Applying Fuzzy Theory in Intelligent Web Systems

1. Applying Fuzzy Theory in Intelligent Web Systems Chih-Ming Chen (陳志銘) 助理教授兼研發組長 資訊科學學系/學習科技研究所 國立花蓮教育大學 智慧型網路系統暨資料探勘實驗室 專長研究領域: 智慧型網路教學系統 人工智慧及智慧型計算理論 個人化課程編序策略研究 網際網路資訊探勘及擷取 網路學習之學習診斷與評量 數位典藏與資訊加值 PDA行動與無所不在學習模式研究 網頁及文件自動分類

2. Outline • The Used Fuzzy Concept • Fuzzy Inference • Fuzzy Association Rule • Neuro-fuzzy network • Current Researches Relating to Fuzzy Theory in Intelligent Web-based Learning Systems • Personalized Mobile Learning System based on Fuzzy Item Response Theory for Promoting English Vocabulary and Reading Abilities • Mining Formative Evaluation Fuzzy Rules Using Learning Portfolios for Web-based Learning Systems • The Other Intelligent Systems • News Archive and Data Mining Agent System • Chinese Word Segmentation System with Intelligent New Word Extension • Q & A Time

3. Fuzzy Inference (1/3) 二值理論推論形式 （事實）　麻雀是鳥 （規則）　鳥會飛 （結論）　麻雀會飛 Fuzzy推論形式： （事實）　這番茄很紅 （規則）　蕃茄若是紅了就熟了 （結論）　這蕃茄很熟了

4. Fuzzy Inference (2/3) (facts) X is (rule) if X is A then Y is B 希望得到的結論是 (result) Y is Mamdani 法 A B 1 1 0 0

5. Fuzzy-logic inference system (3/3)

6. Fuzzy Association Rule (1/8) • The KDD (Knowledge discovery in database) process generally consists of the following three phase: (1) Pre-processing. (2) Data-mining. (3) Post-processing. • Fuzzy Transaction Data-mining Algorithm (FTDA): This is integrates fuzzy-set concepts with the apriori algorithm and uses the result to find interesting item-sets and fuzzy association rules.

7. Fuzzy Association Rule (2/8) An example for the detailed process of Apriori algorithm

8. Fuzzy Association Rule (3/8) An example for mining fuzzy association rule

9. Fuzzy Association Rule (4/8) The defined fuzzy membership function for mining fuzzy association rule

10. Fuzzy Association Rule (5/8)

11. Fuzzy Association Rule (6/8)

12. Fuzzy Association Rule (7/8)

13. Fuzzy Association Rule (8/8)

14. Fuzzy-neuro Network Four-layer learning architecture of the neuro-fuzzy networks

15. Current Researches Relating to Fuzzy Theory in Intelligent Web Systems • Personalized Mobile Learning System based on Fuzzy Item Response Theory for Promoting English Vocabulary and Reading Abilities

16. Introduction • The learning form is dramatically changing • E-learning (Electronic-learning) • M-learning (Mobile-learning) • U-learning (Ubiquitous-learning) • Mobile learning • an effective form of flexible learning • utilizing spare time for learning • learning takes place anytime, anywhere

17. Introduction • English is an international language. • English as Second Language, ESL • English as Foreign Language, EFL • How to learn English well ? • assistant tools • good material • “little and often”

18. Purpose of the Study • Considering the advantages of the mobile learning • Breaking the limitations of time and space • Utilizing spare time for learning • A personalized intelligent m-learning system (PIMS) for supporting effective English learning

19. System Design • A Personalized Intelligent M-learning System (PIMS) includes : • The remote courseware server • The client mobile learning system • The feature of PIMS • Portable • Personalization • Intelligent tutoring system

20. PIMS System Architecture

21. System Architecture of Personalized Vocabulary Learning System

22. The Remote Courseware Server • English news crawler agent • automatically retrieve English News from the Internet • Difficulty assessment agent of English news • automatically measuring the difficulty parameters of English news articles • Courseware management agent • online courseware management

23. The Client Mobile Learning System • Learning interface agent • providing a flexible learning interface • Feedback agent • collecting learner explicit feedback information • Personalized courseware recommendation agent • recommending a personalized courseware • evaluating learners’ reading ability

24. The Client Mobile Learning System • Personalized vocabulary recommendation agent • enhancing learner vocabulary ability • discovering the new vocabularies to individual learners

25. The Learning Procedure of the Client Mobile Learning System

26. English E-News Archive • FTV English e-news • Metadata extraction mechanism • English and Chinese news titles • URL address • Date • News body

27. The detailed procedures of English news archive

28. Measuring Difficulty of English News Article • Readability Flesch reading ease formula, 1948 • The drawback of Flesch formula • No consideration of the reader’s vocabulary ability • Modified Flesch reading ease formula • Flesch RE • Proposed fuzzy difficulty parameter

29. Flesch Reading Ease Formula • Flesch’s reading ease formula can be formulated as follows: • RE represents the reading ease value ,range from 0 (difficulty)~100(easy) • ASL is the average sentence length • ASW is the average number of syllables per word

30. The Proposed Scheme for Evaluating Difficulty of English News Article • Computing the Percentages of Vocabulary • Determining Fuzzy Membership Functions by the K-means Clustering Algorithm • Designing Fuzzy Rule Base • Fuzzy Inference

31. The determined fuzzy membership functions for the percentage of occurring vocabulary of the elementary level

32. The defined membership functions for the difficulty of English News

33. The fuzzy rule base designed by English course experts for inferring difficulty of English news article

34. Defuzzification The center of gravity (COG)：

35. An Example for Inferring the Difficulty of English News Article

36. Computing the value of English news by Flesch’s reading ease formula Step1： Step2： Normalizing the RE value

37. Computing the difficulty of English news by fuzzy inference Step3： The triggered consequent parts of output variable for defuzzification

38. Determining the final difficulty of English news article by integrating the normalized and the inferred difficulty values under the adjustable weight is set to 0.5 Step4：

39. Personalized English News Recommendation Difficulty Easy • Item Response Theory (IRT)

40. Personalized English News Recommendation • Evaluating English reading ability • the Bayesian estimation approaches is applied in this study

41. Personalized English News Recommendation • Recommending English news • the maximum information strategy

42. Personalized English News Recommendation • The drawbacks of item response theory • Learner’s response is not usually belonging to completely understanding or not understanding case for the content of learned courseware • The traditional item response theory cannot estimate learner ability for personalized learning services according to learner’s non-crisp responses (i.e. uncertain/fuzzy responses)

43. Personalized English News Recommendation • Fuzzy Item Response Theory (FIRT)

44. Personalized English News Recommendation • The designed fuzzy rule base for inferring learner’s understanding degree

45. Personalized English Vocabulary Recommendation • Personalized English Vocabulary Learning System • Learner’s vocabulary ability • Vocabulary difficulty parameter Rij : the set of the recommended new vocabularies Ai : the set of vocabularies that the corresponding difficulty parameters are higher than learner’s vocabulary ability Cj : the set of all vocabularies contained in the English news article Li : the set of the acquired vocabularies of the learner

46. The Implemented System (1/8) (b) (a)

47. The Implemented System (2/8) (d) (c)

48. The Implemented System (3/8) (f) (e)

49. The Implemented System (4/8) (h) (g)

50. The Implemented System (5/8) (j) (i)