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Applying data mining techniques to e-learning problem

Applying data mining techniques to e-learning problem






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    Applying data mining techniques to e-learning problem Applying data mining techniques to e-learning problem Presentation Transcript

    • Applying Data Mining Techniques to e-learning Problem Farshad Badie Computer Science M.Sc. farshadbadie@gmail.com University of Debrecen
    • Survey of Data Mining in Learning1. From the Data Mining point of view2. From the e-learning point of view
    • From the Data Mining point of view  Where does Data Mining fit in e-learning processes? (Refer to KDD)There is a close relation between the fields of AI & ML (Main sources of methods and techniques of DM) AND Education Processes
    • General view of Knowledge Discovery
    • The research opportunities in AI and educationModels as scientific tool Means for understanding some aspect of an educational situationModels as component Characteristic of teaching or learning processModels as basis for design of Artificial Education
    • The classification problem in e- learning We usually aim to model the existing relationship (if any) between a set of multivariate data items and a certain set of outcomes for each of them in the from of class membership levels. We will see some of them …
    • Fuzzy LogicMethods
    • Neuro Fuzzy ModelEvaluating the students in an Intelligent Tutoring System DM based on Fuzzy Theory, measures and transformsthe interaction between student and the ITS into linguistic term
    • Fuzzy Group-Decision ApproachAssisting users and domain experts in the evaluation of educational web sites
    • Fuzzy Rule-base MethodFor Monitoring educational webservices DOES Knowledge Integration Will use it as the basis for the design of an Intelligent Management System (Pat.Dis) Conclusion: The system is capable of predicting and handling possible failures of Educational Web Servers ( Improving STABILITY and RELIABILITY)
    • Two-Phase Fuzzy PHASE 1 PHASE 2Integrates an association Rule Mining algorithm Uses an inductive called Apriori learning AlgorithmWill find the information The results couldthat could be fed back also be fed back to to teachers for teachers for reorganization reorganization
    • The clustering problem in e- learning Unlike in classification problem, in data grouping (Clustering), we are not interested in modeling a relation between a set of multivariate data items and a certain set of outcomes We usually aim to discover and model the groups in which the data items are often clustered, according to some item similarity measure
    • Other Data Mining Problems in e- learning Prediction Techniques The interesting intersection with e- learning.It can easily overlap with Classification and Regression Problems. The forecasting of students’ behavior and performance in learning environments
    • Visualization Techniques “Data Exploration through Visualization methods”e.g. Intersection of Social Network Analysis & Distance Learning Atypical Student behavior description Virtual Tutors of e-learning environment
    • DM
    • And bringsOntological and Semantical tools for learning(e.g. Semantic Web)
    • Meeting of DM /e-learning /StatisticsGoal: Discovery andextraction of knowledge froman e-learning DB to supportthe analysis of studentlearning processes Evaluation of effectiveness and usability of courses
    • From the e-learning point of view
    • TABLE 1 Students’ LearningASSESSMEN T
    • TABLE 1 Students’ LearningASSESSMENT
    • TABLE 2 DATA MING Applications Providing An EVALUATION ofthe learning material
    • TABLE 3DATA MINING Applications Providing feedback to e-learning actors (“students” / “tutors” / “educational managers”)
    • ----------------- TABLE 4 REALDATA-MINING BASEDE-LEARNING PROJECTS------------------
    • Thank you!