[LON-CAPA-cvs] cvs: modules /minaeibi survey3.ppt

minaeibi lon-capa-cvs@mail.lon-capa.org
Thu, 19 Sep 2002 19:02:20 -0000 

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minaeibi		Thu Sep 19 15:02:20 2002 EDT

  Added files:                 
    /modules/minaeibi	survey3.ppt 
  Log:
  final presentation for PhD survey
  
  
--minaeibi1032462140
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Content-Disposition: attachment; filename="minaeibi-20020919150220.txt"


Index: modules/minaeibi/survey3.ppt
+++ modules/minaeibi/survey3.ppt
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                                                                                                          ? L          +   8   S u r v e y ,   L O N - C A P A   D a t a   M i n i n g O          =  ^                 (                0   Data Mining for an Educational Web-based system     1         1    C      *             Ph.D. SurveyAdviser: Dr. Bill PunchSeptember 2002Behrouz MinaeiGenetic Algorithm Research and Application GroupDepartment of Computer Science and EngineeringMichigan State University              P     c      	                c          c     
     c         c         c      z    c                     )                    Topics                   c    .             Problem OverviewLiterature ReviewClassification MethodsNon-tree ClassifiersCombination of ClassifiersDecision Tree-based ClassificationConclusion and Next Steps  T   :         S                          :     a    S     a         a                                       Problem Overview                    c    .          L  This research is a part of the latest online educational system developed at MSU, the Learning Online Network with Computer-Assisted Personalized Approach (LON-CAPA). In LON-CAPA, we are involved with two kinds of large data sets: 1) Educational resources: web pages, demonstrations, simulations, individualized problems, quizzes, and examinations. 2) Information about users who create, modify, assess, or use these resources. Find classes of students. Groups of students use these online resources in a similar way. Predict for any individual student to which class he/she belongs.  ,          P L       P      c          c          g       C     c     D    c           c         c          c         c            c          g             c         c           c         c      ;     c          9          +                 	   LON-CAPA  *   
              c    ,      c    0            LON-CAPA is an open source GNU (GPL) system, built as a geographically distributed network of constantly connected serversprovides instructors with a common, scalable platform to assist in all aspects of teaching a courseAllows instructors to create educational materials and to share such learning resources with colleagues across departments and institutionsDistributed Users can log into any machines and everywhere in the network, access resources under a usernameIndividualized Assessment  $                    c                                       Courses using LON-CAPA at MSU                   c    $                                   f                                   Current Status                   c    &          A  Started October 2000High SchoolsMio AuSable High School - Mio, MichiganCharlotte High School - Charlotte, MichiganFowlerville High School - Fowlerville. MichiganTheodore Roosevelt High School - Wyandotte, MichiganCommunity CollegesWestshore Comm. C. - Scottsville, MichiganTruckee Meadows Comm. C. - Reno, Nevada             P         P f        P     c         B       g      f    B    S   g      f   ,   &                     	       J                   Universities (department level)Ohio University at Athens - Athens, OhioSUNY Stony Brook - Stony Brook, New YorkFlorida State University - Tallahassee, FloridaUniversity of Massachusetts at Amherst - Amherst, MassachusettsMichigan State University, various departments - East Lansing, MichiganOutside United StatesSimon Fraser University - Vancouver, CanadaUniversity of Halle - Halle, GermanyUniversity of Oldenberg - Oldenberg, Germany  V          P      B       g      f    b    ~   g      f   P   {                                  	              	       
                                          Resource Evaluation in Lon-CAPA                     a                Questionnaires, which are completed by faculty and students who use the educational materials to assess the quality and efficacy of resources Increasing demand for automated methods for evaluating resources. One such method is data mining.                   a                                       Literature Review                   a             .  D a t a   M i n i n g   T h e   n o n t r i v i a l   p r o c e s s   o f   i d e n t i f y i n g   v a l i d ,   n o v e l ,   p o t e n t i a l l y   u s e f u l ,   a n d   u l t i m a t e l y   u n d e r s t a n d a b l e   p a t t e r n s   i n   d a t a "   ( F r a w l e y   e t   a l . ,   1 9 9 2 ;   F a y y a d   e t   a l . ,   1 9 9 6 )    D e s c r i p t i v e   T a s k s   ( C l u s t e r i n g )  P r e d i c t i v e   T a s k s   ( C l a s s i f i c a t i o n )  M i x e d   T a s k s  A s s o c i a t i o n   R u l e   D i s c o v e r y :   ( P r o d u c e   d e p e n d e n c y   r u l e s   w h i c h   w i l l   p r e d i c t   o c c u r r e n c e   o f   a n   i t e m   b a s e d   o n   o c c u r r e n c e s   o f   o t h e r   i t e m s )  S e q u e n t i a l   P a t t e r n   D i s c o v e r y :   ( S e q u e n t i a l   d e p e n d e n c i e s   a m o n g   d i f f e r e n t   e v e n t s ,   E v e n t   o c c u r r e n c e s   i n   t h e   p a t t e r n s   a r e   g o v e r n e d   b y   t i m i n g   c o n s t r a i n t s )                      M                     c     w    c           c     )     c          c     M    c         c         c      g    c         c        c         c      t    c          c                                                                                                                    	            
                                                       
                    Classification Methods                   a                               Bayesian Classifier Decision tree-based method Neural Network Approach k-Nearest Neighbor (kNN) Decision Rule Parzen Window classifierGenetic Algorithm	(GA)              ]         1                       a         e            a         e            a         e       C     a         e            a         ^              C                                         Data Set: PHY183 SS02                   a             *   227 students12 Homework sets184 Problems     +         +     a                                      Class Labels (3-ways)                   a                                   %   Distributed Data: Course / Students      &         &     c    &            Course Datals -alF /home/httpd/lonUsers/msu/1/2/6/12679c3ed543a25msul1/-rw-r-----   1 www      users    65516545 Apr 25 16:20 activity.log-rw-r-----   1 www      users       17155 Apr 25 16:20 classlist.db-rw-r--r--   1 www      users       60912 Apr 25 16:20 classlist.hist-rw-r-----   1 www      users      103030 May 15 14:47 nohist_calculatedsheets.db-rw-r-----   1 www      users       13050 May  9 21:04 nohist_expirationdates.db-rw-r--r--   1 www      users           6 Jan  4 16:40 passwdStudent Data                                   c      t     c         c      7     c         c      (    c         c           c                                      7              @              2                            /                            2                            2                            /                                                     +    Preprocessing and Extracting the Features     ,         ,     c    $          *                             *                 Preprocessing student database Course data Activity LogOriginal Stored Data1007070627:msul1:1007070573%3a%2fres%2fadm%2fpages%2fgrds%2egif%3aminaeibi%3amsu%261007070573%3a%2fres%2fadm%2fpages%2fstat%2egif%3aminaeibi%3amsu%261007070574%3amsu%2fmmp%2flabquiz%2flabquiz%2esequence___1___msu%2fmmp%2flabquiz%2fnewclass%2ehtml%3aminaeibi%3amsu%261007070589%3amsu%2fmmp%2flabquiz%2flabquiz%2esequence___5___msu%2fmmp%2flabquiz%2fproblems%2fquiz2part2%2eproblem%3aminaeibi%3amsu%261007070606%3a%2fadm%2fflip%3aminaeibi%3amsu%261007070620%3a%2fadm%2fflip%3aminaeibi%3amsu%261007070627%3a%2fres%2fadm%2fpages%2fs%2egif%3aminaeibi%3amsu%261007070627%3a%2fadm%2flogout%3aminaeibi%3amsuPassed over some Perl Modules 1007070573 /res/adm/pages/grds.gif minaeibi /res/adm/pages/stat.gif1007670091 /res/adm/pages/grds.gif minaeibi /adm/flip1007676278 msu/mmp/labquiz/labquiz.sequence___2___msu/mmp/labquiz/problems/quiz1part1.problem 1007743917 /adm/logout minaeibi1008203043 msu/mmp/labquiz/labquiz.sequence___1___msu/mmp/labquiz/newclass.html minaeibi            Z ,       Z        Z         Z X           Z         Z S           Z      b         b          b          b          b          b      X   c           b          c           c       z    c                                                                                                             q                            Q                                                         Extracted Features                   a                               Total number of correct answers. (Success rate)Success at the first tryNumber of attempts to get answerTime spent until correctTotal time spent on the problem Participating in the communication mechanisms  "           n  Z      c    "      !                              Classifiers                   c    8          N  Non-Tree Classifiers (Using MATLAB)Bayesian Classifier1NNkNN  Multi-Layer PerceptronParzen WindowCombination of Multiple Classifiers (CMC)Decision Tree-Based SoftwareC5.0           (RuleQuest <<C4.5<<ID3)CART        (Salford-systems)QUEST      (Univ. of Wisconsin)CRUISE     [use an unbiased variable selection technique]  6  $        Z m       Z        Z         Z        Z        Z $     c     C    c      *    g      3     g      3     c      t    c          c          c         c         c          c          c               >   <                     	                     e            "                             Bayesian Classifier                    a               a pattern classified into class  with the highest posterior probability or likelihood The feature vectors in our dataset are assumed to be Gaussian distributed, Quadratic discriminant FunctionThe parameters of the Gaussians are estimated using maximum likelihood estimation              S                !     e           e        4     e            a    L    a          a        a     Q    a         a         a          #                              k Nearest Neighbor                   a             k  Nonparametric classifier: Classification without any assumptions about the distribution of the training and testing dataThe Euclidean distances between the testing sample and all the samples in the training set are calculatedAssign the test sample to the label most frequently represented among the k nearest neighborIn this survey the optimal value of k is 3  X   l      Z d    c         c           a               c           $                             Neural Network Classifier                   c    (             Multi-layer Perceptron (MLP) Feed Forward Back-propagation algorithm Sigmoidial activation functionTuning the learning rate, the number of epochs, the number of hidden layers, and the number of neurons in every hidden layer   D                          a        a         a          G       
                   %                             Parzen Window Classifier                   c    (             A d-dimensional hypercube is formed around all the training samples and then, based on the number of patterns that fit in those windows the probability estimates of the different classes are made Multivariate normal distribution is assumed for   r                    3              a        a          a         c     2     a                 &                           *   Combination of Multiple Classifiers (CMC)     +         +     c    "            C l a s s i f i e r s   a r e   f o l l o w e d   b y   a   v o t e .   T h e   c l a s s   g e t t i n g   m a x i m u m   v o t e s   f r o m   t h e   i n d i v i d u a l   c l a s s i f i e r s   w i l l   b e   a s s i g n e d   t o   t h e   t e s t   s a m p l e    W e   a s s u m e d   t h e   r u l e   o f   m a j o r i t y   v o t e   a s    g e t t i n g   m o r e   t h a n   o r   e q u a l   7 5 %   v o t e s  ,   n o t    g e t t i n g   m o r e   t h a n   o r   e q u a l   5 0 %   v o t e s   S i g n i f i c a n t   i m p r o v e m e n t     O r a c l e    c h o o s e s   t h e   c o r r e c t   r e s u l t s   i f   a n y        J        6     a        a     o     a    $    a          a    $    a     C     a        a          a         '                             Results of Non-tree Classifiers                     a             
                (                              Decision Tree-based Classifier                   a               P o p u l a r   i n   d a t a   m i n i n g   c o n t e x t  h i e r a r c h i c a l   o r   l a y e r e d   a p p r o a c h   t o   c l a s s i f i c a t i o n    A d v a n t a g e s :   t r a i n   f a s t   -   e v a l u a t e   f a s t   -   c o m p a c t   m o d e l s   -   i n t e l l i g i b l e   i f   s m a l l   -   d o   f e a t u r e   s e l e c t i o n   -   d o n ' t   u s e   a l l   f e a t u r e s   -   e a s y   t o   c o n v e r t   t o   r u l e s   -   c a n   h a n d l e   m i s s i n g   v a l u e s    I m p o r t a n t   r o l e   i n   p a t t e r n   r e c o g n i t i o n :   h a n d l i n g   n o n - m e t r i c   d a t a    C o r e   a l g o r i t h m   u s e s   a   t o p - d o w n ,   r e c u r s i v e ,   g r e e d y   s e a r c h   o n   t h e   s p a c e   o f   a l l   p o s s i b l e   d e c i s i o n   t r e e s :   I D 3 ,   C 4 . 5 ,   C A R T ,   &    C r i t i c a l   i s s u e s :   H o w   t o   s p l i t ,   H o w   t o   a v o i d   o v e r f i t t i n g   ( p r u n i n g )        	       P 	    c       (   S      	             	C            *                          !   Splitting Using Impurity Function     "         "     a         !      	                   A heuristic to select a query that decreases the impurity as much as possible Entropy impurityGini impurityTwoingPearson Chi^2Likelihood ratio G^2MPI (Mean Posterior Improvement )Members of the divergence family  @           Z Q     c         c         c          r              g            +                             Gini vs. Twoing                   a         ,                             Tree Topologies   2                 a               a          8          *                 #   Avoid overfitting: Cross Validation     $         $     a               The main idea of cross-validation is to estimate how well the current hypothesis will predict unseen data k-fold cross-validation: Divide the data into k subsets of approximately equal size. We train the data k times, each time leaving out one of the subsets from training, but using only the omitted subset to compute the error threshold of interest. If k equals the sample size, this is called "Leave-One-Out" cross-validation (Jackknife).In this survey:2-fold, 10-fold and Jackknife are used.            Z k     c         g         f     g            g              g            g         )     g            g              g        	    g              g        7     c          -                          &   10-fold Cross-Validation vs. Jackknife     '         '     c    %            	            .                             Variable Importance using CART                   a               	     /                              A sample of C5.0 tree/ Rule set     !         !     a         !      	           TotalCorrect <= 165::...AvgTries > 850: Low (13/2):   AvgTries <= 850::   :...Discussion > 2::       :...TotalTimeSpent <= 20.57: Low (2):       :   TotalTimeSpent > 20.57: Middle (3/1):       Discussion <= 2::       :...TotalTimeSpent > 22.63: Low (8):           TotalTimeSpent <= 22.63::           :...AvgTries <= 561: Low (7/1):               AvgTries > 561::               :...TotalCorrect > 156: Low (2):                   TotalCorrect <= 156::                   :...TotalCorrect <= 136: Low (3/1):                       TotalCorrect > 136: Middle (6)Rule 1: (158/25, lift 1.2)        TotalCorrect > 165        ->  class Passed  [0.838]Rule 3: (7, lift 3.2)        FirstCorrect <= 78        TotalCorrect <= 165        ->  class Failed  [0.889]  *         Z     g            c        g          g           c        g          g           c        g          g           c         g             g            $c $       (g (      $   (g (          ,c ,       0g 0         0g 0          4c 4       8g 8         8g 8          <c <        g            g            c        g          g           c        g          g           c        g          g           c         g            g            $c $       (g (         (g (          ,c ,       0g 0         0g 0          4c 4       8g 8          <c <        g           c        g           c     !   g           c        g           c         g           $c $       (g (          ,c ,       0g 0          4c 4    !   8g 8          <c <        g            c                	      	       	      	       	-      	       	      	       	<      	       	      	       	      	       	#      	       	      	       	$      	       	!      	       	+      	       	8      	       	I      	       	      	       	,      	     0                          L   CART tree for 3-Classes using Entropy criterion: (10-fold Cross-Validation)     M         M     c          1                              Final Results      2          !                    Summary/ Conclusion (1)                   a             r  Five non-tree classifiers and four tree-based software are used to segregate the students A combination of multiple classifiers leads to a significant accuracy improvement in all three cases. Using multiple linear regression we realize the importance of the features. We can find that 3 of the 6 features have more correlation with the class labels in all three cases.     s        s    a         3          "                    Summary/ Conclusion (2)                   a               S h o w e d   w h e n   o u r   i n d i v i d u a l   c l a s s i f i e r s   a r e   w o r k i n g   w e l l ,   c o m b i n i n g   c l a s s i f i e r s   h a s   l i t t l e   i m p r o v e m e n t   i n   c l a s s i f i c a t i o n   p e r f o r m a n c e ,   b u t   w h e n   w e   h a v e   w e a k   l e a r n e r   c l a s s i f i e r s   C M C   h a s   a   s i g n i f i c a n t   i m p r o v e m e n t   i n   a c c u r a c y .   T h e   s u c c e s s f u l   i m p l e m e n t a t i o n s   o f   c l a s s i f y i n g   t h e   s t u d e n t s   i n   2   a n d   3 - C l a s s e s   d e m o n s t r a t e   t h e   m e r i t s   o f   u s i n g   t h e   L O N - C A P A   d a t a   f o r   p a t t e r n   r e c o g n i t i o n   i n   o r d e r   t o   p r e d i c t   t h e   s t u d e n t s    f i n a l   g r a d e s   b a s e d   o n   t h e i r   f e a t u r e s ,   w h i c h   a r e   e x t r a c t e d   f r o m   t h e   h o m e w o r k   d a t a .     H o w e v e r ,   u s i n g   1 0 - f o l d   C r o s s - V a l i d a t i o n   a n d   J a c k k n i f e   V a l i d a t i o n ,   w e   a r e   a b l e   t o   s h o w   t h a t   t h e s e   d a t a   a r e   s u f f e r i n g   f r o m   o v e r f i t t i n g   i n   t h e   c a s e   o f   9 - C l a s s e s .                Z      c         c      <     c          #         c         c          c                        4         #                    Next Steps (1)                   a               Gathering more sample data by combining one course data during several semesters to avoid overfitting in the case of 9-classes.Finding the paths that students usually choose to solve the different types of the problems from activity log to extract more relevant features.  2               c    !       !             5         $                    Next Steps (2)                   a             M  Finding the association rules and dependencies among the groups of problems  (Mathematical, Optional Response, Numerical, Java Applet, etc)Finding the classification results in relation to every individual problem in order to find which problem would be the best predictor Implementing fuzzy classification for grouping students                                       9         L     a           ?   a        c         c         c          c     	 9    a          6         %                    Next Steps (3)                   a             d  W e i g h t i n g   f e a t u r e s   v s .   f e a t u r e   s e l e c t i o n   U s i n g   E v o l u t i o n a r y   C o m p u t a t i o n   f o r   e x t r a c t i n g   a n d   w e i g h t i n g   t h e   f e a t u r e s   t o   f i n d   t h e   o p t i m a l   s o l u t i o n   i n   L O N - C A P A   D a t a   M i n i n g   C l u s t e r i n g   t h e   s t u d e n t   d a t a b a s e   i n   o r d e r   t o   d e s c r i b e   t h e   s t u d e n t   b e h a v i o r   a n d   e v a l u a t i n g   t h e   t y p e   o f   p r o b l e m s   e f f e c t s   o n   s t u d e n t s    e f f o r t      3       Z 3    a    /         7                                  	              `         333    3   ff3 `       333  3f 33  ff3 `     "  333  3  ̙   ff3 `         K  f  3  ̙   `      &   e  ̙3 g 3f    `      f  333  ̙      po7 `          ___  f3 ̙  ;/ f  9 `          ff     3 Lm  `          ff  3   LmN Lm    >    ?   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   Wingdings    Trebuchet MS    Courier New    宋体    Gulim    Edge    Microsoft Excel Worksheet    Microsoft Equation 3.0 1   Data Mining for an  Educational Web-based system    Topics    Problem Overview  
   LON-CAPA     Courses using LON-CAPA at MSU    Current Status     Resource Evaluation in Lon-CAPA    Literature Review     Classification Methods    Data Set: PHY183 SS02    Class Labels (3-ways) &   Distributed Data: Course / Students   ,    Preprocessing and Extracting the Features     Extracted Features     Classifiers    Bayesian Classifier     k Nearest Neighbor    Neural Network Classifier    Parzen Window Classifier +   Combination of Multiple Classifiers (CMC)      Results of Non-tree Classifiers    Decision Tree-based Classifier "   Splitting Using Impurity Function    Gini vs. Twoing    Tree Topologies  $   Avoid overfitting: Cross Validation '   10-fold Cross-Validation vs. Jackknife    Variable Importance using CART !   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