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系統識別號 U0002-2406200813522100
中文論文名稱 智慧型學習引導系統-使用心理評量理論及Web 2.0技術
英文論文名稱 Using Psychometric Theories and Web2.0 Technologies to Facilitate Intelligent Tutoring System
校院名稱 淡江大學
系所名稱(中) 資訊工程學系博士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 96
學期 2
出版年 97
研究生中文姓名 楊宣哲
研究生英文姓名 Hsuan-Che Yang
學號 894190023
學位類別 博士
語文別 英文
口試日期 2008-06-12
論文頁數 104頁
口試委員 指導教授-施國琛
委員-趙榮耀
委員-楊錦潭
委員-廖弘源
委員-許輝煌
中文關鍵字 智慧型教學方針評斷  學生問題表  試題反應理論  修定版布魯姆認知分類  學習者分類 
英文關鍵字 Intelligent Tutoring and Evaluation  Student-Problem Chart  Item Response Theory  Revised Bloom's Taxonomy  Learner Cluster 
學科別分類 學科別應用科學資訊工程
中文摘要 本研究利用ADL SCORM及IMS QTI之國際標準作為智慧型學習引導系統的整合基準,加入Bloom教育分類理論、IRT試題反映理論、SP table分析以及Kolb學習型態等多項理論之輔助,提出一個利用AJAX及Web Service技術的智慧型學習引導系統;由課程編輯者製作數位學習教材及試題開始、學習者於學習管理平台(Learning Management System)及測驗管理平台(Assessment Management System)上進行學習及測驗活動,並依據傳統測驗理論收集試題屬性,進而利用測驗之方式來分析受測者的學習能力,藉由資訊科技與多種測驗教育理論之結合,以提供教學設計者以及學習者完善且具有可重複利用之整合式學習環境。本研究針對教育與測驗理論融入資訊科技,透過傳統測驗中選擇題的試題分析與選項分析,提供基礎的試題資訊支援教師教學與測驗。另外一方面,搭配SP Chart分析試題分析與學習者分析,提供教師關於學習者類型的資訊與試題分析類型;提供學習者關於學習者的學習建議。搭配修定版Bloom認知分類,運用兩個向度,知識向度與認知向度。學習者測驗後與學習階段對於學習內容的認知向度與之事向度的瞭解百分比。可以搭配IRT估計學習者能力機制,輔助瞭解學習者學習能力。原先IRT學習者能力無法提供學習者學習到哪些內容,透過修定版Bloom認知分類,可清楚提供教師與學習者學習的狀態。除此之外,以搜尋到的學習者能力資訊與學習知識程度,利用二參數分群(Clustering)技術協助教學過程中的課輔分群、學習風格分群、學習能力分群。
英文摘要 This study bases on the international standards - ADL SCORM (Sharable Content Object Reference Model 2004 4th Edition) and IMS QTI (Question and Test Interoperability v2.0) to construct an interoperability learning environment. Content designers construct learning objects and items with authoring tool, learners keep their learning activities with Learning Management System (LMS), Assessment Management System (AMS), and back end Repository mechanism. We use AJAX (Asynchronous JavaScript and XML) and other Web 2.0 concepts to facilitate our system as a RIA (Rich Internet Application).
This study focus on intelligent tutoring and evaluating functions in e-learning platform. In order to integrate learning technology, education theories and information technology, our system supports the following education and test theorems. 1) Student-Problem Chart analysis test items for teachers and learning suggestions to learners. 2) Revised Bloom’s taxonomy has two cognition dimensions which are cognitive process dimension and knowledge dimension. The knowledge dimension is composed of four levels that are defined as factual, conceptual, procedural, and meta-cognitive. The cognitive process dimension consists of six levels that are defined as remember, understand, apply, analyze, evaluate, and create. 3) Item Response Theory applied the discrimination index, the difficulty index and guessing parameter to estimate learner ability. Revised Bloom’s cognition taxonomy combined the learner’s ability estimated by the Item Response Theory to assist learner clustering which can be used in collaborative learning, learning style grouping and remedy course.
論文目次 Table of Contents

TABLE OF CONTENT I

LIST OF FIGURES III

LIST OF TABLES V

CHAPTER 1 INTRODUCTION 1
1-1. E-LEARNING IN MODERN EDUCATION 5
1-2. INTELLIGENT TUTORING SYSTEM AS CUSTOMIZED TUTORING SYSTEM 6
1-3. MOTIVATION 9
1-4. CHAPTER ORGANIZATION 9

CHAPTER 2 RELATED WORKS 11
2-1. RECENT RESEARCH ON ITS 11
2-2. LITERATURE REVIEW 13
2-2-1. Item Response Theory (IRT) 13
2-2-2. Bloom Taxonomy of Educational Objectives 19
2-2-3. Student Problem Table (S-P Table) & Student Problem and Content Table (S-P-C Table) 21
2-2-4. Kolb’s learning style Inventory 31
2-3. TECHNIQUES REVIEW 32
2-3-1. Web2.0 Technologies 32
2-3-2. ADL Shareable Content Object Reference Model (ADL SCORM) 37
2-3-3. IMS Question and Test Interoperability (IMS QTI) 43

CHAPTER 3 PROPOSED FRAMEWORK & DISCUSSIONS 49
3-1 PROPOSED FRAMEWORK TO REALIZE FOUR MODULES OF INTELLIGENT TUTORING SYSTEM 49
3-2. STANDARDIZED LEARNING ENVIRONMENT BASED ON WEB2.0 53
3-2-1. Learning Objects Authoring System 54
3-2-2. Learning and Assessment Management System 55
3-3. BLOOM TAXONOMY OF LEARNING CONTENT 59
3-4. STUDENT-ITEM-CONTENT MODEL FOR STUDENT CLUSTERING 62
3-5. KOLB’S LEARNING STYLE SUGGESTS ITS TEACHING STRATEGY 70

CHAPTER 4. EXPERIMENTAL RESULTS AND CASE STUDY 73
4-1. ITEMS ANALYSIS TOOL SUPPORTING S-P CHART AND BLOOM COGNITION TAXONOMY 73
4-2. INTEGRATING IRT TO ESTIMATE LEARNING ABILITY WITH S-P CHART 83

CHAPTER 5. CONCLUSIONS AND FUTURE RESEARCH 96

BIBLIOGRAPHY 99


List of Figures
Figure 1 Four subsystems intelligent tutoring systems 7
Figure 2 It is one-parameter model of item characteristic curve which is draw with difficulty 3. 14
Figure 3 It used the same discrimination of Figure 2, but the figure is included of three item characteristic curve with distinct difficulties. 14
Figure 4 It includes three dissimilar discrimination same difficulty item characteristic curves. 14
Figure 5 Characteristic curve 16
Figure 6 Characteristic curve 18
Figure 7 Characteristic curve 19
Figure 8 A Revision of Bloom's Taxonomy of Educational Objectives 21
Figure 9 Kolb’s Learning Styles 31
Figure 10 The History of SCORM Specification (revised from http://www.adlnet.org/) 38
Figure 11 ASI Model 44
Figure 12 Content Package of QTI example. 45
Figure 13 choice.xml 46
Figure 14 imsmanifest.xml (to continue) 47
Figure 15 imsmanifest.xml 48
Figure 16 An architecture in study 50
Figure 17 Realization for four modules in ITS 52
Figure 18 Standardized learning environment based on Web2.0 Architecture 53
Figure 19 Online Authoring System 55
Figure 20 Online Assessment Platform 56
Figure 21 Http communication logs 57
Figure 22 AJAX based delivery engine communication sequence 59
Figure 23 Correct response rate vs. Understanding rate 69
Figure 24 Two groups 69
Figure 25 Three groups 70
Figure 26 System Architecture for items analysis tool 73
Figure 27 Diagnosis analysis chart of Test Items 75
Figure 28 Diagnosis analysis chart of Students 75
Figure 29 Assessment design 77
Figure 30 Creating test item 78
Figure 31 Assessment attribute with Bloom classification 79
Figure 32 Test item diagnostic and analysis diagram 79
Figure 33 Students diagnostic and analysis diagram 80
Figure 34 target area for ideal curve 83
Figure 35 analysis tool 92
Figure 36 Class A 94
Figure 37 Class B 94
Figure 38 Students who answer questions correct disparity curves in class A and class B 95


List of Tables
Table 1 Calculations for the one-parameter model, b = 1.0 16
Table 2 Calculations for the two-parameter model, b = 1.0, a = 0.5 17
Table 3 Calculations for the three-parameter model, b = 1.5, a = 1.3, c = 0.2 19
Table 4 Student-Problem Table 26
Table 5 Course-Problem Table 27
Table 6 Course-Content table 27
Table 7 Content related information of each problem 28
Table 8 Revised Course-Problem Chart 29
Table 9 Two students selected for the example 29
Table 10 Student-Course Table 30
Table 11 Cognition level and learning concept analysis (part 1) 60
Table 12 Cognition level and learning concept analysis (part 2) 60
Table 13 Cognition level and learning concept analysis (part 3) 61
Table 14 Cognition level and learning concept analysis examples 61
Table 15 item difficulty, discrimination and response 65
Table 16 First Round Calculation 66
Table 17 Second Round Calculation 66
Table 18 Third Round Calculation 66
Table 19 Correct Response Rate 68
Table 20 Understanding Rate 68
Table 21 Learning characteristics corresponding to learning activity and digital materials 71
Table 22 The learning objects corresponding to four learning styles 72
Table 23 the evaluation standard chart of item discrimination index 76
Table 24 Student questionnaire for feedback 81
Table 25 S-P table to Class A -the P curve -- the S curve 82
Table 26 S-P table to Class B -the P curve -- the S curve 82
Table 27 Sorted S-P Chart of 40 students and 20 problems in Chapter 4 & 5 of “Business Data Communication” 85
Table 28 the discrimination index and difficulty gathered in our exam 86
Table 29 ability estimation of student No.B09510028 iteration 1 87
Table 30 ability estimation of student No.B09510028 iteration 2 88
Table 31 ability estimation of student No.B09510028 iteration 3 88
Table 32 ability estimation of student No.B09510028 iteration 4 89
Table 33 Experiment result of the learner ability 91
Table 34 the pretest and posttest in class A and class B 94
Table 35 Students who answer questions correct in class A and class B 95
參考文獻 Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., et al. (2001). A Taxonomy for Learning, Teaching, and Assessing — A Revision of Bloom's Taxonomy of Educational Objectives. Addison Wesley Longman, Inc.
Aroyo, L., Graesser, A., & Johnson, L. (2007). Intelligent Educational Systems of the Present and Future. IEEE Intelligent Systems , 22 (4), 20-21.
Bloom, B. S. (1956). Taxonomy of Educational Objectives: The Classification of Educational Goals. Susan Fauer Company, Inc.
Chen, D.-J., Lai, A.-F., & Liu, I.-C. (2005). The Design and Implementation of a Diagnostic Test System Based on the Enhanced S-P Model. Journal of Information Science and Engineering , 21, pp. 1007-1030.
Chen, S.-B., Hsu, Y.-C., Hu, C., & Lin, Y.-c. (2006). Applying S-P Chart Analysis as Feedback Mechanism in SCORM Assessment Material. Proceeding of International Plugfest II. Tamsui, Taiwan: Tamkang University.
Cohen, R. j., Montague, P., Nathanson, L. S., & Swerdlik, M. E. (1988). Psychological testing: An introduction to tests and measurement. Mountain View: Mayfield.
Common Cartridge Alliance. (2007). IMS Common Cartridge. Retrieved 6 6, 2008, from IMS Global Learning Consortium: http://www.imsglobal.org/commoncartridge.html
Conklin, J. (1987). Hypertext: An Introduction and Survey. IEEE Computer , 20 (9), pp. 17-41.
Consortium, T. E. (2002). Elvira: An environment for creating and using probabilistic graphical models. Proceeding of 1st Eur. Workshop PGM, (pp. 1–11). Cuenca, Spain.
DaiC.Y., ChengJ.K., SungJ.K., & HoC.P. (2005). An Applied Model of SP Chart in the Technological and Vocational Schools Entrance Examination, Redesigning Pedagogy: Research, Policy, Practice. Proceeding of International Conference on Education. Singapore: Nanyang Technological University.
Dashboard. (2006). Retrieved from Apple Inc.: http://www.apple.com/macosx/leopard/dashboard.html
Ebel, R. L., & Frisbie, D. A. (1991). Essentials of educational measurement (5th edition). Englewood Cliffs: Prentice-Hall.
Farance, F., & Tonkel, J. (2001). Draft Standard for Learning Technologies. Learning Technology Systems Architecture (LTSA). IEEE Inc.
Garret, J. J. (2005, 2 18). Ajax: A New Approach to Web Applications. Retrieved 6 1, 2008, from Adaptive Path: http://www.adaptivepath.com/publications/essays/archives/000385.php
Goh, G. M., & Quek, C. (2007, 1). EpiList: An Intelligent Tutoring System Shell for Implicit Development of Generic Cognitive Skills That Support Bottom-Up Knowledge Construction. IEEE Transactions on Systems, Man and Cybernetics , 1 (37), pp. 58-71.
Gullikson, H. (1987). Theory of mental tests. Hillsdale: Lawrence Erlbaum Associates.
Hambleton, R. K., & Swaminathan, H. (1985). Item response theory: Principles and applications. Boston: Kluwer-Nijhoff.
Hinchcliffe, D. (n.d.). The State of Web 2.0. Retrieved from http://web2.wsj2.com/the_state_of_web_20.htm
Ho, R.-G., & Yen, Y.-C. (2005, 5). Design and Evaluation of an XML-Based Platform-Independent Computerized Adaptive Testing System. IEEE TRANSACTIONS ON EDUCATION , 48 (2).
Hwang, G.-J., Yin, P.-Y., Hwang, G.-H., & Chan, Y. (2005). A Novel Approach for Composing Test Sheets from Large Item Banks to Meet Multiple Assessment Criteria. Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies.
HwangGwo-Jen. (2003). A Test-Sheet-Generating Algorithm for Multiple Assessment Requirements. IEEE Transactions on Education, 46 (3).
Kazi, S. A. (2004). A Conceptual Framework for Web-based Intelligent Learning Environments using SCORM-2004. Proceedings of the IEEE International Conference on Advanced Learning Technologies.
Kelly, T. L. (1939). The Selection of Upper and Lower Groups for the Validation of Test Items. Journal of Educational Psychology , 30, pp. 17-24.
Khan, B. H. (2001). Web-based Training. Englewood Cliffs, NJ: Educational Technology Publications.
Kirschner, P. A., & Paas, F. G. (2001). Web-enhanced Higher Education: a Tower of Babel. Computer in Human Behavior , 347-353.
Kolb, D. (1984). Experiential Learning. Englewood Cliffs, NJ : Prentice-Hall.
Lacave, C., Luque, M., & Diez, F. (2007, 8). Explanation of Bayesian Networks and Influence Diagrams in Elvira. IEEE Transactions on Systems, Man, and Cybernetics , 37 (4), pp. 952-965.
Leung, E., & Li, Q. (2007, 11). An Experimental Study of a Personalized Learning Environment Through Open-Source Software Tools. IEEE Transactions on Education , 50 (4), pp. 331-337.
Lin, N. H., Chang, W., Shih, T. K., & Keh, H. (2005). Courseware Development Using Influence Diagram Supporting e-Learning Specification. Journal of Information Science and Engineering , 21 (5), pp. 985-1005.
Lytras, M. (2002). E-learning Pedagogy: the reveal of value adding learning processes. Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 1826-1830). Chesapeake: AACE.
Michell, J. (1997). Quantitative science and the definition of measurement in psychology. British Journal of Psychology .
Mitrovic, A., Martin, B., & Suraweera, P. (2007). Intelligent Tutors for All: The Constraint-Based Approach. IEEE Intelligent Systems , 38-45.
Murray, T., Shen, T., Piemonte, J., Condit, C., & Tibedau, J. (2000). Adaptively for conceptual and narrative flow in hyper books: The Meta-link system, Adaptive Hypermedia and Adaptive Web-based system. Lecture Notes on Computer Science , 1892, pp. 155-166.
PaulsenM.F. (2003). Online education and learning management systems: global e-learning in a Scandinavian perspective. Information Research, 9 (2), 337.
Sato, T. (1985). Introduction to S-P Curve Theory Analysis and Evaluation. Tokyo, Japan: Meiji Tosho.
Sato, T. (1980). The S-P Chart and the Caution Index. Tokyo, Japan: Nippon Electric Co., Ltd.
SatoT. (1975). The Construction and Interpretation of the S-P Table-Instructional Analysis and Learning Diagnosis. Tokyo, Japan, Meiji Tosho.

Shih, T. K., Lin, N. H., & Chang, H.-P. (2003). An Intelligent E-Learning System with Authoring and Assessment Mechanism. Proceeding of the17th International Conference on Advanced Information Networking and Applications, (pp. 782-787).
Simsek, H., & Akpmar, Y. (2005). Overcoming Scormification Difficulties in Implementing a Learning Content Management System. Proceeding of ITHET 6th Annual International Conference.
TsirigaVictoria, & VirvouMaria. (2003). Initializing Student Models in Web-based ITSs: a Generic Approach. Proceedings of the The 3rd IEEE International Conference on Advanced Learning Technologies.
Weber, G. (1999). Adaptive learning systems in the World Wide Web. Proceeding of Seventh International Conference of User Modeling , pp. 371-377.
Yahoo! Widgets. (2006). Retrieved 6 1, 2008, from Yahoo!: http://widgets.yahoo.com/
Yamanoi, K. (2007). The preparation of the S-P Table. Retrieved 6 6, 2008, from http://www.kasei.ac.jp/cs/Yamanoi/Program/sphyo/index-e.html
Yu, M. (2002). Educational Assessment and Evaluation. Taiwan: The Profile of Psychological Publishing Co., Ltd.


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