1. 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. 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. 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. 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. 5 Introduction Theory framework The conditions mediating the relationship between cooperation and achievement (Johnson and Johnson, 1991) OUTCOME SOCIAL SKILLS PROMOTIVE INTERACTION POSITIVE INTERDEPENDENCE Web Group communication relationships Web Group learning behaviors Goal and Reward interdependence Task, role, and resource interdependence

6. 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 Group interactions Learning behaviors Learning performance Web Server

7. 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 Web Server Group interactions Learning behaviors Learning performance Monitor Extract the relationships

8. 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. 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. 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. 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. 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. 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. 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. 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 extractor Learning features Communication relationships Group profiles 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 monitor Project grades Individual grades Resource sharing frequency Drop out rate On-line notification

16. 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. 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. 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 Data mining and machine learning techniques for evaluating the causal relationship between group learning status and group learning performance significantAbort 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. 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. 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. 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 Students Topics and abstracts Teachers From communication relationships monitor

22. 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 AND … Low-level learning feature query and filter Login frequently Prefer reading in discussion Read count Reply count Login count Fellow-traveler Teachers ’ view Data view

23. 23 Constructing the group learning features spaces Group learning feature space Feature idFeature NameTotal DegreesRangeAbnormal 1.1Login count50 – MAX-- 1.2Homework grades50 – 100-- 1.3Gender21 – 2-- 1.4Reply count50 – MAX-- 1.5Read count50 – MAX-- ………… Feature idFeature NameCombination idDegreeAbnormal 2.1Login frequently1.15N 2.2Login seldom1.11Y 2.3Homework success1.25N 2.4Seldom reply in discussion1.41Y 2.5Prefer reading in discussion1.55N …………… Feature idFeature NameCombinationOperationsAbnormal 3.1Fellow-traveler2.1,2.4,2.5AND, ANDY … Level 1 Level 2 Level 3

24. 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. 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. 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. 27 Communication network analysis Ranking score of topic extracting Category ListRanking Score Question for Chapter 10.421165 Question for Chapter 20.200785 Question for Chapter 30.212877 Question for Chapter 40.554322 Inquiry for system and environment 0.287286 Gossips discussion0.336911 ……

28. 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. 29 Communication network analysis Communication patterns Milsons ’ communication patterns (Milson, 1973) Represent the group communication relationships

30. 30 Communication network analysis The communication pattern extractor

31. 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. 32 Communication network analysis Graph elements (Lau, 1989) Bridge, cut-point, leaf, flow, circle Assigned roles Leader, co-leader, reporter, members D A B C F E G H 5 1 4 2 3 10 3 7 5 I J 2 4 2 Cut- point Leaf bridge circle leader Co- leader reporter member flow

33. 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. 34 Causal relationships analysis Causal relationships between learning status and learning performance extractor Communication relationships Member-roles Causal relationships extractor Group learning performance Project grades Individual grades Resource sharing frequency Drop out rate Causal relationships between learning status and learning performance Bayesian Belief Network Decision Tree Association Rule Statistical analysis

35. 35 Causal relationships analysis Association rules (J.W. Han, 1996) A 1 ^ A 2 ^ … ^ A m → B 1 ^ B 2 ^ … B n where A i (for i  {1, …,m}) and B j (for i  {1, …,m}) For example: Gender=M AND Age=D -> Login_at_mid_night (78%)

36. 36 Causal relationships analysis Bayesian belief network (M. Ramoni, and P. Sebastiani, 1997) Extract the causal relationships between status and performance

37. 37 Causal relationships analysis Decision Tree : C5.0 (J.R. Quinlan, 1993) Extract the partial rules of causal relationships between status and performance Leader_F low D Leaf number Cut-point number 2-in-star Flow- minimum Flow- minimum C A B E <=0 >0 <=1 >1 <=20 >20 <=3 >3 19.4/8.0 2.6/1.2 2.8/0.8 4.4 2.7 >15 <15

38. 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. 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. 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. 41 Experiments and Results Participants, 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. 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. 43 Experiments and Results Group 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. 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. 45 Experiments and Results Communication 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: FeedbackTopicsAbstract Good73.0 % 96.6 % 55.0 % 73.3 % Acceptable23.6 %18.3 % Mistake3.3 %26.67 %

46. 46 Experiments and Results Communication relationships analysis ANOVA analysis for significant difference among patterns GLCN patternunresponsivedominant leadertete-a-tetecliquishidealunsocial Mean 71.6918775.7443568.5404674.0482576.8590638.29603 SD 10.184738.5919568.50668810.0001411.8595822.75898 Count (n) 5181110323 Source of VarianceSSdfMSF Between groups19375.5553875.1116.57* Within groups(errors)14970.2164233.9096 *p<0.01

47. 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. 48 Experiments and Results Causal 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. 49 Experiments and Results Causal 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. 50 Experiments and Results Causal relationship analysis – behaviors Learning performance prediction using Bayesian classifier Tool : Robust Classifier (RoC) Ramoni and Sebastiani, 1999 Knowledge media institute, Open university, UK Total 52 attributes, 70 groups

51. 51 Experiments and Results Causal relationship analysis – behaviors One of the output file of predicted result Group idPredicted Grade GradeProbability of Grade A Probability of Grade B Probability of Grade C Probability of Grade D 1BB0.0490.7300.2130.007 2BB0.0180.9010.0790.001 3DC0.0110.0060.4720.611 4CC0.2500.2460.4500.054 5BB0.0060.9380.0550.000 6BB0.0240.8320.1410.002 7CB0.2580.3480.3670.028 8BB0.0070.9740.0190.000 9BB0.1420.5130.3420.003 10CC0.1720.3430.4640.021 Correct:8 Incorrect:2 Accuracy: 80 % Coverage100.0 %

52. 52 Experiments and Results Causal relationship analysis – behaviors The 7 times of prediction for Grade value and the accuracy (leave-one-out method) Testing dataAccuracy Group 1 to group 1080 % Group 11 to group 2070 % Group 21 to group 3080 % Group 31 to group 4070 % Group 41 to group 5070 % Group 51 to group 6070 % Group 61 to group 7080 % Average Accuracy74.28 %

53. 53 Experiments and Results Causal relationship analysis – behaviors Predicting the flunk groups Group IdPredicted ResultProbability for Predicted Result 11D0.942 14D0.942 22D0.967 30D0.967 39D0.970 42D0.986 47B0.630 54D0.986 61D0.925 62D0.925 63D0.925 67D0.888 68D0.867 Accuracy for flunk prediction92.30 %

54. 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. 55 Experiments and Results Causal relationship analysis – communication relationships Significant difference analysis for GLCN patterns on average individual grades (ANOVA) GLCN patternunresponsivedominant leadertete-a-tetecliquishidealunsocial Mean 71.6918775.7443568.5404674.0482576.8590638.29603 SD 10.184738.5919568.50668810.0001411.8595822.75898 Count (n) 5181110323 Source of VarianceSSdfMSF Between groups 19375.55 5 3875.1116.57* Within groups(errors) 14970.21 64 233.9096 *p<0.01

56. 56 Experiments and Results Causal 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 Groupsunresponsivedominant leadertete-a-tetecliquishidealunsocial unresponsive---------0.9981.000 0.9990.004* Dominant leader ---------0.9091.000 0.000* Tete-a-tete ---------0.9830.9820.000* Cliquish ---------1.0000.000* Ideal ---------0.009* Unsocial ---------

57. 57 Experiments and Results Causal relationship analysis – communication relationships Significant difference analysis for GLCN patterns on group grades (ANOVA) GLCN patternunresponsivedominant leadertete-a-tetecliquishidealunsocial Mean 63.3333380.6481566.3636483.3333383.8888935.14493 SD 35.609775.30565433.464893.7679613.46944341.20628 Count (n) 5181110323 Source of VarianceSSdfMSF Between groups 29015.7355803.1476.85* Within groups(errors) 54256.69 64 847.7607 *p<0.01

58. 58 Experiments and Results Causal 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 Groupsunresponsivedominant leadertete-a-tetecliquishidealunsocial unresponsive---------0.9241.0000.9020.9670.575 Dominant leader ---------0.8941.000 0.001* Tete-a-tete ---------0.8770.9720.145 Cliquish ---------1.000 0.004* Ideal ---------0.206 Unsocial --------- *p<0.01

59. 59 Experiments and Results Causal relationship analysis – communication relationships significant difference analysis for GLCN patterns on resource sharing frequency (ANOVA) GLCN patternunresponsivedominant leadertete-a-tetecliquishidealunsocial Mean 38.654.9444429.3636466.473.6666711.52174 SD 21.4196239.3797136.2195343.0766843.753124.15644 Count (n) 5181110323 Source of VarianceSSdfMSF Between groups 33997.1556799.4295.83* Within groups(errors) 74683.5641166.93 *p<0.01

60. 60 Experiments and Results Causal 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 Groupsunresponsivedominant leadertete-a-tetecliquishidealunsocial unresponsive---------0.9690.9980.8180.8500.763 Dominant leader ---------0.5780.9810.978 0.011* Tete-a-tete ---------0.3050.5590.843 Cliquish ---------1.000 0.006* Ideal ---------0.135 Unsocial --------- *p<0.01

61. 61 Experiments and Results Causal relationship analysis – communication relationships significant difference analysis for GLCN patterns on drop out rate (ANOVA) GLCN patternunresponsivedominant leadertete-a-tetecliquishidealunsocial Mean 2.61.88888932.11.6666676.391304 SD 1.5165751.078611.0954451.370321.5275252.589123 Count (n) 5181110323 Source of VarianceSSdfMSF Between groups 274.563554.912616.73* Within groups(errors) 210.0227643.281605 *p<0.01

62. 62 Experiments and Results Causal 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 Groupsunresponsivedominant leadertete-a-tetecliquishidealunsocial unresponsive---------0.9870.9990.9980.9920.006* Dominant leader ---------0.7651.000 0.000* Tete-a-tete ---------0.9340.9360.000* Cliquish ---------1.0000.000* Ideal ---------0.006* Unsocial --------- *p<0.01

63. 63 Experiments and Results Causal 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. 64 Experiments and Results Causal 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. 65 Experiments and Results Causal relationship analysis – communication relationships GLCN vs. drop out rate － BKD Pattern 輟學未輟學 Unresponsive0.3380.662 Unsocial0.8240.176 Dominant0.1150.885 Tete-a-tete0.1870.813 Fragmented0.1070.893 Ideal0.0260.974  “ 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. 66 Experiments and Results Causal relationship analysis – communication relationships GLCN vs. resource sharing frequency － BKD  Resource sharing frequency was influenced by  Communication Pattern  “sub_group”  “Leader”  “Student”  “Leaf & Center”

67. 67 Experiments and Results Causal relationship analysis – communication relationships GLCN vs. group project grade － BKD 小組成績與 communication relationships 無明顯相關性

68. Experiments and Results Causal relationship analysis – communication relationships bridge 個數 1: : :... 普通學生 -> 普通學生 _ 的討論流量 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: 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 4: B (8.0/3.0) PATTERN = ”tete-a-tete”: :...bridge 個數 2: :... 普通學生 -> 普通學生 _ 的討論流量 普通學生 _ 的討論流量 > 1: D (2.0) 因素的重要性大致為： 1.Leaf & Center 2.Pattern 、 Sub_group 3.Student

69. 69 Experiments and Results Causal 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%

70. 70 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

71. 71 Experiments and Results Causal 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 Opinion giver CoordinatorEnergizerProcedural technician and recorder Encourager harmoni zer playboyDominatorFellow- traveler Count65532016265676919613532133 Ratio9.20%7.50%28.47%8.78%9.20%9.49%9.77%27.76%19.12%4.53%18.84%

72. 72 Experiments and Results Causal 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-traveler0.122 * p < 0.01n=706

73. 73 Experiments and Results Causal 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-traveler0.000* * p < 0.01n=706

74. 74 Experiments and Results Causal 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 - giver0.185 coordinator 0.000* energizer 0.000* Procedural technician and recorder0.011 encourager 0.000* harmonizer0.018 Playboy 0.008* dominator 0.000* Fellow-traveler 0.003* * p < 0.01n=70

75. 75 Experiments and Results Causal 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 - giver0.063 coordinator 0.000* energizer 0.000* Procedural technician and recorder0.023 encourager 0.000* harmonizer0.046 Playboy 0.008* dominator 0.000* Fellow-traveler 0.003* * p < 0.01 n=70

76. 76 Experiments and Results Causal relationship analysis – member-roles Factor analysis into 2 role groups Linear regression analysis on group project grade Roles group1Dominator,Encourager, information-giver,Playboy, Initiator-contributor,Coordinator,Opinion giver Roles group2Fellow-traveler, Follower, Energizer, Procedural technician and recorder

77. 77 Experiments and Results Causal relationship analysis – member-roles Linear regression analysis on group drop out rate Roles group1Dominator,Encourager, information-giver,Playboy, Initiator-contributor,Coordinator,Opinion giver Roles group2Fellow-traveler, Follower, Energizer, Procedural technician and recorder

78. 78 Experiments and Results Causal relationship analysis – member-roles BBN analysis on individual grades Roles group1Dominator,Encourager, information-giver,Playboy, Initiator-contributor,Coordinator,Opinion giver Roles group2Fellow-traveler, Follower, Energizer, Procedural technician and recorder

79. 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. 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 ”

81. 81 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

82. 82 Papers Journal Papers Gwo-Dong Chen, Kuo-Liang Ou, Chen-Chung Liu and Baw-Jhiune Liu Intervention and strategy analysis for web group-learning Journal of Computer Assisted Learning (JCAL), Vol. 17(1), 58-71, 2001. (SSCI) Gwo-Dong Chen, Chen-Chung Liu, Kuo-Liang Ou, and Baw-Jhiune Liu Discovering 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 Lin Web learning portfolios: a tool for supporting performance awareness Innovations 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 Horng Managing 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)

83. 83 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 learning Submitted to Journal of Educational Computing Research (JECR), (SSCI/SCI) Gwo-Dong Chen, Kuo-Liang Ou, and Chin-Yeh Wang Using 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 Wang Using groups ’ social interaction to detect the member roles and discover the influence on group learning performance Prepared for submitting to Human and Computer Interaction (HCI)

84. Thank you very much 懇請指教 klou@db.csie.ncu.edu.tw