1. 南台科技大學 資訊管理研究所 LEARNING SEQUENCES CONSTRUCTION USING VAN HIELE MODEL AND BAYESIAN NETWORK J. Wey Chen, Professor Department of Information Management Southern Taiwan University Tainan, Taiwan

2. 南台科技大學 資訊管理研究所 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 2

3. 南台科技大學 資訊管理研究所 On “Programming Teaching and Learning” 1. "Programming" is a complicated business. 2. Dijkstra 1 argues that learning to program is a slow and gradual process of transforming the "novel into the familiar". 3

4. 南台科技大學 資訊管理研究所 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. 南台科技大學 資訊管理研究所 Purpose of the Study The main focus of this study is designed to: (1)demonstrate a measurement scheme to detect misconceptions employed by the students, (2)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. 南台科技大學 資訊管理研究所 Theoretical Foundation 6

7. 南台科技大學 資訊管理研究所 Van Hiele Model 7 Level 0 Visualization Level 1 Analysis Level 2 Informal Deduction Level 3 Deduction Level 4 Rigor Information Guided orientation Explication Free orientation Integration

8. 南台科技大學 資訊管理研究所 The Cognitive Theory 8 Bonar and Soloway 11 represented and arranged programming knowledge according to its level of difficulty in four cognitive levels: Lexical and Syntactic Semantic Schematic Conceptual

9. 南台科技大學 資訊管理研究所 The Combined Model 9 Knowledge structure for each learning node

10. 南台科技大學 資訊管理研究所 Bayesian network (BN) 10 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.

11. 南台科技大學 資訊管理研究所 Using Bayesian Networks in Diagnostic Test 11 Ａ ＢＣ Ｄ E yn Ａ 0.90.1 A=yA=n C=y0.80.1 C=n0.20.9 A=yA=n B=y0.60.2 B=n0.40.8 B=yB=n E=y0.70.15 E=n0.30.85 B=yB=n D=y0.30.8 D=n0.70.2 Ａ ＢＣ Ｄ E

12. 南台科技大學 資訊管理研究所 Chen’s Implementation (2006) 12 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 Level 1 Visualization Level 2 Descriptive & Relations Level 3 Implications Level 4 Logic Modification & Analogy Level 5 Abstraction & Modeling

13. 南台科技大學 資訊管理研究所 A Practical Methodology 13 1. Hold an expert roundtable discussion to roughly determine a set of knowledge concepts required for a course. 2. Manually construct the course DAG with the aid of the course textbook. 3. 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. 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.

14. 南台科技大學 資訊管理研究所 1. Hold an expert roundtable discussion to roughly determine a set of knowledge concepts required for a course. 14

15. 南台科技大學 資訊管理研究所 2. Manually construct the course DAG with the aid of the course textbook. 15

16. 南台科技大學 資訊管理研究所 3. Develop a diagnostic test 16 CP Item KnownNRLNP(NRLN) CP Item Number NRLN Number P(NRLN | CP_Item) N2L0YN2L1 0.310344828 48160.333333333 N2L0NN2L11020.2 N2L1YN2L2 0.448275862 18100.555555556 N2L1NN2L240160.4 N2L3YN3L0 0.310344828 640.666666667 N2L3NN3L052140.269230769 N3L0YN3L1 0.275862069 18100.555555556 N3L0NN3L14060.15 N4L0YN4L1 0.137931034 820.25 N4L0NN4L15060.12 N4L1YN4L2 0.137931034 840.5 N4L1NN4L25040.08 N5L1YN5L2 0.448275862 18160.888888889 N5L1NN5L240100.25 N5L2YN5L3 0.344827586 26160.615384615 N5L2NN5L33240.125 N4L3YN6L0 0.724137931 860.75 N4L3NN6L050360.72 N6L0YN6L1 0.655172414 42320.761904762 N6L0NN6L11660.375 N8L1YN8L2 0.342926863 18140.777777778 N8L1NN8L24060.15 N8L2YN8L3 0.412382567 20180.9 N8L2NN8L33860.157894737

17. 南台科技大學 資訊管理研究所 4.Extensively conduct the test and collect sufficient Bayesian training data. 17

18. 南台科技大學 資訊管理研究所 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

19. 南台科技大學 資訊管理研究所 19

20. 南台科技大學 資訊管理研究所 Dignostic test Results and Discussion 20

21. 南台科技大學 資訊管理研究所 Knowledge Structure for Dignostic Test 21

22. 南台科技大學 資訊管理研究所 22

23. 南台科技大學 資訊管理研究所 -To move around the levels in a node Discussion 23

24. 南台科技大學 資訊管理研究所 Discussion –To move to different learning nodes 24

25. 南台科技大學 資訊管理研究所 Discussion To determine the learning sequence 25 N4 L3 N5 L0 N6 L0 N4 L3 ？？ N6 L0 N5 L0

26. 南台科技大學 資訊管理研究所 Discussion Diagnosis 26 N5 L3 N7 L3 N8 L0 ？ N7 L3

27. 南台科技大學 資訊管理研究所 Conclusions 27 1.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. 2.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. 3.The system is able to utilize Bayesian network techniques in modeling the student knowledge based on the proposed knowledge structure.

28. 南台科技大學 資訊管理研究所 Thank you for your attention!! 28