1. Semantic Web in Group Formation Asma Ounnas Learning Societies Lab School of Electronics and Computer Science The University of Southampton, UK www.ecs.soton.ac.uk/~ao05r ao05r@ecs.soton.ac.uk

2. Group Formation in e-learning g1g1 g2g2 gNgN Formation in terms of the constraints + Collaboration Goals as a set of constraints Students Instructor Groups Reasoning Web Summer School, Dresden, 07/09/2007 International female leader I want David to be supervise my group

3. We want Web-based group formation in e-learning Personalize the allocation of students Give a degree of freedom for choosing constraints for the formation Enable the formation of different types of groups Reasoning Web Summer School, Dresden, 07/09/2007

4. Potential of the Semantic Web Used in Personalization of Learning Objects Used in Social networks Used in CoPs and Expert Finders Reasoning Web Summer School, Dresden, 07/09/2007

5. Constraint-based Group Formation The allocation of participants to groups based on some constraints Collaboration task has a set of goals, each is a set of constraints Each constraint has a value Maximize the utility of all constraints within all goals and minimize the deviation of the groups satisfaction => Optimal formation Reasoning Web Summer School, Dresden, 07/09/2007

6. Assumptions non-overlapping group formation all groups have a similar size All formed groups are stable while the formation is not announced by the instructor Reasoning Web Summer School, Dresden, 07/09/2007

7. Hypothesis Semantic Web technologies can effectively automate the process of Web- based constrained group formation in the e-learning domain. Reasoning Web Summer School, Dresden, 07/09/2007

8. Group Formation Process 1.Initiating the formation: the instructor sets the constraints and the students list; 2.Identifying the members: analysing the students profiles to identify who should be in which group, the descriptions of the students 3.Negotiating the formation: the negotiation of the students allocations to groups involves running a specific algorithm that can satisfy the constraints. => Modeling + Constraint Satisfaction

9. Modeling Variables Task-related: experience, education level, knowledge, skills, abilities (cognitive and physical), grades, interests, preferences of topics and experts. Relation-oriented: gender, age, culture (race, ethnicity, national origin), social status, personality and behavioral style, social ties, trust. Context-related: geographical location, availability schedules, and communication tools. Reasoning Web Summer School, Dresden, 07/09/2007

10. Modeling For Group Formation Group TypeTask-relatedRelation-oriented TeamsAll variables CommunitiesInterests, topic preferences, experience, expert preferences Expertise relationship, trust Intensional Networks Skills, abilities, experienceNone Social NetworksInterests, topic preferencesSocial ties, trust Reasoning Web Summer School, Dresden, 07/09/2007

11. Implementation Reasoning Web Summer School, Dresden, 07/09/2007 Groups list Jena persistent storage (ontology instances) Students Interface (Extended foaf-a-matic) Students Profiles (SLP ontology) Group Generator (inference rules) Group Formation Algorithms Instructor Interface Instructor Constraints Instructor Students

12. Semantic Learner Profile Ontology Based on FOAF Added 14 classes and 34 properties Uses the University of Southampton CS modules ontology Uses the Trust ontology Domain ontologies can be added Reasoning Web Summer School, Dresden, 07/09/2007

13. Semantic Learner Profile Ontology <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:slp="http://www.ecs.soton.ac.uk/~ao05r/LearnerProfile/slp.owl"> Asma Ounnas Female leader Arabic Web Technologies Knowledge Technologies e-learning Semantic Web Hugh Davis Ilaria Liccardi Reasoning Web Summer School, Dresden, 07/09/2007

14. Student User Interface Reasoning Web Summer School, Dresden, 07/09/2007

15. Instructor User Interface

16. Group Generator CSP solvers to get the optimal formation data mining module for incomplete data Deduction rules eg. IF John is a captain of the football team, THEN John is a leader IF Sarah has a high grade in discrete mathematics AND Sarah has a high grade in Logic for CS, THEN Sarah will perform well in formal methods Reasoning Web Summer School, Dresden, 07/09/2007

17. Evaluation Metrics framework for evaluating Group formation Quality Using 3 algorithms for group formation with incomplete data: without rules vs with rules. No claim for: Proving that any particular set of constraints leads to better results in terms of the groups performance. Claiming that any particular algorithm leads to best grouping.

18. Feedback Questions Reasoning Web Summer School, Dresden, 07/09/2007 Suggestions Coments