Presentation on theme: "Measuring the Transfer of Knowledge Skills Constrained-student Modeler Autonomous Agent University of Electro-Communications Graduate School of Information."— Presentation transcript:
Measuring the Transfer of Knowledge Skills Constrained-student Modeler Autonomous Agent University of Electro-Communications Graduate School of Information Systems,Japan Safia Belkada Toshio Okamoto
Outline of the Presentation Problem state Framework Research Purpose The domain knowledge model The tutor agent model Comments about current and future research
Problem State Defect of previous methods in building a learner models for ITSs –Learner model revision Changes of the learner s knowledge are not represented. Information about previous learner s reasoning are lost
Framework We propose an approach of learner modeling that focuses on instruction purpose only.
Research purpose Domain knowledge representation The components behavior process level of the domain knowledge, which is described by relevant/ satisfaction constraints. The help system build as reactive tutor agent The controller of the learning process level that manages the feedback in regard to the learner's goals and the transfer of knowledge skill acquisition.
Student GUI Symbolic and procedural knowledge Objects Library+domain application constraints initialization of the problem state KBNN DataRules Constraints verification pattern matcher Hints rules learner modeler Agent System Control Flow and Building Blocks Model generator Problem solver Knowledge evaluator of CK, MK Student DB
The Transfer of Knowledge Skill Acquisition Model knowledge (Generality) Concept Knowledge procedural Knowledge Learning/discovery Data acquisition exploration Domain application +Generic tools = symbolic knowledge Help activation
Domain Knowledge Representation Components of the domain knowledge = collection of constraints. State constraint => unit Each state => ordered pair. Cr is a cluster relevant constraints and Cs is cluster of satisfaction constraints. Problem state Pi = constraint is relevant. Learning objective Loi = learning goals of the learner Subset of domain knowledge=> DK =,,
Description: Lo= Learning objective Ck ::= Sk symbolic knowledge (Ck1 … Ckn) conjunction of concepts Pk (Sk1….Skn) functional dependency between procedural and conceptual knowledge
Description (continue) Pi can be defined inductively as following: All elements in Cr are action types. If Lo 1 …Lo n are distinct objects in Dk and A 1 …A n are the appropriate instruction set during the design steps, then every expression which conforms to one of the following is a problem state of the form: –[Lo 1 :C r1 =>A 1 …. Lo n :C rn =>A n ]; sub-expressions Lo i :C ri 1
Initialize Constraints analysis Update commitments Tasks Commitments Communication Rulebase React Tutor Agent Model Cr patterns Matches the problem State that matches Cs no yes
Modeling the agents task The agents task is a method that is described as, a.taskModel(,p, ), where: –a is the instance of the agent –p the purpose of the model (generate appropriate feedback, measurement of student's knowledge). – corresponds to the computational constraints.
Modeling Agent's Tasks The agent accomplishes two types of tasks: - Measurement of the student's knowledge. - Difficulty for a particular learning objective. - Dependency between learning objective - Constraints violated - Hint Taken - The generation of the feedback related to the measured knowledge.
Communication Skills The event modeling consists on: identifying an event library for the whole system, determining an object model encapsulating the learner action and problem state. classMethod selector constraints ComponentReference to method Reference to Send-Msg(class, instruction_selector) Initial message receiver object
Commitment Rule Each commitment rule contains a message condition and an action. In order to determine whether such a rule fires, the message condition is matched against the current tasks of the agent. If the rule fires, then the agent becomes committed to the action. The operation of the agent is described by the following loop 1) Read all current messages, updating commitments where necessary; 2) Execute all commitments for the current cycle where the capability condition of the associated action is satisfied; 3) Goto(1)
Update the Commitment Rule –determining which methods are applicable to which components, –defining what effects these methods have on the objects and the corresponding pair of relevant/satisfaction constraint clusters. –identifying the clusters Cr1…Crn of learners problem states in which instructional A1…An. are appropriate, and retrieve hints associated to them. These allow the agent to update its commitment rules.
Conclusion System that need adaptivity without having a runnable learner or the expert models. Focus on computation of knowledge as well as understanding level of the learners, rather that traditional focus on diagnosis and assessment.