for significant difference among patterns
GLCN pattern unresponsive dominant leader tete-a-tete cliquish ideal unsocial
Mean 71.69187 75.74435 68.54046 74.04825 76.85906 38.29603
SD 10.18473 8.591956 8.506688 10.00014 11.85958 22.75898
Count (n) 5 18 11 10 3 23
Source of Variance SS df MS F
Between groups 19375.55 5 3875.11 16.57*
Within groups(errors) 14970.21 64 233.9096
*p<0.01
47
Experiments and Results overview
Participants, environment and collected data
Group learning behaviors analysis Interpersonal interaction analysis Causal relationships between group learning
status and group learning performance analysis Learning behavior Communication relationships Member-roles
48
Experiments and ResultsCausal relationship analysis – behaviors
Causal relationships between learning behaviors and learning performance using Association rules analysis
Tool : DB Miner Han, J.W. , 1996 Simon Fraser University, Canada
Login_count=D -> P_grade=D (83%)Gender=M AND Age=D -> Login_at_mid_night (78%)P_grade =D -> Login_count=D AND Post_count=D (65%)P_grade=D -> Login_count=D AND Read_count=D (68%)Login_day=Saturday AND Login_ time=morning → post=D (75%)Login_count =D AND Discuss_count=D -> H_grade=D (85%)
49
Experiments and ResultsCausal relationship analysis – behaviors
Causal relationships between learning behaviors and learning performance using Bayesian belief network analysis
Tool : Bayesian Knowledge Discover (BKD) Ramoni and Sebastiani , 1997 Knowledge media institute, Open university, UK
50
Experiments and ResultsCausal relationship analysis – behaviors
Learning performance prediction using Bayesian classifier Tool : Robust Classifier (RoC)
Ramoni and Sebastiani , 1999 Knowledge media institute, Open university, UK
Group Id
Online Discussion
Working on Task
Competition Reading Resource
Uploading Resource
Updating Resource
Grade
1 195 195 34 64 18 3 ? 2 186 186 17 165 45 12 ? 3 42 42 9 0 8 1 ? 4 131 131 118 44 11 2 ? 5 251 251 215 196 35 42 ? 6 265 265 55 198 23 3 ? 7 103 103 614 91 28 0 ? 8 266 266 84 175 69 18 ? 9 141 141 25 68 27 2 ?
10 142 142 15 70 33 1 ? … … … … 70 50 50 34 4 6 0 D
Total 52 attributes, 70 groups
51
Experiments and ResultsCausal relationship analysis – behaviors
One of the output file of predicted result
Group id Predicted Grade
Grade Probability of Grade A
Probability of Grade B
Probability of Grade C
Probability of Grade D
1 B B 0.049 0.730 0.213 0.007
2 B B 0.018 0.901 0.079 0.001
3 D C 0.011 0.006 0.472 0.611
4 C C 0.250 0.246 0.450 0.054
5 B B 0.006 0.938 0.055 0.000
6 B B 0.024 0.832 0.141 0.002
7 C B 0.258 0.348 0.367 0.028
8 B B 0.007 0.974 0.019 0.000
9 B B 0.142 0.513 0.342 0.003
10 C C 0.172 0.343 0.464 0.021
Correct: 8Incorrect: 2Accuracy: 80 %Coverage 100.0 %
52
Experiments and ResultsCausal relationship analysis – behaviors
The 7 times of prediction for Grade value and the accuracy (leave-one-out method)
Testing data Accuracy
Group 1 to group 10 80 %
Group 11 to group 20 70 %
Group 21 to group 30 80 %
Group 31 to group 40 70 %
Group 41 to group 50 70 %
Group 51 to group 60 70 %
Group 61 to group 70 80 %
Average Accuracy 74.28 %
53
Experiments and ResultsCausal relationship analysis – behaviors
Predicting the flunk groupsGroup Id Predicted
ResultProbability for Predicted Result
11 D 0.942
14 D 0.942
22 D 0.967
30 D 0.967
39 D 0.970
42 D 0.986
47 B 0.630
54 D 0.986
61 D 0.925
62 D 0.925
63 D 0.925
67 D 0.888
68 D 0.867
Accuracy for flunk prediction 92.30 %
54
Experiments and Results overview
Participants, environment and collected data
Group learning behaviors analysis Interpersonal interaction analysis Causal relationships between group learning
status and group learning performance analysis Learning behavior Communication relationships Member-roles
55
Experiments and ResultsCausal relationship analysis – communication relationships
Significant difference analysis for GLCN patterns on average individual grades(ANOVA)
GLCN pattern unresponsive dominant leader tete-a-tete cliquish ideal unsocial
Mean 71.69187 75.74435 68.54046 74.04825 76.85906 38.29603
SD 10.18473 8.591956 8.506688 10.00014 11.85958 22.75898
Count (n) 5 18 11 10 3 23
Source of Variance SS df MS F
Between groups 19375.55 5 3875.11 16.57*
Within groups(errors) 14970.21 64 233.9096
*p<0.01
56
Experiments and ResultsCausal relationship analysis – communication relationships
Post hoc (Sheffe’s method)
The result shows the unsocial pattern has significant difference with other patterns on average individual grades
*p<0.01
Groups unresponsive dominant leader tete-a-tete cliquish ideal unsocial
unresponsive --------- 0.998 1.000 1.000 0.999 0.004*
Dominant leader --------- 0.909 1.000 1.000 0.000*
Tete-a-tete --------- 0.983 0.982 0.000*
Cliquish --------- 1.000 0.000*
Ideal --------- 0.009*
Unsocial ---------
57
Experiments and ResultsCausal relationship analysis – communication relationships
Significant difference analysis for GLCN patterns on group grades(ANOVA)
GLCN pattern unresponsive dominant leader tete-a-tete cliquish ideal unsocial
Mean 63.33333 80.64815 66.36364 83.33333 83.88889 35.14493
SD 35.60977 5.305654 33.46489 3.767961 3.469443 41.20628
Count (n) 5 18 11 10 3 23
Source of Variance SS df MS F
Between groups 29015.73 5 5803.147 6.85*
Within groups(errors) 54256.69 64 847.7607
*p<0.01
58
Experiments and ResultsCausal relationship analysis – communication relationships
Post hoc (Sheffe’s method)
The result shows the unsocial pattern has significant difference with dominant leader and cliquish pattern on group grades
Groups unresponsive dominant leader tete-a-tete cliquish ideal unsocial
unresponsive --------- 0.924 1.000 0.902 0.967 0.575
Dominant leader --------- 0.894 1.000 1.000 0.001*
Tete-a-tete --------- 0.877 0.972 0.145
Cliquish --------- 1.000 0.004*
Ideal --------- 0.206
Unsocial ---------
*p<0.01
59
Experiments and ResultsCausal relationship analysis – communication relationships
significant difference analysis for GLCN patterns on resource sharing frequency(ANOVA)
GLCN pattern unresponsive dominant leader tete-a-tete cliquish ideal unsocial
Mean 38.6 54.94444 29.36364 66.4 73.66667 11.52174
SD 21.41962 39.37971 36.21953 43.07668 43.7531 24.15644
Count (n) 5 18 11 10 3 23
Source of Variance SS df MS F
Between groups 33997.15 5 6799.429 5.83*
Within groups(errors) 74683.5 64 1166.93
*p<0.01
60
Experiments and ResultsCausal relationship analysis – communication relationships
Post hoc (Sheffe’s method)
The result shows the unsocial pattern has significant difference with dominant leader and cliquish pattern on resource sharing frequency
Groups unresponsive dominant leader tete-a-tete cliquish ideal unsocial
unresponsive --------- 0.969 0.998 0.818 0.850 0.763
Dominant leader --------- 0.578 0.981 0.978 0.011*
Tete-a-tete --------- 0.305 0.559 0.843
Cliquish --------- 1.000 0.006*
Ideal --------- 0.135
Unsocial ---------
*p<0.01
61
Experiments and ResultsCausal relationship analysis – communication relationships
significant difference analysis for GLCN patterns on drop out rate (ANOVA)
GLCN pattern unresponsive dominant leader tete-a-tete cliquish ideal unsocial
Mean 2.6 1.888889 3 2.1 1.666667 6.391304
SD 1.516575 1.07861 1.095445 1.37032 1.527525 2.589123
Count (n) 5 18 11 10 3 23
Source of Variance SS df MS F
Between groups 274.563 5 54.9126 16.73*
Within groups(errors) 210.0227 64 3.281605
*p<0.01
62
Experiments and ResultsCausal relationship analysis – communication relationships
Post hoc (Sheffe’s method)
The result shows the unsocial pattern has significant difference with other patterns on drop out rate
Groups unresponsive dominant leader tete-a-tete cliquish ideal unsocial
unresponsive --------- 0.987 0.999 0.998 0.992 0.006*
Dominant leader --------- 0.765 1.000 1.000 0.000*
Tete-a-tete --------- 0.934 0.936 0.000*
Cliquish --------- 1.000 0.000*
Ideal --------- 0.006*
Unsocial ---------
*p<0.01
63
Experiments and ResultsCausal relationship analysis – communication relationships
Factor analysis for 37 GLCN elements into 4 primary factors Leader = 組長功能
由因素負荷量最高的特徵“組長的討論流量” 代表。 Sub-group = 2 至 3 人之間關係
由因素負荷量最高的特徵“ 2-in-star 數目”代表。 Student = 學生互動數量
由因素負荷量最高的特徵“學生間溝通總次數”代表。 Leaf & Center = 單一溝通與溝通中心
由因素負荷量最高的特徵“ BRIDGE 個數”代表。
64
Experiments and ResultsCausal relationship analysis – communication relationships
GLCN vs. drop out rate - BKD
“ sub_group” 影響 Pattern “組長功能” 影響 “ subgroup”
“組長功能” 影響 “單一溝通與溝通中心”
Pattern 影響 “學生輟學率”
65
Experiments and ResultsCausal relationship analysis – communication relationships
GLCN vs. drop out rate - BKD
Pattern 輟學 未輟學Unresponsive
0.338 0.662
Unsocial 0.824 0.176Dominant 0.115 0.885Tete-a-tete 0.187 0.813Fragmented 0.107 0.893Ideal 0.026 0.974
“ unsocial”pattern 的小組有 82.4% 的機率成為較易輟學的小組組長討論量大致上與小組輟學的可能性為負相關
66
Experiments and ResultsCausal relationship analysis – communication relationships
GLCN vs. resource sharing frequency -BKD
Resource sharing frequency
was influenced byCommunication Pattern“ sub_group”“ Leader”“ Student”“ Leaf & Center”
67
Experiments and ResultsCausal relationship analysis – communication relationships
GLCN vs. group project grade - BKD 小組成績與 communication relationships 無明顯相關性
Experiments and ResultsCausal relationship analysis – communication relationships
bridge 個數 <= 1::...2-in-star_with_reciprocity 次數 <= 1: E (29.0/13.0): 2-in-star_with_reciprocity 次數 > 1:: :... 普通學生 -> 普通學生 _ 的討論流量 <= 3: C (2.0): 普通學生 -> 普通學生 _ 的討論流量 > 3: B (4.0)bridge 個數 > 1::...PATTERN = ”unresponsive”: B (2.0/1.0) PATTERN = ”ideal”: B (0.0) PATTERN = ”unsocial”: B (1.0) PATTERN = ”fragmented”: :...bridge 個數 <= 3: B (5.0/1.0) : bridge 個數 > 3: C (4.0/1.0) PATTERN = ”dominant”: :... 普通學生 -> 普通學生 _ 的討論流量 > 1: D (3.0/1.0) : 普通學生 -> 普通學生 _ 的討論流量 <= 1: : :...2-in-star_with_reciprocity 次數 <= 4: C (5.0/2.0) : 2-in-star_with_reciprocity 次數 > 4: B (8.0/3.0) PATTERN = ”tete-a-tete”: :...bridge 個數 <= 2: C (3.0/1.0) bridge 個數 > 2: :... 普通學生 -> 普通學生 _ 的討論流量 <= 1: A (2.0/1.0) 普通學生 -> 普通學生 _ 的討論流量 > 1: D (2.0)
因素的重要性大致為:1. Leaf & Center2. Pattern 、 Sub_
group3. Student
69
Experiments and ResultsCausal relationship analysis – communication relationships
Decision rules on project grades
Rule 0/11: (cover 29) bridge 個數 <= 1 2-in-star_with_reciprocity 次數 <=1 -> class E [0.548]
若小組符合 ”小組內 Bridge 個數 <= 1, 2-in-star with reciprocity 次數 <=1次” , ” 小組團體成績表現”為 E 機率為54.8%
Rule 0/3: (cover 22) 2-in-star 次數 > 4 -> class B [0.625]
若小組符合 ” 2-in-star 次數大於 4”,
”小組團體成績表現”為 B 機率為62.5%
Rule 1/7: (cover 22.4) PATTERN = unsocial bridge 個數 <= 1 -> class E [0.516]
若小組符合 ” Pattern=unsocial, Bridge 個數 <=1 個” , ” 小組團體成績表現”為 E 機率為51.6%
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Experiments and Results overview
Participants, environment and collected data
Group learning behaviors analysis Interpersonal interaction analysis Causal relationships between group learning
status and group learning performance analysis Learning behavior Communication relationships Member-roles
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Experiments and ResultsCausal relationship analysis – member-roles
11 member-roles are detected by observing the group communication patterns and learning behaviors
N=706 Each student plays : at least 1 role, at most 8 roles, average 1.53
Detected member-
roles
Initiator-contributor
Information Giver
Opiniongiver
Coordinator Energizer Procedural technician
and recorder
Encourager harmonizer
playboy Dominator Fellow-traveler
Count 65 53 201 62 65 67 69 196 135 32 133
Ratio 9.20% 7.50% 28.47% 8.78% 9.20% 9.49% 9.77% 27.76% 19.12% 4.53% 18.84%
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Experiments and ResultsCausal relationship analysis – member-roles
T-test for significant difference evaluation of member-roles exist or not on individual grades
Detected Member-roles (Y/N) p
Initiator - contributor 0.000*
Information - giver 0.000*
Opinion - giver 0.000*
coordinator 0.000*
energizer 0.000*
Procedural technician and recorder
0.000*
encourager 0.000*
harmonizer 0.000*
Playboy 0.000*
dominator 0.000*
Fellow-traveler 0.122
* p < 0.01 n=706
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Experiments and ResultsCausal relationship analysis – member-roles
T-test for significant difference evaluation of member-roles exist or not on resource sharing frequency
Detected Member-roles (Y/N) p
Initiator - contributor 0.000*
Information - giver 0.000*
Opinion - giver 0.000*
coordinator 0.000*
energizer 0.000*
Procedural technician and recorder
0.000*
encourager 0.000*
harmonizer 0.000*
Playboy 0.000*
dominator 0.000*
Fellow-traveler 0.000*
* p < 0.01 n=706
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Experiments and ResultsCausal relationship analysis – member-roles
T-test for significant difference evaluation of member-roles exist or not on group project grades
Detected Member-roles (Y/N) p
Initiator - contributor 0.000*
Information - giver 0.000*
Opinion - giver 0.185
coordinator 0.000*
energizer 0.000*
Procedural technician and recorder
0.011
encourager 0.000*
harmonizer 0.018
Playboy 0.008*
dominator 0.000*
Fellow-traveler 0.003*
* p < 0.01 n=70
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Experiments and ResultsCausal relationship analysis – member-roles
T-test for significant difference evaluation of member-roles exist or not on group drop out rate
Detected Member-roles (Y/N) p
Initiator - contributor 0.000*
Information - giver 0.000*
Opinion - giver 0.063
coordinator 0.000*
energizer 0.000*
Procedural technician and recorder
0.023
encourager 0.000*harmonizer 0.046
Playboy 0.008*
dominator 0.000*
Fellow-traveler 0.003*
* p < 0.01 n=70
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Experiments and ResultsCausal relationship analysis – member-roles
Factor analysis into 2 role groups
Linear regression analysis on group project grade
Member-roles Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
(Constant) 71.869 4.421 16.257 .000
Roles group1 17.957 4.768 .302 3.766 .000 .897 1.115
Roles group2 -9.207 1.164 -.633 -7.911 .000 .897 1.115
Group project grade = 0.302 * roles group1 – 0.633 * roles group2
Roles group1 Dominator,Encourager, information-giver,Playboy,Initiator-contributor,Coordinator,Opinion giver
Roles group2 Fellow-traveler, Follower, Energizer, Procedural technician and recorder
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Experiments and ResultsCausal relationship analysis – member-roles
Linear regression analysis on group drop out rate
Roles group1 Dominator,Encourager, information-giver,Playboy,Initiator-contributor,Coordinator,Opinion giver
Roles group2 Fellow-traveler, Follower, Energizer, Procedural technician and recorder
Unstandardized
Coefficients
Standardized Coefficients t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
(Constant) 4.404 .526 8.374 .000
Positive roles -1.250 .567 -.274 -2.203 .031 .897 1.115
Negative roles -.115 .138 -.104 -.832 .408 .897 1.115
Drop rate = 0.274 * roles group1 – 0.104 * roles group2 –
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Experiments and ResultsCausal relationship analysis – member-roles
BBN analysis on individual grades
Roles group1 Dominator,Encourager, information-giver,Playboy,Initiator-contributor,Coordinator,Opinion giver
Roles group2 Fellow-traveler, Follower, Energizer, Procedural technician and recorder
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Conclusion contributions of results
The 11 member-roles exist or not has significant influence on Project grades, individual grades, resource sharing frequency and
drop out rate Member-roles 人數對 project grades 具有正相關
Dominator,Encourager, information-giver,Playboy,Initiator-contributor,Coordinator,Opinion giver
Member-roles 人數對 project grades 具有負相關 Fellow-traveler, Follower, Energizer,
Procedural technician and recorder Member-roles 人數對於 drop out rate 無影響
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Conclusion contributions of results
The communication relationships have significant influence on Project grades, resource sharing frequency and drop out rate
The groups have “unsocial” communication pattern have higher probability that the group will get lower group performance
The BBN analysis extracted: The inter-relationships among GLCN elements
Communication Pattern are influenced by “ sub-group” “ sub-group” and “Leaf & Center” are both influenced by “ Leader”
The influence of communication-relationships on group learning performance
“Drop out rate” are influenced by communication patterns “Resource sharing frequency” are influenced by (1) communication
patterns, (2) “Leader”, (3) “subgroup”, (4)“Leaf & Center”,(5)”Student” Decision Tree analysis
“ Leaf & Center” > Pattern “、 Sub-group” > “Student”
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Conclusion contributions of works
Propose the learning feature space concept for teachers monitoring group learning status by exploring group learning behaviors
Provide the communication network exploring tool for teachers monitoring group learning status by exploring communications
Integrate theories of social science theories, educational theories and computer science techniques to extract the causal relationships between learning status and learning performance
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Papers Journal Papers
Gwo-Dong Chen, Kuo-Liang Ou, Chen-Chung Liu and Baw-Jhiune LiuIntervention and strategy analysis for web group-learningJournal of Computer Assisted Learning (JCAL), Vol. 17(1), 58-71, 2001. (SSCI)
Gwo-Dong Chen, Chen-Chung Liu, Kuo-Liang Ou, and Baw-Jhiune LiuDiscovering decision knowledge from web log portfolio for managing classroom processes by applying decision tree and data cube technology. Journal of Educational Computing Research (JECR), Vol. 23(3), 305-332, 2000. (SSCI/SCI)
Gwo-Dong Chen, Chen-Chung Liu, Kuo-Liang Ou, and Ming-Song LinWeb learning portfolios: a tool for supporting performance awarenessInnovations in Education and Training International (IETI), Vol. 38(1), 2000. (SSCI)
Chen-Chung Liu, Gwo-Dong Chen, Kuo-Liang Ou, Baw-Jhiune Liu, and Jorng-Tzong HorngManaging Activity Dynamics of Web Based Collaborative Applications.International Journal on Artificial Intelligent Tools (JAIT), Vol 8,(2) 207-227, 1999.
Chih-Kai Chang, Gwo-Dong Chen, and Kuo-Liang Ou Student Portfolio Analysis by Data Cube Technology for Decision Support of Web Based Classroom Teacher Journal of Educational Computing Research (JECR), Vol. 19(3), 1998. (SSCI/SCI)
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Papers Journal Paper Submitted & Prepared for
Submitting Gwo-Dong Chen, Kuo-Liang Ou, and Chin-Yeh Wang
Use of group discussion and learning portfolio to build knowledge for managing web group learningSubmitted to Journal of Educational Computing Research (JECR), (SSCI/SCI)
Gwo-Dong Chen, Kuo-Liang Ou, and Chin-Yeh WangUsing group communication relationships to monitor web group learning.Prepared for submitting to Journal of Computer Assisted Learning (JCAL)
Gwo-Dong Chen, Kuo-Liang Ou, and Chin-Yeh WangUsing groups’ social interaction to detect the member roles and discover the influence on group learning performancePrepared for submitting to Human and Computer Interaction (HCI)
Thank you very much