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

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

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INTERACTIVE TEACHING ADAPTIVE TESTING ADAPTIVE LEARNING

CATEGORY SELECTION

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

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

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

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)

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

Level 1 task: modify a file description

User can’t find Files list and asks for help

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

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

Level 1 task: delete a file

User finds the list of file

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

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

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

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

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