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南台科技大學 資訊管理研究所
IVHPS: A Web-based Bayesian van Hiele Problem Solver for
Java Language
J. Wey Chen, ProfessorDepartment of Information Management
Southern Taiwan UniversityTainan, Taiwan
南台科技大學 資訊管理研究所
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
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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. 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.
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南台科技大學 資訊管理研究所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).
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南台科技大學 資訊管理研究所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.
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南台科技大學 資訊管理研究所
Theoretical Foundation
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南台科技大學 資訊管理研究所
Van Hiele Model
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Level 0Visualization
Level 1Analysis
Level 2Informal
Deduction
Level 3Deduction
Level 4Rigor
InformationGuided orientation
ExplicationFree orientation
Integration
南台科技大學 資訊管理研究所
The Cognitive Theory
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Bonar and Soloway11 represented and arranged programming knowledge according to its level of difficulty in four cognitive levels:
• Lexical and Syntactic• Semantic• Schematic• Conceptual
南台科技大學 資訊管理研究所
The Combined Model
9Knowledge structure for each learning node
南台科技大學 資訊管理研究所
Bayesian network (BN)
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)(
)()|(
YP
YXPYXP
)()|()()|()( YPYXPXPXYPYXP
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.
南台科技大學 資訊管理研究所Using Bayesian Networks in
Diagnostic Test
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A
B C
D E
y n
A 0.9 0.1
A=y A=n
C=y 0.8 0.1
C=n 0.2 0.9
A=y A=n
B=y 0.6 0.2
B=n 0.4 0.8
B=y B=n
E=y 0.7 0.15
E=n 0.3 0.85
B=y B=n
D=y 0.3 0.8
D=n 0.7 0.2
A
B C
D E
南台科技大學 資訊管理研究所
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南台科技大學 資訊管理研究所Chen’s Implementation
(2006)
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Level 1Visualization
Level 2Analysis
Level 3Informal
Deduction
Level 4Deduction
Level 5Rigor
InformationGuided orientation
ExplicationFree orientation
Integration
Level 1Visualization
Level 2Descriptive &
Relations
Level 3Implications
Level 4Logic
Modification & Analogy
Level 5Abstraction &
Modeling
南台科技大學 資訊管理研究所
General architecture of intelligent van Hiele Problem Solver
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Intelligent Tutoring Systems
WAN
DataBase
Knowledge Base
Van Hiele Problem Solver
User InterfaceInput*Answers for sample quizzes*Choosing a study goal*Stating known topics
Output*Lecture notes*Sample quizzes*Recommendations*Learning Sequences
BayesianInference
Adaptive Guidance
Student
Tutorial Unit
Navigation Support
Quick-runUnit
DiscussionBoard
Expert Template
E-mailIntelligentTutoringSystems
InternetAssignment
Unit
.Lecture notes
.Sample quizzes
.Solution keys
PrerequisiteRecommendations
Generating Learning Sequences
Server
Client
南台科技大學 資訊管理研究所
A screen shot of IVHPS displaying the diagnostic report
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南台科技大學 資訊管理研究所A screen shot of IVHPS displaying the lecture notes for the concept “Data
Types”
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南台科技大學 資訊管理研究所
A screen shot of IVHPS displaying a typical quick-run sample output
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南台科技大學 資訊管理研究所A screen shot of IVHPS displaying a typical practice sample from the
expert template
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南台科技大學 資訊管理研究所
Dignostic test Results and Discussion
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南台科技大學 資訊管理研究所Knowledge Structure for
Dignostic Test
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南台科技大學 資訊管理研究所
-To move around the levels in a node
Discussion
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南台科技大學 資訊管理研究所
Discussion
– To move to different learning nodes
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南台科技大學 資訊管理研究所
Discussion
• To determine the learning sequence
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N4L3
N5L0
N6L0
N4L3
? ?
N6L0
N5L0
南台科技大學 資訊管理研究所
Discussion
• Diagnosis
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655172414.019/15
)08(
)35()35|08()08|35(
LNP
LNPLNLNPLNLNP
890555924.0735689243.0
896551724.026/19
)08(
)37()37|08()08|37(
LNP
LNPLNLNPLNLNP
N5L3
N7L3
N8L0
?
N7L3
南台科技大學 資訊管理研究所
Conclusions
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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.
南台科技大學 資訊管理研究所A Practical Model for Applications
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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:
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.
南台科技大學 資訊管理研究所
Thank you for your attention!!
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