||Fuzzy Data Mining Techniques for the Learning and Study Strategies Inventory
||Department of Computer Science and Information Engineering
||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.
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
188.8.131.52 CART 20
184.108.40.206 CHAID 21
220.127.116.11 ID3 and C4.5 22
2.2.2 Association Rule 23
18.104.22.168 Hash-based algorithm 27
22.214.171.124 Partition-based algorithm 28
126.96.36.199 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
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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