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

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

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

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

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

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

1.Uniform representation

2. An opened and distributed architecture

3.Experimentation with Learning Objects

4.Conclusions and future works

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.

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.

Modeling systems and resources

Modeling users’ activities

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

1.Uniform representation

2. An opened and distributed architecture

3.Experimentation with Learning Objects

4.Conclusions and future works

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

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

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

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

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

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

1.Uniform representation

2. An opened and distributed architecture

3.Experimentation with Learning Objects

4.Conclusions and future works

Modeling specific systems and resources

Learning objects’ activities

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

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

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

1.Uniform representation

2. An opened and distributed architecture

3.Experimentation with Learning Objects

4.Conclusions and future works

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

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

Questions