
系統識別號 
U00260301201214160000 
論文名稱(中文) 
發展以模糊技術為基礎具多屬性評估之先備知識診斷系統應用於生物資訊課程 
論文名稱(英文) 
Development of a FuzzyBased Prior Knowledge Diagnostic System with Multiple Attribute Evaluation Applied in a Bioinformatics Course 
校院名稱 
成功大學 
系所名稱(中) 
工程科學系碩博士班 
系所名稱(英) 
Department of Engineering Science 
學年度 
100 
學期 
1 
出版年 
100 
研究生(中文) 
林怡君 
研究生(英文) 
YiChun Lin 
電子信箱 
jellyplum@gmail.com 
學號 
n9895111 
學位類別 
博士 
語文別 
英文 
論文頁數 
71頁 
口試委員 
指導教授黃悅民 口試委員楊錦潭 口試委員游寶達 口試委員朱治平 口試委員張智凱 口試委員陳宗禧

中文關鍵字 
模糊多屬性決策
先備知識
診斷系統
跨領域學科

英文關鍵字 
Fuzzy multiattribute decision making
prior knowledge
diagnostic system
interdisciplinary course

學科別分類 

中文摘要 
先備知識為學習者學習中不可或缺之元素，其能夠影響學習者如何去解釋、組織、同化與吸收即將學習的知識或技能，文獻更指出，於學習過程中，學習者若能擁有足夠的先備知識，則能夠提升其學習動機與成效。因此，為了能夠於教學前瞭解學習者之先備知識程度，一般而言，測驗為常被教師使用的方式，然而傳統的測驗往往只會於測驗結果中提供一分數來讓教師與學習者瞭解其測驗結果，如此的方式可能會造成教師與學習者無法清楚的瞭解問題之所在。為此，先前的研究提出一先備知識測量與診斷系統來協助診斷學習者之先備知識能力，進而提升與強化其所需之先備知識，以面對即將來臨的課程；雖然研究結果顯示應用該系統能有效改善學習者的學習成效，但由於該系統僅以測驗中之單一屬性來診斷學習者之先備知識，忽略了此問題其實應為多屬性決策，並且各屬性之重要性往往會隨著教學情境的不同而有所變化，基於此，本研究應用模糊多屬性決策之技術，發展一增強式學習診斷系統來克服上述之問題。為評估此系統，本研究將其應用於實際教學情境中來探討其對於教學成效之影響，實驗結果顯示出本研究所提出之系統能夠改善學習者之學習動機與提升學習成效，最後，本研究針對先備知識之診斷結果來進行分析，其結果顯示所提出之診斷機制能夠準確的診斷出學習者之先備知識的能力。

英文摘要 
Students learn new instructions well by building on relevant prior knowledge as such knowledge affects how instructors and students interact with the learning materials. Moreover, studies have found that good prior knowledge can enable students to attain better learning motivation, comprehension, and performance. This suggests it is important to assist students in obtaining the relevant prior knowledge, as this can enable them to engage meaningfully with the learning materials. Generally, tests are often used to help instructors assess students’ prior knowledge. Nevertheless, conventional testing approaches usually assign only a score to each student, and this may mean that students are unable to realize their own individual weaknesses clearly. To address this problem, previous work has developed a prior knowledge testing and diagnosis (PKT&D) system to assist instructors and students in diagnosing and strengthening prior knowledge. Although, the PKT&D system has shown its effectiveness in helping students improve their learning performance, past experiences of applying this model also reveal the limits of applying it. One of the major problems of applying the PKT&D system is that it does not consider the necessity of multiple attributes when identifying the learning problems of individual students. Moreover, the importance ratios of these attributes may be varied for different educational contexts or if the instructors have different opinions about them. Therefore, this study applied the Efficient Fuzzy Weighted Average (EFWA) technique to develop a Fuzzy Prior Knowledge Test and Diagnosis (FPKT&D) system with a multiattribute decision making model to deal with these above weaknesses. To demonstrate the usefulness of the FPKT&D system, a quasiexperiment was conducted to evaluate its efficacy with regard to improving teaching and learning performance. Furthermore, an analysis was also performed to investigate how accurately the system can diagnose students’ prior knowledge and thus provide them with appropriate materials to strengthen this.

論文目次 
List of Tables
中文口試合格證明 I
英文口試合格證明 II
摘要 III
Abstract IV
致謝 VI
Chapter 1 Introduction 1
Chapter 2 Related Works 4
2.1 The importance of prior knowledge in learning 4
2.2 Review of the PKD (Prior knowledge Diagnosis) Model 5
2.3 Fuzzy Sets in Multiattribute Decision Making 8
2.3.1 Introduction of fuzzy set theory 9
2.3.2 Fuzzy MADM method 11
2.4 Application of fuzzy techniques in elearning 11
Chapter 3 Methodology 13
3.1 Analysis of decision attributes 14
3.2 Fuzzy Prior Knowledge Diagnosis Model 18
3.3 Illustrative example 24
Chapter 4 System Implementation 32
4.1 System architecture and components 32
4.2 System operation procedures 34
Chapter 5 Experiment 38
5.1 Research instruments, measures, and goals 38
5.2 Experimental design, participants, and procedure 39
Chapter 6 Results 43
6.1 Learning motivation survey 43
6.2 Learning attitude survey 45
6.3 Perceived usefulness survey 47
6.4 Pretest/Posttest evaluation 49
6.5 Interview investigation 51
6.5.1 Perceptions of the instruction 54
6.5.2 Perceptions of the interaction 55
6.5.3 Perceptions of the technology 55
Chapter 7 Diagnosis evaluation of FPKT&D system 57
7.1 Evaluation design 57
7.2 Correctness rate analysis 57
Chapter 8 Conclusions and Suggestions 59
8.1 Contribution of the FPKT&D system to interdisciplinary learning 59
8.2 Further applications of FPKT&D system for educators 61
8.3 Limitations and future work 61
References 63
List of Tables
Table 1 Difficulty degree of each test item 14
Table 2 Association between test items and concepts 15
Table 3 Relationships between concepts 16
Table 4 Relationship between students’ answers and test items 17
Table 5 The definitions of membership functions 20
Table 6 Description of EFWA algorithm (Lee and Park, 1997.) 22
Table 7 Illustrative example of the difficulty of each test item 25
Table 8 Illustrative example of the relationships between test items and concepts 25
Table 9 Illustrative example of the relationships among concepts 25
Table 10 Illustrative example of the relationships between students’ answers and test items 26
Table 11 Illustrative example of the relationships among test items, concepts, and the fourth student’s answers 26
Table 12 The input values related to the fourth student’s answers with regard to the second concept 28
Table 13 Major teaching and learning activities in the bioinformatics course 41
Table 14 ANCOVA results of the learning motivation posttest score among the three groups 44
Table 15 The paired ttest results of learning motivation for the three groups of students 44
Table 16 Student attitudes towards learning bioinformatics 46
Table 17 Experiment group students’ perceptions of using the FPKT&D system 48
Table 18 Pretest ANOVA on knowledge of bioinformatics of the three groups 50
Table 19 The paired ttest results of the learning improvement of the three groups 51
Table 20 Posttest ANOVA on the bioinformatics knowledge of the three groups 51
Table 21 Example interview comments about the three topics 52
Table 22 Evaluation of correctness rate results 58
List of Figures
Figure 1 Membership function of a fuzzy set 9
Figure 2 Triangular membership function 10
Figure 3 The hierarchical structure of the decision problem. 13
Figure 4 The membership functions of the rating level 19
Figure 5 The membership function of relative importance 19
Figure 6 The membership functions of alternatives 21
Figure 7 The resulting membership function 31
Figure 8 The architecture of the FPKT&D system 34
Figure 9 Screenshot of the testsheet development interface 35
Figure 10 Screenshot of the relevant test items selection interface 36
Figure 11 Screenshot of the assessment results for instructors 36
Figure 12 Screenshot of the testing interface 37
Figure 13 Screenshot of the diagnostic results interface for students 37
Figure 14 Experimental process 42

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