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Pedagocical Agents

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  • 1. Pedagogical agents T he experience of Consorzio FOR.COM. Mikail Feituri ICT manager Consorzio FOR.COM. Rome, 23 October 2008
  • 2.
    • software elements being responsible for carrying out given tasks by means of artificial intelligence techniques
    • Conceptually, the agents implement a metaphor being common to the typical way of operating in the market:
      • visiting a place,
      • using a service (possibly following a negotiation)
      • then moving elsewhere.
      • After the agent has gathered the results wished, it goes back to the user.
    Intelligent agent
  • 3.
    • Definition:
    • particular type of intelligent agent
    • actual virtual tutor accompanying the student of the educational system during the learning process
    • Features:
    • always visible to the user within the educational milieu
    • human (or humanoid) forms
    • Interacts with the user both verbally and non verbally
    • It moves and interacts directly with the learning milieu and within the milieu itself.
    Pedagogical agent
  • 4. Parmenide project
    • Goals:
    • Two pilot applications for training of operators employed in the transport sector in the anti firing security field.
    • Features of the applications:
    • Innovative assessment tool
    • Extremely stimulating scenarios for the students
    • The virtual tutor simulates a teacher who submits an exam to a teacher
    Platform
  • 5. BEHIND THE PILOT APPLICATION
    • The pilot application starts on choosing randomly an important question among those available.
    • Our expert in anti firing security has defined which questions have to be considered as important.
    • Another parameter, which has been considered, is the difficulty of the question.
  • 6.
    • The system works with 3 Fuzzy Logic inference system (FIS).
    • Fuzzy Logic, with its linguistic rules, simulates human behaviour. In fact, it translates human behaviour based on natural language syntax in an artificial language suitable for computers.
    FUZZY LOGIC
  • 7. First Fuzzy inference engine FIS 1 Importance Difficulty Fastness Correct / Incorrect Knowledge depth It defines a learning path for the student
  • 8.
    • The knowledge depth is the degree of user knowledge about the topic
    • Depending on the quality of the user answer, the system provides again another important question or any other question.
    • The system behaves like a normal teacher
    • In the pilot application the minimum question numbers is 3 and the maximum is 5
    Knowledge depth
  • 9. It provides the score carried out by a user when he / she answered a question. Second Fuzzy inference engine FIS 2 Importance Difficulty Fastness Correct / Incorrect Score
  • 10. It defines the verbal and non verbal tutor behaviour FIS 3 Cumulative score Knowledge Depth (if answer is right) Verbal and non verbal behaviour (facial expressions) Score (if answer is wrong) Third Fuzzy inference engine
  • 11.
    • More than 100 verbal feedback are stored in the database.
    • This messages are classified from very negative to very positive.
    • The tutor decides which one to supply from the third fuzzy engine output.
    Verbal behaviour
  • 12.
    • The tutor is able to provide 11 different facial expressions
    • The tutor puts on a neutral expression when he reads the questions and she provides the didactic pills.
    • The tutor decides which one to supply from the third fuzzy engine output.
    Non Verbal behaviour
  • 13.
    • We tried to avoid virtual tutor behaviours which can be classified as unstable.
    • For this aim, we considered the user performance carried out in all the questions and not just in the last question answered.
    • On doing this, we tried to simulate the behaviour of a normal teacher who submits an exam to a student.
    Non Hysterical behaviour
  • 14. Remarks and improvements
    • The number of questions is very limited because this is a pilot application for testing new didactic methods.
    • Only multiple choice questionnaire for each scenario has been used because of the particularity of the didactic topic
    • Among other sectors, more complex and various scenarios could be used.
  • 15. Looking ahead: T 2 project
    • T 2 adapts and transfers the pedagogical and didactic model developed in PARMENIDE in the field of microfinance
    • The aim is to apply the “PARMENIDE model” to a comprehensive and already produced E-course
  • 16. Looking ahead: COACH BOT project
    • COACH BOT is a pilot project that aims essentially to develop an intelligent tutor
    • Like a real tutor, the pedagogical agent will provide help, suggestions on the lessons, in-depth information, ...
    • For this, the development should be focus on the agent’s dialogue capacity with the student
    • The artificial intelligent techniques to be used will be probably rather different from the ones developed for PARMENIDE.
  • 17.
    • Thank you!
      • Mikail Feituri
      • FOR.COM. Interuniversity Consortium
      • Rome – Italy
      • [email_address]
      • +39 06 37725542