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IVHPS: A Web-based Bayesian van Hiele Problem Solver for Java Language

IVHPS: A Web-based Bayesian van Hiele Problem Solver for Java Language. J. Wey Chen, Professor Department of Information Management Southern Taiwan University Tainan, Taiwan. Outline. Introduction * Motivation * Purpose of the study * Advantages of the System

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IVHPS: A Web-based Bayesian van Hiele Problem Solver for Java Language

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  1. IVHPS: A Web-based Bayesian van Hiele Problem Solver for Java Language J. Wey Chen, Professor Department of Information Management Southern Taiwan University Tainan, Taiwan

  2. Outline • Introduction * Motivation * Purpose of the study * Advantages of the System • Theoretical Foundation * Van Hiele Model * The Cognitive Theory * Bayesian network (BN) * General architecture • 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. 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. 3

  4. Purpose of the Study This paper formulates an alternative pedagogical approach that encompasses the van Hiele Model, cognitive model, and Bayesian network to design a web-based intelligent van Hiele Problem Solver (IVHPS). 4

  5. Advantages of the System The system takes full advantage of Bayesian networks (BNs) to: 1. provide intelligent navigation support, and 2. make individualized diagnosis of student solutions in learning computer programming languages. 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.

  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 13

  13. General architecture of intelligent van Hiele Problem Solver

  14. A screen shot of IVHPS displaying the diagnostic report 15

  15. A screen shot of IVHPS displaying the lecture notes for the concept “Data Types” 16

  16. A screen shot of IVHPS displaying a typical quick-run sample output 17

  17. A screen shot of IVHPS displaying a typical practice sample from the expert template 18

  18. Dignostic test Results and Discussion

  19. Knowledge Structure for Dignostic Test

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

  21. Discussion • To move to different learning nodes

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

  23. Discussion • Diagnosis N5L3 N7L3 N7L3 ？ N8L0

  24. 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. 25

  25. A Practical Model for Applications • To help engineering educators wisely utilize the information described in this paper, we suggest the following approach be taken to design sound curriculum content and sequence: • 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. 26

  26. Thank you for your attention!!

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