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Gabriela Moise, Monica Vladoiu, Zoran Constantinescu

GC-MAS - a Multiagent System for Building Creative Groups used in Computer Supported Collaborative Learning. Gabriela Moise, Monica Vladoiu, Zoran Constantinescu . Subject. method for building creative teams, based on unsupervised learning and with support from a multiagent system

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Gabriela Moise, Monica Vladoiu, Zoran Constantinescu

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  1. GC-MAS - a Multiagent System for Building Creative Groups used in Computer Supported Collaborative Learning Gabriela Moise, Monica Vladoiu, Zoran Constantinescu

  2. Subject • method for building creative teams, based on unsupervised learning and with support from a multiagent system • first experiments on grouping learners involved in online brainstorming

  3. Computer Supported Collaborative Learning (CSCL) • has appeared as a reaction to software used previously in learning, which have been forcing learners to study and learn as isolated individuals • in CSCL, learning is obtained by computer-supported interactions both between learners and between learners and teachers

  4. GC-MAS - A Multiagent System for Building Creative Groups

  5. The Communication Agent (CommGC): • interfacing with the users and with the agents • managing the activities of the other agents • The Creative Groups’ Builder (BuildGC): • construction of the creative groups (unsupervised learning algorithm, classification techniques) • The Creativity Evaluation Agent (EvalGC): • support for assessment of group creativity • The Creativity Booster (EnvrGC): • stimulates creative contextual environments that provide for increasing group creativity

  6. The Glue Role Agent (GlueGC): • seeking out and taking on otherwise neglected tasks that have potential to facilitate creative group performances • The Facilitator Agent (FCL-GC): • supports the facilitator in helping groups to interact more efficiently • The Team Relational Support (TRS-GC): • supports the team members in providing support for the other group members

  7. BuildGC • construction and iterative refinement of creative groups taking into account • the components that generate creativity • their interdependencies that have effect on creativity • the purpose of building of creative groups (to solve a problem, to complete a task etc.) • The current reasoning process is based on an adapted version of the Q-learning algorithm

  8. In brief, this algorithm is a reward learning algorithm that starts with an initial estimate Q(s, a) for each pair <state, action>. When a certain action a is chosen in a state s, the system (BuildCG) gets a reward R(s, a) and the next state of the system is acknowledged

  9. in our case, we tackle n students for each student, a characteristic vector that includes m individual features is constructed, namely (c1, c2, …, cm) a state consists of this vector and the group number, while an action refers to moving a student to another group Q expresses the quality of association between a state and an action our goal is to build the most creative k groups (k being given)

  10. GC-Q-learning adapted algorithm build a bi-dimensional matrix Q for all the possible pairs <state, action>. The columns of this matrix consists of (c1, c2, …, cm, no_group, action_number, q) initialize the optim_policy (in our case is the optimal grouping) with a guided policy, and Q_optimal with Q

  11. GC-Q-learning adapted algorithm group the students and undertake working sessions (in our first experiments, online brainstorming), in which the group creativity is assessed and its score is assigned to R(s,a) – using EvalGC. For each such working session, the matrix Q is calculated analyze matrix Q. The optimal policy is given by the action for which Q_optimal gets the maximum value

  12. GC-Q-learning adapted algorithm once the optimal policy consisting in tuples(c1, c2, …, cm, group number) is obtained, predictions for each set of data can be made based on advanced classification techniques (Bayesian networks, neural networks etc.) the Q values are the same for all the members of a group

  13. Sample data • Our goal: to group in increasingly creative groups 12 students having the (Gough, motivation) as follows: • 3 students with (3,1) • 4 students with (3,2) • 2 students with (2,1) • 1 students with (1,1) • 2 students with (4,1)

  14. Sample data

  15. Sample data - interpretation • A student with (3,1) would be the most creative if s/he would be in group 7, and decreasing - in group 1, 6, 5 or 9

  16. Sample data - interpretation • A student with (3,2) would be the most creative if s/he would be in group 8, and decreasing - in group 4, 3, 9, 2 or 1

  17. Conclusions and future work • we introduced here our semi-automated method of grouping team members in increasingly creative groups • future work: corroborating the results obtained with several creativity evaluation scales, assessment of creativity before and after activities assumed to help trigger creativity, inclusion of contextual and organizational factors, testing the method in other activities, improving of the algorithm, offering the method as an online open service

  18. Thank you! 

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