2. Elisa Benetti, Lepida SpA, Italy Cristina De Castro, IEIIT-CNR, National Research Council of Italy

3. Introduction Aim Traditional training VS proposed method The proposed adaptive training architecture Logical flow example

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6. INTERACTIVE TEACHING ADAPTIVE TESTING ADAPTIVE LEARNING

7. CATEGORY SELECTION

10. Authentication and Group Selection New Task Selection Learning Phase Adaptive Testing Petri Net On-line Tutoring On-Line Tasks Tasks GroupN Scores and levels achieved Centralized DB OFF-LINE SERVICE ON-LINE SERVICE SUPPLIER

11. LDAP(Tasks)LDAP(Tasks)DBMS (Training Material) DBMS SERVICE (User) SERVICE (Provider) CENTRALIZEDDBMS (Error tracking) CENTRALIZEDDBMS

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13. Click Upload Choose File Click Start Upload Modify Description Click Save Delete File LEVEL 0 TASKS LEVEL 1 TASKS Click File List

14. INPUT P1: level 1 task P3: learning phase P13: return to level 1 task P2:click help (user action) P7: click file list (user action) P8:modify descriptin (user action) P9: click save (user action) P10: click delete (user action)

15. OUTPUT P11: final level reached P12: current POSITIVE score (coming from task) P14: current NEGATIVE score (coming from help)

16. Level 1 task: modify a file description

17. User can’t find Files list and asks for help

18. Level 1 task: modify a file description Learning materials are presented to the user

19. Level 1 task: modify a file description User is redirected to a level 1 task with a negative score of 3

20. Level 1 task: delete a file

21. User finds the list of file

22. Level 1 task: delete a file User correctly deletes the file

23. Level 1 task: delete a file User achieves a positive score of 10 and COULD go to level 2

24. CONDITION 1: the total score achieved (P12-P14) is sufficient CONDITION 2: user could go to the next level (P11)

25. Adaptive training is a new approach to web- based services training Users are divided into main categories An off-line training phase is mandatory to handle with the real service in the next phase The training system autonomously upgrades itself, using frequent errors to propose new tasks

26. Thank you for your kind attention Thank you for your kind attention elisa.benetti@lepida.it cristina.decastro@ieiit.cnr.it