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An adaptive Multi-Agent based Architecture for Engineering Education
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An adaptive Multi-Agent based Architecture for Engineering Education

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  • A la representación lógica se la lee así: "x" tal que "x" es una asignatura, es decir cualquier valor que x puede tomar de un conjunto de  dominio, es una asignatura,  "y" tal que "y" es un contenido, es decir cualquier valor que y puede tomar de un conjunto de  dominio, es un contenido, El conjunto AC es la relación entre A y C y  {y/y define una asignatura}   quiere decir que si "x"es una asignatura y "y" es  un contenido entonces "y" define a "x", o "y" define a una asignatura..
  • Falta un contexto del proyecto y un diagrama de contexto con Moodle. Habría que comentar qué objetivos tenemos antes de poner esta transparencia
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An adaptive Multi-Agent based Architecture for Engineering Education An adaptive Multi-Agent based Architecture for Engineering Education Presentation Transcript

  • An adaptive Multi-Agent based Architecture for Engineering Education Dunia Inés Jara, Paola Sarango Lapo, Miguel Rodríguez Artacho UTPL, Loja (Ecuador) UNED University, Madrid (Spain) http://www.utpl.edu.ec
  • An adaptive Multi-Agent based Architecture for Engineering Education ∗ Introduction ∗ Adaptative logical architecture proposed for moodle ∗ Tutor Module ∗ Knowledge Base Module ∗ Student Module ∗ Interface Module for Users ∗ Future projects ∗ Conclusions
  • Introduction• VLE – Moodle ▫ Open source with tendency to an Adaptative Educational System (AES) ▫ Moodle is based on three main components:  The professor, the classroom, the student Classroom Database Guidelines Interface Activities Interface Resources Teacher Student• The proposed architecture is based on the main areas of adaptation defined in Brusilovsky (1996), providing presentation and navigation adaptation using intelligent agents associated to different modules in Moodle.
  • Objectives∗ Adaptive Navigational support ∗ Ex. Links ∗ The better next ∗ Link hidding∗ Adaptive collaboration suport ∗ Group creation ∗ Automatic share of information∗ Presentation support ∗ Prerrequisite of a given task ∗ Tool sorting (according to priorities)
  • Tutor ModuleSupported by the instructional designThe tutor modeling agent has been designed to perform the following functions:•Didactical-Pedagogical. (teaching style)•Tutor Modeling. (implementation of contents)
  • Knowledge Base Module• Initial knowledge of the system, expressed in inference rules or probability distributions, these are used by the agent to infer a conclusion or new knowledge, used various information sources. aps taxo -Courses p tual m n omie conce s -Enrolled students student’s perception -Virtual library environme personal nt data -LO repository data Agents information interaction on actions data instructional design the sau ies r us olog ont
  • Knowledge base inference Axiom Description Concepts Relationship Logic representation A={x/x is an area} C={y/y is a degree} One area contains one AC = {x/y y>=1} Area/Degree of more degrees Area has A={x/x is a subject} C={y/y is a content} A subject is defined by AC={y/y defines a subject } Subject one or more contents Subject StudyThe set AC is a relationship between A and C and {y/y defines a subject} is “x is a subject and ‘y’ is acontent then “y” defines an “x”, that is to say “y” defines a subject
  • Student ModuleThe agent for studentmodelingperforms some functions:•Creation of StudentModels.•User informationUpdate.
  • Intelligent Agent for Student Modeling Multiagent System Agent1: Monitoring Agent VLE Agent 2: Student Modeling Agent Access to Resources and activities Collection the Interaction data Task Algorithm Interaction level in the course C4.5 (Decission tree generation) Interaction level with the Bayesian Net resource Interaction level in the VLE J48 www.cs.waikato.ac.nz/ml/weka/
  • ADAPTATIVE LOGICAL ARCHITECTURE PROPOSED FOR MOODLE -Competencies - Assesment - User (student) tracking - Institutional approach
  • Interface Module for UsersIt shows all the information to the students, trying to capture theirattention and keeping them motivated, through redaction ofmessages type “Inverted Pyramids” .The objective of this agent is to determine the best interfaceto be offered to each user based on the hardware andsoftware used for the connection.
  • Intelligent Agent for making instructional decisions (ToDei)The objective of this intelligent agent is to fulfill these functions as well as to transmitthe content to the user.Furthermore, considering the characteristics and greatest needs, it decides the bestway to offer information generated in this process
  • CONCLUSIONS• Different Inference approaches for different domains (tutor, student, navigation, interface,..)• Use of the Moodle information model to track actors activity• Evaluation not developed, but tracking is persistent• The ToDei agent constitutes the main component inside this architecture since it allows visualization of the adaptive effect generated by the interaction of the components.• Work in progress. Main development is knowledge base interface information model and inference system
  • An adaptive Multi-Agent based Architecture for Engineering Education Dunia Inés Jara, Paola Sarango Lapo, Miguel Rodríguez Artacho UTPL, Loja (Ecuador) UNED University, Madrid (Spain) http://www.utpl.edu.ec