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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 PowerPoint Presentation
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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

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

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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

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  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. 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) Web Group learning behaviors Web Group communication relationships PROMOTIVEINTERACTION SOCIAL SKILLS OUTCOME Task, role, and resource interdependence Goal and Reward interdependence POSITIVEINTERDEPENDENCE

  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 Group interactions Students Students Learning behaviors Learning behaviors Web Server Learning performance

  7. Introduction Research Goals • Extractthe 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 Monitor Group interactions Monitor Monitor Students Students Learning behaviors Learning behaviors Web Server Learning performance Extract 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. Resource sharing module Task assignment module Group Portfolio module Scheduler / Calendar module Heterogeneous grouping module On-line monitor modules Assessment modules Asynchronous communication module Synchronous communication module Email / on-line notification module System overview System architecture • System overview Students Interaction modules Project modules Teachers Monitor and management modules

  15. System overview System architecture • The tools for assisting teachers to monitor and promote groups to learn on web group learning Group learning interaction in web logs Group learning behavior in web logs Group Learning status extractor Resource sharing frequency Individual grades Learning features Communication relationships Project grades Drop out rate On-line notification Learning status on-line monitor Group learning performance The causal relationships between learning status and learning performance supports teachers on-line promoting groups to learn The relationships between learning status and learning performance extractor Group profiles

  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 Machine learning Computer science techniques Information retrieval Data mining Social network Group learning Social science theories Group learning monitor Educational theories Positive social interdependence Role theory

  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 Group learning status & group learning performance p >= 0.01 significant Abort analyzing p < 0.01 The causal relationships between group learning features on group learning performance Data mining and machine learning techniques for evaluating the causal relationship between group learning status and 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 From communication relationships monitor • Learning behavior on-line monitor Topics and abstracts Students Teachers Group learning behavior in web logs Member-roles extractor Member-roles Feature space and member-roles on-line monitor Feature space generator Learning features

  22. Teachers’ view Data view Constructing the group learning features spaces • Group learning feature space Level-3 Fellow-traveler AND Login frequently Seldom reply in discussion Prefer reading in discussion Level-2 … Reply count Login count Read count … Level-1 Low-level learning feature query and filter Learning behaviors in web logs

  23. Feature id Feature id Feature id Feature Name Feature Name Feature Name Combination Total Degrees Combination id Operations Range Degree Abnormal Abnormal Abnormal 1.1 2.1 3.1 Login count Login frequently Fellow-traveler 2.1,2.4,2.5 5 1.1 AND, AND 0 –MAX 5 -- N Y 2.2 Login seldom 1.1 1 Y … 1.2 Homework grades 5 0 –100 -- 2.3 Homework success 1.2 5 N 1.3 Gender 2 1 – 2 -- 2.4 Seldom reply in discussion 1.4 1 Y 1.4 Reply count 5 0 –MAX -- 2.5 Prefer reading in discussion 1.5 5 N 1.5 Read count 5 0 –MAX -- … … … … … … … … … Constructing the group learning features spaces • Group learning feature space 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 Communication patterns Students Teachers Communication relationships on-line monitor Sub-group Communication patterns Communication relationships analyzer Group learning communicaitons in web logs Graph elements Interaction content analyzer Topics and abstracts To learning behavior monitor

  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. 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 … … Communication network analysis • Ranking score of topic extracting

  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. 2-in-star Reciprocal 2-out-star Transitive 2-mixed-star Cyclic 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)

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

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

  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 <=20 2.6/1.2 C 19.4/8.0 Leaf number D A 2.8/0.8 >20 <=0 <=1 Flow- minimum Cut-point number >0 >15 >1 <=3 B 4.4 2-in-star Leader_Flow E 2.7 >3 <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. Feedback Topics Abstract Good 73.0 % 96.6 % 55.0 % 73.3 % Acceptable 23.6 % 18.3 % Mistake 3.3 % 26.67 % 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:

  46. GLCN pattern Source of Variance SS unresponsive df dominant leader MS F tete-a-tete cliquish ideal unsocial Mean Between groups 19375.55 71.69187 5 75.74435 3875.11 16.57* 68.54046 74.04825 76.85906 38.29603 Within groups(errors) SD 14970.21 10.18473 64 8.591956 233.9096 8.506688 10.00014 11.85958 22.75898 Count (n) 5 18 11 10 3 23 Experiments and ResultsCommunication relationships analysis • ANOVA analysis for significant difference among patterns *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 Total 52 attributes, 70 groups