This document proposes an adaptive framework for tracking user activity and attention metadata in web-based learning environments. The framework includes a uniform model for representing attention data, an open and distributed architecture, and services for managing the model and tracking attention instances. It was developed to enable personalization of learning experiences based on collecting and sharing user attention data across different learning systems and applications.

1. An adaptative framework for tracking Web-based Learning Environments Valentin Butoianu, Phillipe Vidal, Julien Broisin Institut de Recherche en Informatique de Toulouse, France {butoianu, vidal, broisin}@irit.fr

2. Context Mass personalization Students: adapt learning scenarios to each user Instructors and designers: adapt their courseware to various users’ learning paths => Need to collect a large amount of attention metadata from Web-based Learning Environments

3. Research issues Existing tracking systems Students’ online activities within LMS Discussion forums Chat, instant messaging, … Main drawbacks: Specificity Attention metadata enclosed into systems being traced  Prevents attention share, thus mass personalization Uniform representation of attention metadata An architecture promoting the share and reuse of attention metadata

4. Plan 1.Uniform representation 2. An opened and distributed architecture 3.Experimentation with Learning Objects 4.Conclusions and future works

5. Our approach Model based approach to represent attention metadata produced by various learning systems High level of abstraction Extensible Reuse of WBEM, a « de facto » standard proposed by DMTF Dedicated to the network, systems and applications management.

6. Modeling systems and resources

7. Modeling users’ activities

8. Plan 1.Uniform representation 2. An opened and distributed architecture 3.Experimentation with Learning Objects 4.Conclusions and future works

9. Architecture LEARNING ENVIRONMENT TRACKING ENVIRONMENT MIDDLEWARE SOAP HTTP TRACKING REPOSITORY TRACKING MANAGER Learning Application 1 Learning Application 2 Learning Application 3 Management Application SOAP HTTP SOAP HTTP SOAP INT AGENT AGENT AGENT SOAP INT SOAP INT SOAP INT TRACKING SERVICE WBEM INTERFACE SOAP INTERFACE MODEL MANAGEMENT SERVICE SOAP INTERFACE WBEM INTERFACE

10. Model management service Dedicated to attention classes Easy specialization of generic models to represent any attention data Define new classes and/or properties Modify or delete existing classes and/or properties Rules: Apply to classes inheriting only from the generic models Deny modifications of the generic models

11. Tracking service Dedicated to attention instances SetTrace method: self-adapt to the evolution of the model by creating a XML Schema conform to the model. GetTrace method: explores the tracking repository in order to get an unified view of all attention metadata This methods are self-adapting to the attention model

12. Plan 1.Uniform representation 2. An opened and distributed architecture 3.Experimentation with Learning Objects 4.Conclusions and future works

13. Modeling specific systems and resources

14. Learning objects’ activities

15. Specific architecture LEARNING ENVIRONMENT TRACKING ENVIRONMENT Management Application MIDDLEWARE SOAP HTTP SOAP INT AGENT AGENT SOAP INT SOAP INT Learning Application 1 Learning Application 2 TRACKING REPOSITORY TRACKING MANAGER SOAP HTTP SOAP HTTP TRACKING SERVICE WBEM INTERFACE SOAP INTERFACE MODEL MANAGEMENT SERVICE SOAP INTERFACE WBEM INTERFACE OPEN PEGASUS gSOAP

16. Use case: Consultation of a LO from Moodle Moodle Tracking service Tracking manager Tracking repository 1.Request model classes 2.Return model classes 3.Build XML Schema 4.Consult document 5.Build XML trace 6.Send XML trace 7.Validate XML trace 8.Build CIM Instances 9.Send CIM Instances 10.Insert instances into repository

17. Plan 1.Uniform representation 2. An opened and distributed architecture 3.Experimentation with Learning Objects 4.Conclusions and future works

18. Conclusions and future works A framework to manage attention metadata Integrated within ubiquitous operating systems (Windows XP, Linux) Easy to satisfy multiple objectives: model management service Facilitates share and reuse : Externalisation of attention metadata Self - adapting tracking service Automated update of agents according to the model Use Manager to Manager communication Exploit the collected traces: intelligent tutoring systems, recommendation systems

19. Questions