依據群體模組監控之網路群體學習系統 group model monitor on network group learning...

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

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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

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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

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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

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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

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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

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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

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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

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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



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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



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Outline Introduction Related Researches System overview Approaches for group model monitor on network

group learning Constructing the Group learning feature space


Communication relationships Analyze causal relationships between group status and

group performance Experiments and results Conclusion

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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

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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

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Outline Introduction Related Researches System overview Approaches for group model monitor on network

group learning Constructing the Group learning feature space


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

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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


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


Outline




21


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

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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

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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

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Outline




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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

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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

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Ranking score of topic extracting

Category List Ranking Score

Question for Chapter 1 0.421165




Inquiry for system and environment

0.287286

Gossips discussion 0.336911

… …

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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

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Communication patterns Milsons’ communication patterns (Milson, 1973)

Represent the group communication relationships

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The communication pattern extractor

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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

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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

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Outline




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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


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

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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%)

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Bayesian belief network (M. Ramoni, and P. Sebastiani, 1997)

Extract the causal relationships between status and performance

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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

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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

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Group learning behaviors analysis Communication relationship analysis Causal relationships between group learning


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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)

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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

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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 %

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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

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Group learning behaviors analysis Interpersonal interaction analysis Causal relationships between group learning


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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%)

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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

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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

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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 %

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The 7 times of prediction for Grade value and the accuracy (leave-one-out method)

Testing data Accuracy

Group 1 to group 10 80 %







Average Accuracy 74.28 %

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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 %

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Experiments and ResultsCausal relationship analysis – communication relationships

Significant difference analysis for GLCN patterns on average individual grades(ANOVA)


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


Between groups 19375.55 5 3875.11 16.57*


*p<0.01

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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 ---------

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Significant difference analysis for GLCN patterns on group grades(ANOVA)


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


Between groups 29015.73 5 5803.147 6.85*


*p<0.01

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The result shows the unsocial pattern has significant difference with dominant leader and cliquish pattern on group grades


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

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significant difference analysis for GLCN patterns on resource sharing frequency(ANOVA)


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


Between groups 33997.15 5 6799.429 5.83*


*p<0.01

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The result shows the unsocial pattern has significant difference with dominant leader and cliquish pattern on resource sharing frequency


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

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significant difference analysis for GLCN patterns on drop out rate (ANOVA)


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


Between groups 274.563 5 54.9126 16.73*


*p<0.01

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The result shows the unsocial pattern has significant difference with other patterns on drop out rate


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


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


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


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


GLCN vs. resource sharing frequency －BKD


was influenced byCommunication Pattern“ sub_group”“ Leader”“ Student”“ Leaf & Center”

67


GLCN vs. group project grade － BKD 小組成績與 communication relationships 無明顯相關性


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


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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71

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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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

73


T-test for significant difference evaluation of member-roles exist or not on resource sharing frequency




Opinion - giver 0.000*

coordinator 0.000*

energizer 0.000*


0.000*

encourager 0.000*

harmonizer 0.000*

Playboy 0.000*

dominator 0.000*

Fellow-traveler 0.000*

* p < 0.01 n=706

74


T-test for significant difference evaluation of member-roles exist or not on group project grades




Opinion - giver 0.185

coordinator 0.000*

energizer 0.000*


0.011

encourager 0.000*

harmonizer 0.018

Playboy 0.008*

dominator 0.000*


* p < 0.01 n=70

75


T-test for significant difference evaluation of member-roles exist or not on group drop out rate




Opinion - giver 0.063

coordinator 0.000*

energizer 0.000*


0.023

encourager 0.000*harmonizer 0.046

Playboy 0.008*

dominator 0.000*


* p < 0.01 n=70

76


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

77


Linear regression analysis on group drop out rate



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 –

78


BBN analysis on individual grades



79

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

80

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

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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...

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