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Expert Systems with Application 36 (2009) Chun-Hsiung Lee, Gwo-Guang Lee, Yungho Leu

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. Outline. Introduction Purpose in This Study Research Approach

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Expert Systems with Application 36 (2009) Chun-Hsiung Lee, Gwo-Guang Lee, Yungho Leu

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  1. 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 • Introduction • Purpose in This Study • Research Approach • Experiment and Data Analysis • - test & Analysis • Conclusion and Discussion

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

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

  5. Outline • Introduction • Purpose in This Study • Research Approach • Experiment and Data Analysis • - test & Analysis • Conclusion and Discussion

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

  7. Flow Chart of Concept Diagnosis

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

  9. Outline • Introduction • Purpose in This Study • Research Approach • Experiment and Data Analysis • - test & Analysis • Conclusion and Discussion

  10. Presetting Conceptual Weight ‘0’: not relevant‘1’: strongly relevant

  11. Recording Test Portfolio of Testees ‘0’: Student answered correctly the test item‘1’: Student failed to answer correctly the test item

  12. Find Out All Large Item sets

  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)

  14. Find Out All Large Item sets(Cont.) MS ≥ 0.4 0.4 x 5 ≥ 2

  15. Ruling the Test Question Association • The confidence level of the test question association rule QQ is the concept of conditional probability. • It implies that a testee gives a wrong answer to Question Q, there is a probability for the testeeto give a wrong answer to Question Q, too • The estimated confidence level formula is

  16. Using Association Rules of Data Mining Confidence(Q1Q2)=P(Q2|Q1)= 100% Confidence(Q1Q2)=P(Q1|Q2)= 75% Confidence(Q3Q2)=P(Q3|Q2)= 80% Note: ‘0’: Student answered correctly the test item ‘1’: Student failed to answer correctly the test item

  17. Using Association Rules of Data Mining • Let the minimum confidence(MC) level be below 70% • Rule 1. Confidence (Q1 Q2) = 100%Rule 2. Confidence (Q1 Q3) = 100%Rule 3. Confidence (Q2 Q1) = 75%Rule 4. Confidence (Q2 Q3) = 100%Rule 5. Confidence (Q3 Q2) = 80%Rule 6. Confidence (Q4 Q3) = 100%Rule 7. Confidence (Q5 Q4) = 100%

  18. Relationship Between Concept and Concept • Conversion from “test question association rules” to the effect of “relation between concept and concept” • Q: th test questionC: th conceptRQC: relavance degree between Q and CWCC: relevance degree between CandX

  19. Relationship Between Concept and Concept • Q1 Q2 = C1C2 = Confidence(Q1 Q2)  RQ1C1  RQ2C2 = 1  1  1 = 1 • Q1Q3 = C1C3 = Confidence(Q1Q3)  RQ1C1  RQ3C3= 1  1  0.5 = 0.5 • Q2Q1 = C2C1 = Confidence(Q2Q1)  RQ2C2  RQ1C1= 0.75  1  1 = 0.75 • Q2Q3 = C2C1 = Confidence(Q2Q3)  RQ2C2  RQ3C1= 1 1  0.5 = 0.5(0.5 < 0.75(3)) = C2C3 = Confidence(Q2 Q3)  RQ2C2  RQ3C3= 1  1  0.5 = 0.5 • Q3 Q2 = C1C2 = Confidence(Q3 Q2)  RQ3C1  RQ2C2 = 0.8  0.5  1 = 0.4(0.4 < 1(1)) = C3C2 = Confidence(Q3 Q2)  RQ3C3  RQ2C2 = 0.8  0.5  1 = 0.4 Rule 2. Confidence (Q3Q1) = 100% Rule 4. Confidence (Q2Q3) = 100%

  20. Relationship Between Concept and Concept • Q4 Q3 = C2C3 = Confidence(Q4 Q3)  RQ4C2  RQ3C3 = 1  0.4  0.5 = 0.2(0.2 < 0.5(4)) = C4C3 = Confidence(Q4Q3)  RQ4C4  RQ3C3= 1 0.3  0.5 = 0.15 • Q5Q4 = C5C1 = Confidence(Q5Q4)  RQ5C5  RQ4C1= 1 1  0.3 = 0.3= C5C2 = Confidence(Q5 Q4)  RQ5C5  RQ4C2= 1  1  0.4 = 0.4 = C5C1 = Confidence(Q5 Q4)  RQ5C5  RQ4C4= 1  1  0.3 = 0.3

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

  22. Preliminary Concept Maps (Cont.) • Q3 Q2 = C1C2 = Confidence (Q3 Q2)  RQ3C1  RQ2C2= 0.8  0.5  1 = 0.4 (0.4 < 1(1))Q3 Q2 = C3C2 = Confidence(Q3 Q2)  RQ3C3  RQ2C2= 0.8  0.5  1 = 0.4 • Q4 Q3 = C2C3 = Confidence (Q4 Q3)  RQ4C2  RQ3C3= 1  0.4  0.5 = 0.2 (0.2 < 0.5(4))Q4 Q3 = C4C3 = Confidence (Q4 Q3)  RQ4C4  RQ3C3= 1  0.3  0.5 = 0.15 • Q5Q4 = C5C1 = Confidence (Q5 Q4)  RQ5C5  RQ4C1= 1  1  0.3 = 0.3Q5 Q4 = C5C2 = Confidence (Q5 Q4)  RQ5C5  RQ4C2= 1  1  0.4 = 0.4Q5 Q4 = C5C1 = Confidence (Q5 Q4)  RQ5C5  RQ4C4= 1  1  0.3 = 0.3 • Q1 Q2 = C1C2 = Confidence (Q1 Q2)  RQ1C1  RQ2C2= 1  1  1 = 1 • Q1 Q3 = C1C3 = Confidence (Q1 Q3)  RQ1C1  RQ3C3= 1  1  0.5 = 0.5 • Q2 Q1 = C2C1 = Confidence (Q2 Q1)  RQ2C2  RQ1C1= 0.75  1  1 = 0.75 • Q2 Q3 = C2C1 = Confidence (Q2 Q3)  RQ2C2  RQ3C1= 1  1  0.5 = 0.5 (0.5 < 0.75(3))Q2 Q3 = C2C3 = Confidence(Q2 Q3)  RQ2C2  RQ3C3= 1  1  0.5 = 0.5

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

  24. Adjusting Concept Map of Learning(Stage 2) NP: Number of father concepts contained in the son conceptNC: Number of son concepts contained in the father concept

  25. Complete Concept Map C5 WC5C4 = 0.3 WC5C1 = 0.3 WC5C2 = 0.4 C1 C4 C2 WC1C2 = 1 WC2C3 = 0.5 WC1C3 = 0.5 WC4C3 = 0.2 C3

  26. Determination of Learning barrier • Calculate the ratio of wrong answers given in the test portfolio:ER(C) = • : weight of the th concept of the th test question which was wrongly answer: weight of the th concept in the whole test paper

  27. Table of Ratio of Wrong Answer (Failratio) ER(C1) = = 0.44 ER(C2) = = 0.29

  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 (CihiRootConcept)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

  29. Intelligent concept diagnostic system(ICDS)

  30. Intelligent concept diagnostic system(ICDS)

  31. Outline • Introduction • Purpose in This Study • Research Approach • Experiment and Data Analysis • - test & Analysis • Conclusion and Discussion

  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 <0.2

  33. Flow Chart of Data Analysis

  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

  35. Sub-Cluster • Experimental group: The RIP in concept map served as the learning guide • Control group: Traditional non-guided network learning way was adopted

  36. Outline • Introduction • Purpose in This Study • Research Approach • Experiment and Data Analysis • - test & Analysis • Conclusion and Discussion

  37. - test & Analysis • : significant standard • H0 : There is a significant difference between the mean of experimental group and the mean of control group • If P-value <, then H0is rejected. • If P-value , then H0 is not rejected

  38. -test of Independent Samples of Experimental Group and Control Group of Three Cluster * < 0.1 * < 0.01

  39. Outline • Introduction • Purpose in This Study • Research Approach • Experiment and Data Analysis • - test & Analysis • Conclusion and Discussion

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

  41. Thank You for Your Participating

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