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E-raamat: Advances in Personalized Web-Based Education

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This book aims to provide important information about adaptivity in computer-based and/or web-based educational systems. In order to make the student modeling process clear, a literature review concerning student modeling techniques and approaches during the past decade is presented in a special chapter. A novel student modeling approach including fuzzy logic techniques is presented. Fuzzy logic is used to automatically model the learning or forgetting process of a student. The presented novel student model is responsible for tracking cognitive state transitions of learners with respect to their progress or non-progress. It maximizes the effectiveness of learning and contributes, significantly, to the adaptation of the learning process to the learning pace of each individual learner. Therefore the book provides important information to researchers, educators and software developers of computer-based educational software ranging from e-learning and mobile learning systems to educational games including stand alone educational applications and intelligent tutoring systems.

1 Student Modeling for Personalized Education: A Review of the Literature
1(24)
1.1 Introduction
1(2)
1.2 Student Modeling Techniques and Methods
3(7)
1.2.1 The Overlay Method
3(1)
1.2.2 User Stereotypes
4(1)
1.2.3 Models for Misconceptions and Erroneous Knowledge
5(2)
1.2.4 Machine Learning Techniques
7(1)
1.2.5 Cognitive Theories
7(1)
1.2.6 Modeling the Uncertainty of Learning
8(2)
1.2.7 Ontology-Based Student Modeling
10(1)
1.3 Student's Characteristics to Model
10(11)
1.3.1 Knowledge Level
11(3)
1.3.2 Errors/Misconceptions
14(2)
1.3.3 Cognitive Features Other Than Knowledge Level
16(3)
1.3.4 Affective Features
19(1)
1.3.5 Meta-Cognitive Features
20(1)
1.4 Discussion
21(4)
2 Fuzzy Logic in Student Modeling
25(36)
2.1 Introduction
25(1)
2.2 An Overview of Fuzzy Logic
26(6)
2.2.1 Type-1 Fuzzy Sets
28(1)
2.2.2 Interval Type-2 Fuzzy Sets
28(1)
2.2.3 Rule-Based Fuzzy Logic System
29(2)
2.2.4 Applications of Fuzzy Logic
31(1)
2.3 Fuzzy Logic for Knowledge Representation
32(13)
2.3.1 Knowledge Domain Representation Using a Fuzzy Related-Concept Network
36(9)
2.4 A Novel Rule-Based Fuzzy Logic System for Modeling Automatically the Learning or Forgetting Process of a Student
45(14)
2.4.1 Integration of the Fuzzy Rules
49(1)
2.4.2 Application of the Presented Rule-Based Fuzzy Logic System in a Programming Tutoring System
50(9)
2.5 Conclusions and Discussion
59(2)
3 A Novel Hybrid Student Model for Personalized Education
61(30)
3.1 Introduction
61(1)
3.2 Related Work
62(2)
3.3 The F.O.S. Hybrid Student Model
64(5)
3.3.1 Fuzzy Rules
65(2)
3.3.2 Overlay Model
67(1)
3.3.3 Stereotypes
68(1)
3.4 Operation of F.O.S
69(3)
3.5 Application of F.O.S. in a Programming Tutoring System
72(7)
3.5.1 Fuzzy Rules
72(1)
3.5.2 Overlay Model
73(1)
3.5.3 Stereotypes
74(5)
3.5.4 Cognitive State Transitions of Learners of the Programming Tutoring System
79(1)
3.6 Examples of Operations
79(10)
3.7 Conclusions
89(2)
4 Evaluation
91(24)
4.1 Introduction
91(2)
4.2 The Evaluation Method
93(4)
4.2.1 The Evaluation Framework PERSIVA
93(1)
4.2.2 The Evaluation Criteria
93(1)
4.2.3 The Evaluation Process
94(2)
4.2.4 The Evaluation Population
96(1)
4.3 Results
97(17)
4.3.1 Learners' General Satisfaction
97(1)
4.3.2 Learners' Performance
97(2)
4.3.3 Changes on Learners' Behavior and Thoughts About Computer Programming
99(1)
4.3.4 Changes on Learners' Behavior and Thoughts About E-Learning
100(1)
4.3.5 Results on Learners' Further Studies
100(8)
4.3.6 Learners' Satisfaction About the System's Adaptive Responses to Their Needs
108(1)
4.3.7 The Validity of the Conclusions Drawn by the Student Model Concerning the Aspects of the Students' Characteristics
109(3)
4.3.8 The Validity of the Adaptation Decision Making of the Student Model
112(2)
4.4 Conclusions
114(1)
Conclusions and Discussion 115(4)
Appendix A The Matrixes of the System's FR-CN 119(10)
Appendix B Questionnaires 129(8)
Appendix C Screenshots 137(8)
References 145