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

transcript

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

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

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.

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).

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.

Theoretical Foundation

Van Hiele Model

Level 0Visualization

Level 1Analysis

Level 2Informal

Deduction

Level 3Deduction

Level 4Rigor

InformationGuided orientation

ExplicationFree orientation

Integration

The Cognitive Theory

Bonar and Soloway11 represented and arranged programming knowledge according to its level of difficulty in four cognitive levels:

• Lexical and Syntactic• Semantic• Schematic• Conceptual

The Combined Model

9Knowledge structure for each learning node

Bayesian network (BN)

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

ＢＣ

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

ＢＣ

南台科技大學資訊管理研究所Chen’s Implementation

(2006)

Level 2Analysis

Level 3Informal

Deduction

Level 4Deduction

Level 5Rigor

InformationGuided orientation

ExplicationFree orientation

Integration

Level 2Descriptive &

Relations

Level 3Implications

Level 4Logic

Modification & Analogy

Level 5Abstraction &

Modeling

General architecture of intelligent van Hiele Problem Solver

Intelligent Tutoring Systems

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

.Lecture notes

.Sample quizzes

.Solution keys

PrerequisiteRecommendations

Generating Learning Sequences

Server

Client

A screen shot of IVHPS displaying the diagnostic report

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”

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

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

Dignostic test Results and Discussion

南台科技大學資訊管理研究所Knowledge Structure for

Dignostic Test

-To move around the levels in a node

Discussion

– To move to different learning nodes

Discussion

• To determine the learning sequence

25.0)34|05( LNLNP 75.0)34|06( LNLNP

？？

Discussion

• Diagnosis

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655172414.019/15

)35()35|08()08|35(

LNPLNLNPLNLNP

890555924.0735689243.0

896551724.026/19

)37()37|08()08|37(

LNPLNLNPLNLNP

Conclusions

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

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.

Thank you for your attention!!

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

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