1. Learner Modelling in a Multi-Agent System through Web Services Katerina Kabassi, Maria Virvou Department of Informatics, University of Piraeus

2. Education on IT Computer skills on top of other more traditional domains. In a way that will permit learners to try things out and receive help when necessary. Training in computer labs. A human instructor has to monitor the work of each individual trainee. Intelligent Learning Environments (ILE).

3. Intelligent Learning Environments An ILE can: Monitor users, help them perform their tasks, provide them with feedback in a manner that contributes to their learning process. Effective and efficient learning. Experiences fitting their specific background knowledge and objectives.

4. Learner Modelling Information Technology skills  observable by computers. When users interact with a computer, they provide a great deal of information about themselves. Learner Models. Adaptation of the system’s interaction to each individual user.

5. Agents for Learner Modelling Agents have been quite successful at observing users’ behaviour. In learning environments for: capturing the users’ characteristics performing user modelling tasks Software agents play an important role in HCI in the coordination of the internal processes of the system

6. F-SMILE F-SMILE  File-Store Manipulation Intelligent Learning Environment Multi-agent intelligent learning environment for novice users of a GUI A protected environment for novice users Users can work as they would normally do. The system silently reasons about their actions. The system offers adaptive tutoring and help.

7. Web F-SMILE F-SMILE stored information learner models locally. No computer has a full history record. Web F-SMILE Modelling a large population of individuals Following each one anywhere Learner Modelling Server Separate Model in each client

8. Agents in Web F-SMILE Multiple Agents observing the student while s/he is actively engaged in his/her usual activities providing spontaneous advice in case of an error Learner Modelling (LM) Agent, Advising Agent, Tutoring Agent, Speech-driven Agent.

9. Operation of the system Every time the learner issues a command, the LM Agent reasons about it. In case of an error diagnoses the cause of the learner’s error generates alternative actions sends the alternative actions to the Advising Agent Informs the Tutoring Agent that the user needs tutoring Advising Agent  selects the most appropriate advice. Tutoring Agent  forms an adaptive presentation of the lesson to be taught to the learner Speech-driven Agent  presents the information in a unified and easy to access fashion.

10. Simple example Copy(exam 21-01-2003.doc) Copy(exam 13-09-2003.doc) Second Action  suspect Cannot find alternative action LM Agent  Tutoring Agent Multi-selection of files and folders Then selects both files and pastes them in C:\My Documents\exams\

11. LM Agent Observation of the learners while they are actively engaged in their usual activities, Maintenance and management of the learner profiles. Provision of relevant information whenever other agents request it. Client side.

12. Web Services (WS) Interaction of the LM Agent with the Learner Modelling Server Self-contained, modular applications that provide a set of functionalities. Interaction through web standards such as: WSDL (Web Service Definition Language) SOAP (Simple Object Access Protocol) UDDI (Universal Description, Discovery and Integration) A new model on the Web in which information exchange more conveniently, reliably and easily.

13. Interaction through WS (1) LM Agent sends the username and password of the learner to WS Learner Modelling Server. WS Learner Modelling Server finds the learner model sends this information to the client that requested it Update the learner model that is maintained locally: Information sent by the WS Learner Modelling Server. Information gathered locally.

14. Interaction through WS (2) Information acquired is sent to the Web Service Learner Modelling Server  update the Server learner model Web Services Learner Model keeps track of intentions and possible confusions of each individual user. Information available to the application irrespective of the computer where it is running.

15. Internet Learner Modelling Server Web Service LM Agent (client) Two separate learner models. Check whether the user’s PC is connected to the Internet or not. Online Interaction

16. Learner Model Initialisation If the learner model does not exist on the Web Service Server  the LM Agent initialises the LM. Stereotypes LM Agent sends the information to the WS WS creates a new learner model based on the information that was available from the local learner model.

17. Learner Model Update (1) If the learner model on the Server exists  the LM Agent is responsible for finding the local learner model. If local learner model does not exist  LM Agent makes a copy of the learner model from the Server to the hard disk of the learner’s PC. Otherwise, the LM Agent undertakes the difficult task of updating both models with the latest information.

18. Learner Model Update (2) Learner Model  Summative information Timestamps  registers each learner interaction date-time of the interaction  each registration of the learner model each interaction differentiates from all the others Identification of the interactions of the local learner model that have not been included on the Server and vice versa A flag states whether the interaction has been submitted to the Server or not  reduce network traffic.

19. Adaptive Tutoring Humans pay attention only to information that seems relevant to them. Provide the ‘right’ pieces of information in the ‘right’ way and at the ‘right’ time. Adaptive hypermedia techniques to protect learners from information overflow. Adaptivity the learner's habits, prior knowledge, skills.

20. Adaptive hypermedia Adaptive presentation  content level. Adaptive navigation support  link level. Adaptive presentation techniques examples of use of an unknown command in the context of the learner’s own file-store. Tutoring Agent  dynamic examples so that it may use the names of the particular learner’s existing files and folders. adaptive link annotation techniques  present other parts of knowledge that are believed to be of interest to the learner for the particular case.

21. Conclusions (1) Web F-SMILE  a multi-agent learning environment over the Web that helps users learn how to operate their file store. Focus: the Learner Modelling Agent that is responsible for the personalisation of the tutoring and the advice given to learners. Problem: a learner does not always use the same PC Solution  Web.

22. Conclusions (2) Web F-SMILE keeps one learner model for every learner centrally on the Learner Modelling Server one learner model in each computer Effective interaction through Web Services. Web Services  web standards. Enables the dynamic integration of applications distributed over the Internet, independently of their underlying platforms.