LEARNING SEQUENCES CONSTRUCTION USING VAN HIELE MODEL AND BAYESIAN NETWORK

1. LEARNING SEQUENCES CONSTRUCTION USING VAN HIELE MODEL AND BAYESIAN NETWORK J. Wey Chen, Professor Department of Information Management Southern Taiwan University Tainan, Taiwan

2. Outline • Introduction * Motivation * Purpose of the study • Theoretical Foundation * Van Hiele Model * The Cognitive Theory * Bayesian network (BN) * General architecture • A Practical Methodology • Dignostic test Results and Discussion • Conclusion

3. On “Programming Teaching and Learning” 1. "Programming" is a complicated business. 2. Dijkstra1 argues that learning to program is a slow and gradual process of transforming the "novel into the familiar". 3

4. On “Programming Teaching and Learning” 3. programming is not a simple set of discrete skills; the skills form a hierarchy, and a programmer will be using many of them at any point in time. 4. The Educational institutions and businesses are placing more course materials online to supplement classrooms and business training situations. 4

5. Purpose of the Study The main focus of this study is designed to: demonstrate a measurement scheme to detect misconceptions employed by the students, provide a convenient descriptive tool for diagnosing students' programming abilities by representing flaws in the networks. More specifically, this study will help us design A complete Java curriculum content and instructional sequence. 5

6. Theoretical Foundation

7. Van Hiele Model Level 0 Visualization Level 1 Analysis Level 2 Informal Deduction Level 3 Deduction Level 4 Rigor Information Guided orientation Explication Free orientation Integration

8. The Cognitive Theory • Bonar and Soloway11represented and arranged programming knowledge according to its level of difficulty in four cognitive levels: • Lexical and Syntactic • Semantic • Schematic • Conceptual 8

9. The Combined Model Knowledge structure for each learning node 9

10. Bayesian network (BN) A Bayesian network (BN) consists of directed acyclic graphs (DAG) and a corresponding set of conditional probability distributions (CPDs). Based on the probabilistic conditional independencies encoded in the DAG, the product of the CPDs is a joint probability distribution. 10

11. Using Bayesian Networks in Diagnostic Test Ａ Ａ Ｂ Ｃ Ｂ Ｃ Ｄ E Ｄ E

12. Chen’s Implementation (2006) Level 1 Visualization Level 2 Descriptive & Relations Level 3 Implications Level 4 Logic Modification & Analogy Level 5 Abstraction & Modeling Level 1 Visualization Level 2 Analysis Level 3 Informal Deduction Level 4 Deduction Level 5 Rigor Information Guided orientation Explication Free orientation Integration E-mail Discussion Board Assignment Units Tutorial Unit Quick-run Unit Expert Template 12

13. A Practical Methodology • Hold an expert roundtable discussion to roughly determine a set of • knowledge concepts required for a course. • Manually construct the course DAG with the aid of the course textbook. • Develop a diagnostic test to have test questions which cover every • cognitive category for every level of understanding in the entire curriculum • structure. • 4. Extensively conduct the test and collect sufficient Bayesian training data. • Analyze and use the Bayesian training data to trim the unrelated content • and adjust the logical sequence for learning. Once the process is • completed, a new course DAG will be produced. • Group the related knowledge concepts into chapters according to their • sequences appearing on the course DAG. 13

14. 1. Hold an expert roundtable discussion to roughly determine a set of knowledge concepts required for a course. 14

15. 2. Manually construct the course DAG with the aid of the course textbook. 15

16. 3. Develop a diagnostic test 16

17. Extensively conduct the test and collectsufficient Bayesian training data. 17

18. 5. Analyze and use the Bayesian training data to trim the unrelated content and adjust the logical sequence for learning. Once the process is completed, a new course DAG will be produced. 6. Group the related knowledge concepts into chapters according to their sequences appearing on the course DAG. 18

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20. Dignostic test Results and Discussion

21. Knowledge Structure for Dignostic Test

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23. Discussion -To move around the levels in a node

24. Discussion • To move to different learning nodes

25. Discussion • To determine the learning sequence ？ ？ N4L3 N4L3 N5L0 N5L0 N6L0 N6L0

26. Discussion • Diagnosis N5L3 N7L3 N7L3 ？ N8L0

27. Conclusions • The success of this model is attributed to the extensive review of the available literature and to the exploratory interviews with students who participated in the first phase of study. • The proposed Modified van Hiele Model for Computer Science Teaching can help unveil the mystery of the “hidden mind” and provide a logical link for students to inductively learn problem-solving and programming skills. • The system is able to utilize Bayesian network techniques in modeling the student knowledge based on the proposed knowledge structure. 27

28. Thank you for your attention!!