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minaeibi Thu Sep 19 15:03:26 2002 EDT
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V 0 Data Mining for an Educational Web-based system 1 1 c 6 Ph.D. SurveySeptember 2002Behrouz MinaeiAdviser: Dr. Bill PunchGenetic Algorithm Research and Application GroupDepartment of Computer Science and EngineeringMichigan State University P c c c
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z c Topics c . _ Problem OverviewLON-CAPALiterature ReviewClassifying the StudentsConclusion and Next Steps $ _ ` a Problem Overview c . E 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 belongs. , P E P c c g C c D c c c c c c g c c c c 4 c What is 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 course, from lecture preparation to administration of homework assignments and examsAllows instructors to create educational materials and to share such learning resources with colleagues across departments and institutionsUsers can log into any machines in the networkUsers can access courses/resources from anywhere on the network under one username 4 - $ @ ; c Individualized Assessment c Courses use LON-CAPA at MSU c $ f The Project: Status a 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 Z Z f Z 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 : ( F i n d r u l e s t h a t p r e d i c t s t r o n g 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 ) M c w c c ) c c M c c c g c c c c M c c
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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 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 $ 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.gif 1007670091 /res/adm/pages/grds.gif minaeibi /adm/flip 1007676278 msu/mmp/labquiz/labquiz.sequence___2___msu/mmp/labquiz/problems/quiz1part1.problem 1007743917 /adm/logout minaeibi 1008203043 msu/mmp/labquiz/labquiz.sequence___1___msu/mmp/labquiz/newclass.html minaeibi Z , Z Z Z b b b b b b X c b c c c x c c q Q Extracted Features a Total number of correct answers. (Success rate)Success at the first tryNumber of attempts to get answerHours until correctTotal time spent on the problem Participating in the communication mechanisms " n Z c " Classifiers c 8 Non-Tree Classifiers (Using MATLAB)Bayesian Classifier1NN and kNN (k=3)Multi-Layer PerceptronParzen WindowDecision Tree-Based SoftwareC5.0CARTQUESTCRUISE z $ P $ a P a a a D b Bayesian Classifier a Quadratic discriminant FunctionThe feature vectors in our dataset are assumed to be Gaussian distributed, The parameters of the Gaussians are estimated using maximum likelihood estimation l " a a a a a ! Neural Network Classifier c ( Multi-layer Perceptron (MLP) Back-propagation learning 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 C
" Parzen Window Classifier c ( A d-dimensional window 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 @ a a a # * Combination of Multiple Classifiers (CMC) + + c " C l a s s i f i e r s 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 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 C 2 a a l a $ a a $ a C a a a $ Results of Non-tree Classifiers a
% Decision Tree-based Classifier 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 ) Z c ( 4 C & Splitting Criteria c & > H o w t o u s e t h e d a t a t o d e t e r m i n e t h e s p l i t s : S e l e c t e a c h s p l i t o f a n o d e s o t h a t t h e d a t a i n e a c h o f t h e c h i l d n o d e s a r e p u r e r t h a n t h a t i n t h e p a r e n t n o d e . N
" d 2 c a ' ! Splitting Using Impurity Function " " a ! A heuristic to select a query that decreases the impurity as much as possible Entropy impurityGini impurityOthers: Twoing, Pearson Chi^2, Likelihood ratio G^2, MPI (Mean Posterior Improvement ), and 5 Other members of the divergence family P Q c c c c ( Tree Topologies 2 a a ) & 10-fold Cross-Validation vs. Jackknife ' ' c % * Variable Importance using CART ` + 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 , , Final Results @ - Summary/ Conclusion (1) a u Five non-tree classifiers and four tree-based classifiers 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. v v a . 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 f / 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 ! ! 0 ! Next Steps (2) a Making ANOVA to evaluate the effect of interaction between groups of problems and groups of studentsFinding the association rules and dependencies among the groups of problemsImplementing fuzzy classification for grouping students Z a 1 " 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 / 2 ` 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 > ? " d d @ * ? n A d @ q < n A q F L K #M n ? " d d @ @@ `` P R M ` 2 p > > (
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Wingdings Trebuchet MS Courier New 宋体 Gulim Lucida Sans Unicode Edge Microsoft Excel Worksheet Microsoft Equation 3.0 1 Data Mining for an Educational Web-based system Topics Problem Overview What is LON-CAPA? Individualized Assessment Courses use LON-CAPA at MSU The Project: 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 Neural Network Classifier Parzen Window Classifier + Combination of Multiple Classifiers (CMC) Results of Non-tree Classifiers Decision Tree-based Classifier Splitting Criteria " Splitting Using Impurity Function Tree Topologies ' 10-fold Cross-Validation vs. Jackknife Variable Importance using CART ! A sample of C5.0 tree/ Rule set Final Results Summary/ Conclusion (1) Summary/ Conclusion (2) Next Steps (1) Next Steps (2) Next Steps (3) Fonts Used Design Template Embedded OLE Servers Slide Titles " _7 7rje r j e
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