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LOG O Application of Automatically Constructed Concept Map of Learning to Conceptual Diagnosis of e-learning Expert Systems with Application 36 (2009)

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Presentation on theme: "LOG O Application of Automatically Constructed Concept Map of Learning to Conceptual Diagnosis of e-learning Expert Systems with Application 36 (2009)"— Presentation transcript:

1 LOG O Application of Automatically Constructed Concept Map of Learning to Conceptual Diagnosis of e-learning Expert Systems with Application 36 (2009) Chun-Hsiung Lee, Gwo-Guang Lee, Yungho Leu Presenter : Liew Keng Hou

2 Outline 2

3 What is Concept Map? A A B B Epistemological order of concept map 3

4 Types Concept Map for Learning  Completely manual  Semi-automatic  Automatic 4

5 Outline 5

6 Purpose in This Study 1.Develop the intelligent Concept Diagnostic System(ICDS) of an automatically constructed concept map of learning by the algorithm of Apriori for Concept Map 2.Teachers were provided with the constructed concept map of learners to diagnose the learning barriers and misconception of learners. 3.Remedial-Instruction Path(RIP) was constructed through the analyst of the concepts and weight in the concept map to offer remedial learning. 4.Statistical methods were used to analyze whether the learning performance of learners can be significantly enhanced after they have been guided by the RIP. 6

7 Flow Chart of Concept Diagnosis 7

8 Remedial-Instruction Path A A B B C C Relationships of the epistemological order 8 D D E E Remedial-Instruction Path

9 Outline 9

10 Presetting Conceptual Weight QuestionConcept C1C2C3C4C5 Q110000 Q201000 Q30.50 00 Q40.30.400.30 Q500001 ‘0’: not relevant ‘1’: strongly relevant 10

11 Recording Test Portfolio of Testees QuestionTestees C1C2C3C4C5Total Q1111003 Q2111104 Q3111115 Q4001113 Q5000112 ‘0’: Student answered correctly the test item ‘1’: Student failed to answer correctly the test item 11

12 Comparison Chart S1Q1,Q2,Q3 S2Q1,Q2,Q3 S3Q1,Q2,Q3,Q4 S4Q2, Q3, Q4, Q5 S5Q3, Q4, Q5 Find Out All Large Item sets ItemsetNo. of supports Q13 Q24 Q35 Q43 Q52 12

13 Find Out All Large Item sets(Cont.)  Using association rules of data mining  Sets Min support(MS) = 0.4 (Depends on teacher)  Number of testees = 5  Questions with wrong answers given by testees has to be ≥ MS x N (0.4 x 5 = 2) 13

14 Find Out All Large Item sets(Cont.) Itemset No. of support Q1, Q23 Q1, Q33 Q1, Q41 Q2, Q34 Q2, Q42 Q3, Q43 Q4, Q52 Itemset No. of support Q1, Q23 Q1, Q33 Q2, Q34 Q2, Q42 Q3, Q43 Q4, Q52 MS ≥ 0.4 0.4 x 5 ≥ 2 14

15 Ruling the Test Question Association 15

16 Using Association Rules of Data Mining QuestionTestees C1C2C3C4C5Total Q1111003 Q2111104 Q3111115 Q4001113 Q5000112 Note: ‘0’: Student answered correctly the test item ‘1’: Student failed to answer correctly the test item 16

17 Using Association Rules of Data Mining 17

18 Relationship Between Concept and Concept 18

19 Relationship Between Concept and Concept 19 QuestionConcept C1C2C3C4C5 Q110000 Q201000 Q30.50 00 Q40.30.400.30 Q500001 QuestionConcept C1C2C3C4C5 Q110000 Q201000 Q30.50 00 Q40.30.400.30 Q500001 QuestionConcept C1C2C3C4C5 Q110000 Q201000 Q30.50 00 Q40.30.400.30 Q500001 QuestionConcept C1C2C3C4C5 Q110000 Q201000 Q30.50 00 Q40.30.400.30 Q500001

20 Relationship Between Concept and Concept 20

21 Preliminary Concept Maps(Stage 1) C1 C2 C3 C4 C5 1 0.75 0.3 0.4 0.5 0.4 0.2 0.5 21

22 Preliminary Concept Maps (Cont.) 22

23 Preliminary Concept Maps(Cont.) C1 C2 C3 C4 C5 1 0.75 0.3 0.4 0.5 0.4 0.2 0.5 23

24 Adjusting Concept Map of Learning(Stage 2) Child Concept Parent concept NP C1C2C3C4C5 ChildC1―0000.31 C21―000.42 C30.5 ―0.203 C4000―0.31 C50000―0 NC21013 NP: Number of father concepts contained in the son concept NC: Number of son concepts contained in the father concept 24

25 Complete Concept Map C5 C1 C2 C3 C4 W C5C1 = 0.3 W C5C2 = 0.4 W C5C4 = 0.3 W C1C2 = 1 W C1C3 = 0.5 W C2C3 = 0.5 W C4C3 = 0.2 25

26 Determination of Learning barrier 26

27 QuestionConcept C1C2C3C4C5 Q110000 Q201000 Q30.50 00 Q40.30.400.30 Q500001 0.80.40.50.30 1.81.40.50.31 0.440.29110 Table of Ratio of Wrong Answer (Failratio) 27

28 Algorithm of Remedial-Instruction Path 010 Void main () 020 Call Find_Remedial-Instruction_Path(k, Fault-Concept) 030 End 040 050 //Cj denotes the FaulConcept, and k denoted the index of a father concept on Cj 060 Sub Find_Remedial-Instruction_Path(k,Cj) 070 //judge whether the failratio of Concept Cj is greater than the tolerance for the ratio of the giving wrong answers. 080 If ER(Cj) failratio then 090 Push Cj 100 W = Max{WCiCjj1 5 i 5 n} 110 While (Cihi RootConcept)do //Not Find to Root-Concept 120 push Ci base on W 130 Wend 140 While Stack is not empty //Find to Root-Concept 150 //RIP: Remedial-Instruction_Path 160 RIP = Find_Remedial-Instruction_Path(i,Pop()) 170 Wend 180 End if 190 End Sub 28

29 Intelligent concept diagnostic system(ICDS) 29

30 Intelligent concept diagnostic system(ICDS) 30

31 Outline 31

32 Design of Experiment and Data Analysis  Target of Study  245 Grade 1 students of a senior high school  Pre-test of “Visual Basic Programming Language”  Table of discrimination index of Questions Discrimination Question Q1Q2Q3Q4Q5Q6Q7Q8Q9Q10 PH 0.93 0.710.860.640.140.86 PL 0.710.570.360.21 0.140.21 00.07 Discrimination Index 0.210.360.570.71 0.570.640.430.140.79 <0.2 32

33 Flow Chart of Data Analysis 33

34 Cluster  In order to understand the difference of concept maps produced from the test portfolio of students at different standards  Optimal ratio is 27% for the high-score and low-score clusters 34

35 Sub-Cluster 1.Experimental group: The RIP in concept map served as the learning guide 2.Control group: Traditional non-guided network learning way was adopted GroupCluster Cluster 1 (High-score cluster) Cluster 2 (Medium- score cluster) Cluster 3 (Low-score cluster) Experimental group335633 Control group335133 Number of students6611366 35

36 Outline 36

37 37

38 Cluster and groupItem Mean Standard deviation Significance High-score cluster Experimental group72.5715.83.610.554 Control group67.8612.97 Medium- score cluster Experimental group52.2511.52 2409.030* Control group40.1310.23 Low-score cluster Experimental group37.299.59 3.695.003** Control group19.298.62 38

39 Outline 39

40 Conclusion and Discussion 1.Discrimination index of test questions  If the test question is too simple or difficult? 2.Attribute of test questions  Which type of test question? 3.Learning performance  Which cluster(s) has better performance? 40

41 LOG O Thank You for Your Participating


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