近年來，隨著高等教育普及化，各大學在提昇學生學習效能的評量及相關研究愈來愈多。然而，在實際執行層面，各校的諮商輔導人力有限，再加上傳統評量工具多為紙筆測驗，因而成效有限。以「讀書與學習策略量表」為例，受測者必須回答十幾個學習策略分量表共87題，填寫起來不僅費時，且容易讓受測者因疲憊而產生排斥感，進而影響作答意願，不易達到預期的目標。
為改善上述情況，本篇論文提出運用模糊理論的資料探勘技術對大學生學習與讀書策略自我評量系統進行研究。採用模糊資料探勘的作法，主要分為二個步驟：首先，根據決策樹分析的樹狀結構作分類，找出評量問卷的各學習策略分項中較有價值或具有關鍵性的題目，直接減少所需回答題目的數量；接著利用關聯性法則，找出學習策略各分項之間的關聯性，間接減少具關聯性策略分項的題目。除此之外，結合模糊集合的運算觀念，將資料探勘所獲得的規則組合成樹狀結構，並將以往從第一題回答至最後一題的傳統作答方式，改變成依照受測者回答的結果，再決定下一題題目的智慧型學習與讀書評量系統。
本研究建置的學習與讀書策略自我評量系統，並不是要取代原來的評量系統，而是利用極少量的題目便可得到與原評量近似的測驗效果。藉由評量問題數量的減少，以增加大學生填寫評量的意願；並透過網頁平台快速進行自我評量，將結果提供給學校諮商輔導員，協助諮商人員找出疑似有學習障礙的高困擾群學生，進一步追蹤輔導，以節省輔導資源與成本，並有效提昇大學生的學習效能。此外，模糊資料探勘作法亦可應用在其他社會科學領域，特別是問卷題目精簡化的相關研究，將極具效益與實用性。

With the popularity of higher education during recent years, universities and colleges have had more and more researches on assessments to enhance students’ learning performance. Practically performed, however majority schools have limited counselors. In addition, traditional assessment is mostly pen-and-paper tests, therefore, the results are restricted. Take the Learning and Study Strategies Inventory (LASSI) for example, exam participants have to answer questions from more than ten scales of study strategy with 87 assessment items, which is time-consuming and easily resulting in student’s resistance, fatigue and unwillingness to complete the assessment. Therefore, it is difficult to reach expected effect.
To improve foregoing situation, in this dissertation, we come up with a fuzzy data mining technique for the LASSI. Two major steps are taken to do so. First step is to extract valuable or critical questions from questionnaires to directly reduce the number of assessment questions for LASSI, according to the classification charts of decision tree analysis. Second step is to find the related scale of study strategy from association rule analysis to indirectly decrease the correlative scale of study strategy and reduce the assessment questions for LASSI. Moreover, by integrating the concepts of fuzzy set theory, the rules discovered by data mining techniques are assembled as tree structure, and the ways in the past to answer questions from the first to the end are changed with students’ answer results and then the results will be evaluated to decide whether further assessment is required. 
A web-based Learning and Study Strategy self-assessment system (Web-LSA) is developed in this dissertation. It is not to replace the original LASSI assessment but to minimize the assessment questions for approximate result. Fewer questions and the web-based system will enhance students’ willingness to more quickly do self-assessment. The results of the self-assessment will be provided counselors to help in finding high-risk students with study disturbances. Therefore, counselors can pay their attention only on these students, which can not only cut down human resource and counseling cost, but make student’s learning performance more efficiently as well. Furthermore, fuzzy data mining techniques can also be applied to social scientific researches, and can be especially efficient and practical in simplifying the assessment questions.

Abstract I
Contents  IV
List of Figures  VII
List of Tables  VIII

Chapter 1 Introduction  1
1.1 Research Motivation of This Dissertation  1
1.2 Research Objectives of This Dissertation  2
1.3 Organization of This Dissertation  4

Chapter 2 Background Knowledge  5
2.1 Learning and Study Strategy Scale Inventory for University Students  5
2.1.1 Introduction to the LASSI Scales  8
2.1.2 Scoring to the LASSI  12
2.2 Data Mining Technology  15
2.2.1 Decision Tree  17
2.2.1.1 CART  20
2.2.1.2 CHAID  21
2.2.1.3 ID3 and C4.5  22
2.2.2 Association Rule  23
2.2.2.1 Hash-based algorithm  27
2.2.2.2 Partition-based algorithm  28
 2.2.2.3 FP-growth algorithm  29
2.3 fuzzy set concepts  30
2.3.1 crisp set theory  31
2.3.2 Fuzzy set theory  32

Chapter 3 Fuzzy Data Mining Method  38
3.1 Overview   38
3.2 Preprocess of survey data  40
3.3 Decision tree analysis   43
3.4 Selecting candidate items for LASSI Scales  44
3.5 Association analysis  45
3.6 Prioritizing the LASSI scales  49

Chapter 4 Implementation of Web-LSA  56
4.1 Web-based self-assessment for the university students 58
4.2 Guidance-support system for the counselors  61 
Chapter 5 Performance Evaluations  63 
5.1 Reduction of items  63
5.1.1 Applying decision tree analysis results  63
5.1.2 Applying fuzzy method analysis results  67
5.2 Experimental results analysis  69
5.2.1 Efficacy of applying decision tree  69
5.2.2 Efficacy of applying fuzzy method  73

Chapter 6 Conclusions and Future Directions  77
6.1 Conclusions  77
6.2 Future Directions  78

Bibliography 79

Vita 96 

Figure 2.1 An example of decision tree  19
Figure 2.2 A comparison of fuzzy set and crisp set  30
Figure 2.3 The membership function of trapezoidal  34
Figure 3.1 The LASSI data mining processing  40
Figure 3.2 The results from decision tree analysis for motivation scale  44
Figure 3.3 The tree graph of motivation scale  45
Figure 3.4 Parts of association rules produced by association analysis  47
Figure 3.5 The association rules tree  54
Figure 4.1 The framework of the Web-LSA system  56
Figure 4.2 The system flowchart of Web-LSA  58
Figure 4.3 LASSI self-assessment system start menu  59
Figure 4.4 Sample of the assessing item  59
Figure 4.5 Sample of the assessing results  60
Figure 4.6 Sample of the assessing reference  60
Figure 4.7 Interfaces of the Web-LSA Database Query  61
Figure 4.8 Interfaces of the Web-LSA Guidance-support system  62 

Table 2.1 The LASSI Scales  7
Table 2.2 The percentage rank norms for LASSI  14
Table 3.1 Original questionnaire answer table  41
Table 3.2 Original LASSI scales scoring table  42
Table 3.3 LASSI Scales Self-assessment converted table  43
Table 3.4 The processed rules from association rule analysis  49
Table 3.5 The correlation of all study strategies for poor association  51
Table 3.6 The correlation of eight scales (second prioritizing) 52
Table 3.7 The correlation of six scales (third prioritizing)  53
Table 3.8 The correlation of three scales (fourth prioritizing)  53
Table 5.1 Necessary items of LASSI based on decision tree analysis  66
Table 5.2 Necessary items of LASSI based on Fuzzy Method  68
Table 5.3 Necessary items for decision tree prediction  70
Table 5.4 Accuracy of decision tree prediction  72
Table 5.5 Necessary items for Fuzzy Method prediction  74
Table 5.6 Accuracy of Fuzzy Method prediction  76

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