臺灣博碩士論文加值系統

正義
我們於2006年正式將我們的自動程式評分系統(APAS)命名為DICE,一個羅馬神話下的女神,象徵著正義的人格特質.我們的目標在於建立一個線上自動評分系統用於評閱程度語言作業. 正義是DICE極重要的特性之一, 我們在混成式學習環境下由教師的監督下使用DICE,獲得56%學習成效的提昇. 同時我們也強調DICE的防剽竊機制發揮了揚善罰惡的正義真義.

邊界
孔夫子主張"有教無類". 作為一個特殊的學習管理機制, DICE扮演著將訓練學生程式語言相關資訊集中管理及轉遞的角色. DICE可以檢查程式作業中特定主題的知識概念. DICE被設計成網路上的獨立服務,可不計對象提供跨越時空的教育服務.

實踐
孔夫子主張"學而時習之".DICE被視為一個典型的講授教學的延深訓練方法,結果是學生於課堂上付出更多的努力透過實作來改善他們的程式設計技巧.

循序
孔夫子主張"循序漸進". 我們應用軟體工程的"測試驅動發展"(TDD)概念於DICE中. DICE TDD模組借用正式的數學定義,將訓練材料依技巧與觀念.由探索學習模式至引導學習模式,共分為16種模式. DICE TDD 幫助系統將程式碼按技巧及觀念來檢驗.我們證明這個模組可以有效的對程式設計低成就的同學有極大的幫助.

適性
孔夫子主張"因材施教". 我們的研究節果顯示,學生的kolb學習型態,對於使用TDD及不使用TDD的學習成效,有顯著的差異.我們據此規劃了一個適性學習模組於DICE中(DALM),DAML係依照學生的個別差異調適學生至不同TDD模式.

我們也發展了一個型態化心智圖系統作為共同的資料模組及知識表達方法.
經評估,DICE可比不使用DICE的傳統訊練方法提昇11%學習成效.而DICE加上TDD模組又比純粹使用DICE的訓練方法可提昇51%學習成效.

JUSTICE
DICE, the personification of justice, a goddess of Roman mythology, was taken as the name of our Automatic Programming Assessment System (APAS) since 2006. We aimed to build an on-line auto-grading system for judging programming assignments. Justice is one of the most important characteristics in DICE, which has been used in blended learning in the classroom under an instructor’s supervision. We emphasize the automatic assessment system and plagiarism detection mechanism to guard against injustice by punishing injustice and rewarding virtue.

BOUNDARY
Confucius argued that “Education knows no boundaries.” As a specialized learning management system, DICE plays the role of delivering general purpose assignments and exercises. DICE checks programming exercises and conceptual knowledge in a specific topic area. DICE is designed as a stand-alone service to support the education across space and time.

Practice
Confucius claimed that “To learn and then have occasion to practice what you have learned—is this not satisfying?” DICE is treated as an augmented training approach to support the classical lecture-based teaching for computing. The result is the students put more effort on improving their programming skills via practice in class.

Sequence
Confucius argued for “Improvement in proper sequence.” We have made use of the method of ‘Test Driven Development” (TDD) which was introduced in software engineering. The DICE TDD classifies the training material from exploration to instruction into 16 levels. It supports DICE to help check student’s code, in terms of both his programming skill and his conceptual knowledge of a specific topic.

Adaptation
Confucius said, “Teach students in accordance with their aptitude.” Our results indicate that the influence of Kolb learning style has different significance on learning outcomes in TDD verse non-TDD. An adaptive learning model was proposed to adapt to students with different TDD training materials based on their personality differences.

We also developed a Typed Mind Map as a common data model and knowledge representation in DICE. DICE promotes learning performance up to 11% better than a non-DICE approach, while a DICE with TDD model improves the learning performance to 51% more than the pure DICE approach.

ABSTRACT ii
List of Tables v
List of Figures vi
1 Introduction 1
1.1 Motivation, Objectives and Significance 1
1.2 DICE, a parse-tree based online automatic programming assessment system (APAS) 3
1.3 Test Driven Development in DICE 4
1.4 Using Typed Mind Maps as a data model in DICE 5
1.5 Evaluation 5
1.6 Contribution 6
2 Literature Review and Background 8
2.1 Automatic Graders 8
2.1.1 Computer Augmented Learning Management Systems8
2.1.2 Teaching Software Testing 10
2.1.3 Automatic Grading 11
2.1.4 First Generation - Early Assessment Systems: 13
2.1.5 Second Generation - Tool-Oriented System: 14
2.1.6 Third Generation - Web-Oriented Systems: 15
2.1.7 Automated Assessment Approaches for Programming Assignments 16
2.1.7.1 Dynamic analysis approach 16
2.1.7.2 Static analysis approach 18
2.2 Test Driven Development 24
2.2.1 History of TDD 25
2.2.2 Recently Research about TDD in Education 25
2.3 Learning Style 29
2.3.1 Definition of Learning Style theories 29
2.3.2 Kolb’s Learning Style Theory 30
2.4 Training Method 32
2.5 Mind Maps 33
3 Overview of DICE 35
3.1 Architecture 35
3.2 Making Parse Trees 40
3.3 Grading 42
3.4 Plagiarism detection 42
3.5 Implementation 43
3.6 Screenshots 45
3.6.1 Server Events 45
3.6.2 Server Side Current Events 45
3.6.3 Monitoring and Interacting on/with a Client 46
3.6.4 Client Side 47
3.6.5 Grading Result 48
3.6.6 Instructor on Client Side 48
3.6.7 DICE Server with Typed Mind Maps Representation 48
3.6.8 Judging Results with TDD Hints 49
3.6.9 Judging the Result Time of a Student’s Judge Thread 51
3.6.10 Judging Result with Plagiarism Detection 51
4 TDD model in DICE 53
4.1 TDD in an automatic grading system 54
4.1.1 A Thorough Example 54
4.1.2 Test-based data sets versus TDD data sets 55
4.1.3 TDD of programming skill vs. TDD of concept 56
4.2 An Example with C 62
4.2.1 The problem description 63
4.2.2 The test units 63
4.2.3 The sample test-driven code 64
4.2.4 A Implementation of DICE TDD 66
4.2.4.1 The TDD test suite node 66
4.2.4.2 The user interface of DICE TDD model 67
5 Using a Typed Mind Map as a Data Model in a TDD DICE System 70
5.1 Mind Map 70
5.1.1 Formal Definition of the Mind Map 71
5.1.2 Informal Definition of the Typed Mind Map 73
5.1.3 An Implementation of the Typed Mind Map 75
5.2 Using Typed Mind Map in DICE 76
5.2.1 Training Material Organization for DICE TDD model 77
5.2.1.1 Storage Policies 77
5.2.1.2 The DICE TDD data model using Typed Mind Maps. 79
5.2.2 A Widget for Evaluating Learner''s Kolb Learning Style 81
5.2.3 Answer Parsing - Handling C code 83
5.2.3.1 Parsing C code to its Parse Tree into a TMM 85
5.2.3.2 Retrieving Functions from a C Code to a TMM 85
5.2.3.3 Converting C code to a TDD Test Suite 86
5.2.3.4 System Configuration 86
6 Evaluations 88
6.1 Evaluations of DICE TDD Learning Performance 88
6.1.1.1 DICE versus Non-DICE 88
6.1.1.2 Evaluation 88
6.1.1.3 Research Model 89
6.1.1.4 Experimental Design 90
6.1.1.5 Data Analysis 90
6.1.2 TDD verse Non-TDD 93
6.1.2.1 Evaluation 93
6.1.2.2 Research Model 94
6.1.2.3 Experimental Design 94
6.1.2.4 Data Analysis 95
6.1.2.5 Results 97
6.1.3 TDD versus Non-TDD with Kolb Learning Style 98
6.1.3.1 Evaluation 98
6.1.3.2 Research Model 98
6.1.3.3 Experimental Design 101
6.1.3.4 Data Analysis 101
6.1.4 Comparison with other TDD evaluations 104
6.1.5 Removing the TDD Roadblocks 106
6.2 Comparison with other APASs 106
7 Related work 110
7.1 DICE Adaptive Learning Model (DALM) 110
7.2 A Visual Lambda Calculator using Typed Mind Map 113
7.2.1 Motivating Example 114
7.2.1.1 VLM representation of the Y Combinator 116
7.2.1.2 Lambda Expression Terms. 116
7.2.1.3 Lambda Calculus Operations 118
7.2.2 The Lambda Calculus Flow in VLM 118
7.2.3 The Automatic Combinator Generator in VLM 119
7.2.4 The α-conversion and η-reduction 121
8 Conclusions and Future work 123
8.1 Future Work 123
8.1.1 About DICE 123
8.1.2 About the DICE TDD Model 123
8.1.3 About Typed Mind Maps 123
8.1.4 About DAML 124
8.2 Conclusion 125
REFERENCES 128
A-1: ACM Q476 138
A-2: An answer of Q476 140
A-3: Test suite TMM of Q476 which is generated by DICE 143
A-4: Function TMMs of Q476 which is generated by DICE 144
B-1: ACM Q477 145
B-2: An Answer of Question Q477 147
B-3: Test suite TMM of Q477 which is generated by DICE 150
B-4: Function: TMMs of Q477 which is generated by DICE 151
C-1: Mind Maps of teaching unit ACM 152
C-2: Test Suite C1=Q476 152
C-3: Test Suite C2=Q477 153
H.1 Visual Lambda Expressions in VLM 159
H.2 Operations 163
H.3 The Well-Defined VLM Visual Lambda Expression 166

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