適性化數位學習已被證實有助於提升學生的學習效益，在此環境下提供學習教材、策略與數位課程應以學習類型為依據；因此，實現適性化數位學習環境則須考量二個必要程序－（1）分析：辨識學習者所屬類型，以及（2）評估：驗證所提供之教材、策略與課程之可行性。
針對學習者類型之辨識與分析，本論文提出了一個學習類型分類方法，此方法提升了k個最近鄰居（k-nearest neighbor, k-NN）分類法（classification）並結合基因演算法（genetic algorithms, GA），以學生的行為特徵為依據藉以分類並辨識其學習類型。為展示所提分類法之可行性，國小學童的學習行為特徵需要事先收集以提供驗證之用；由驗證結果可看出所提分類法確實可有效分類並識別學生之學習類型。
然而，在收集實際的學生資料為實驗之需求時卻發現了兩項缺點：(1)建立一個數位學習系統以供收集行為樣本(behavioral patterns)之用須要花費大量時間與人力成本，且(2)收集大量行為樣本的過程常需耗費多時。因此，為因應驗證與評估可行性之需求，本論文則提出學習行為派翠網路(Learning Behavioral Petri Nets, LBPN)以供塑模(model)學生在數位學習環境中的學習行為；本論文所提學習行為派翠網路延伸了彩色派翠網路(Colored Petri Nets, CPN)之色彩標記(colored token)以識別學生與學習內容，並加入了時間變數(time variable)以表現多位學生之不同學習時間；經驗證後，結果證實基於學習行為派翠網路模組(LBPN-based model)所產生出的行為樣本相當接近實際學生資料，進而可降低收集實驗所需資料之時間與成本。

Adaptive e-learning considers providing correspondingly learning materials, learning strategies and/or e-courses according to students’ learning styles, which has been indicated that it is a critical requirement for promoting the learning performance of students. For achieving adaptive e-learning environments, two necessary processes are (1) “analysis,” in which a student’s learning style needs to be indentified, and (2) “evaluation,” in which the provided materials, strategy and e-course need to be verified the availability.
For the analysis and the identification of students’ learning styles, this dissertation proposes a learning style classification approach to classify and then identify learning styles according to students’ behavioral features, which improves k-nearest neighbor (k-NN) classification and combines it with genetic algorithms (GA). To demonstrate the viability of the proposed classification approach, the learning behavioral features of elementary school students are obtained and then are classified by the proposed classification approach. The experimental results indicate that the proposed classification approach can effectively classify and identify students’ learning styles.
In the process of collecting the actual data for the experiments, this dissertation observes that (1) building an e-learning system for collecting behavioral patterns requires a great deal of time and effort, and (2) collecting the sufficiency of behavioral patterns is often time-consuming. Hence, for the evaluation and the verification of availability, this dissertation proposes the Learning Behavioral Petri Nets (LBPN) to model learning behavior in e-learning environments, which extends the colored tokens of Colored Petri Nets to identify students and learning contents, and raises the time variable to represent diverse learning times for individual students. The experimental results illustrated in this dissertation confirm that the generated behavioral pattern based on the LBPN-based model not only is very close to actual data, but also substantially reduces the time and cost spent to collect experimental data.

Advanced Distributed Learning (ADL) (2004) Shareable Content Object Reference Model (SCORM), web site at http://www.adlnet.org/
Aviation Industry Computer Based Training Committee (AICC) (2003) web site at http://www.aicc.org
IEEE Learning Technology Standards Committee (LTSC) (2002) web site at <http://ltsc.ieee.org/wg12/>
Instructional Management System (IMS) (2003) web site at http://www.imsproject.org/
Alexander, B. (2006) A new way of innovation for teaching and learning. Educause Review, 41 (2), 32-44.
Back, B., Laitinen, T. & Sere, K. (1996) Neural networks and genetic algorithms for bankruptcy predictions. Expert Systems with Applications, 11(4), 407-413.
Barker, K. (2002) E-learning in three step. In School business affairs. Available http://www.asbointl.org.
Bashirov, R. & Crespi, V. (2006) Analyzing permutation capability of multistage interconnection networks with Colored Petri nets. Information Sciences, 176(21), 3143-3165.
Beasley, R. E. & Waugh, M. L. (1997) Predominant initial and review patterns of navigation in a fully constrained hypermedia hierarchy: An empirical study. Journal of Educational Multimedia and Hypermedia, 6(2), 155-172.
Brusilovsky, P. (1999) Adaptive and intelligent technologies for web-based education. Künstliche Intelligenz, 4, 19-25.
Brusilovsky, P. (2001) Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11 87-100.
Brusilovsky, P., Eklund, J. & Schwarz, E. (1998) Web-based education for all: a tool for development adaptive courseware. Computer Networks and ISDN Systems, 30(1-7), 291-300.
Brusilovsky, P. & Maybury, M. T. (2002) From adaptive hypermedia to the adaptive web. Communications of the ACM, 45(5), 30-33.
Bucklin, R. E. & Sismeiro, C. (2003) A model of web site browsing behavior estimated on clickstream data. Journal of Marketing Research 40(3), 249-267.
Cai, L., Mei, T., Sun, Y., Sun, L. & Ma, Z. (2006) Modeling and analyzing multi-agent task plans for intelligent virtual training system using Petri nets. In Proceeding of The Sixth World Congress on Intelligent Control and Automation (WCICA2006), 1, 4766-4770. Dalian, China.
Carver, C. A., Howard, R. A. & Lane, W. D. (1999) Addressing different learning styles through course hypermedia. IEEE Transactions on Education, 42(1), 33-38.
Castaneda, D. A. (2007) The Effects of Wiki-and Blog-technologies on the Students' Performance When Learning the Preterite and Imperfect Aspects in Spanish. Unpublished Doctoral, West Virginia University, Morgantown.
Chang, C. I. F. (2002) Intelligent assessment of distance learning. Information Sciences, 140(1-2), 105-125.
Chang, Y. C., Chang, C. P., Chiu, C. H., Chen, Y. C. & Chu, C. P. (2005) Constructing a SCORM-compliant intelligent strategy repository. Lecture Notes in Computer Science (LNCS), 3583, 157-162.
Chang, Y. C. & Chu, C. P. (2009) Applying Learning Behavioral Petri Nets to the analysis of Learning Behavior in Web-based Learning Environments. Information Sciences. Doi: http://dx.doi.org/10.1016/j.ins.2009.11.022,
Chang, Y. C., Huang, Y. C. & Chu, C. P. (2009) B2 model: A browsing behavior model based on high-level Petri nets to generate behavioral patterns for e-learning. Expert Systems With Applications, 36(10), 12423-12440.
Chang, Y. C., Kao, W. Y., Chu, C. P. & Chiu, C. H. (2009) A learning style classification mechanism for e-learning. Computers & Education, 53(2), 273-285.
Chang, Y. C. & Liao, H. C. (2005) EMM: an event-driven mobility model for generating movements of large numbers of mobile nodes. Simulation Modelling Practice and Theory, 13(4), 335-355.
Chen, A. P. & Chen, M. Y. (2006) Integrating extended classifier system and knowledge extraction model for financial investment prediction: An empirical study. Expert Systems with Applications, 31(1), 174-183.
Chen, C. M. (2008) Intelligent web-based learning system with personalized learning path guidance. Computers & Education 51(2), 787-814.
Chen, C. M., Lee, H. M. & Chen, Y. H. (2005) Personalized e-learning system using Item Response Theory. Computers & Education, 44(3), 237-255.
Chen, J. H., Yen, Z. H. & Ho, S. Y. (2004) Design of optimal nearest neighbor classifier using an intelligent multi-objective evolutionary algorithm. Lecture Notes in Computer Science (LNCS), 3157, 262-271.
Chen, J. N., Huang, Y. M. & Chu, W. C. (2005) Applying dynamic fuzzy petri net to web learning system. Interactive Learning Environments, 13(3), 159-178.
Chen, L. H. (2009) Web-based learning programs: Use by learners with various cognitive styles. Computers & Education, Article In Press.,
Chiu, C. H., Chuang, C. H., Hsiao, H. F. & Yang, H. Y. (2009) Exploring the patterns of computer mediated synchronous collaboration by elementary school students. Appear to Computers in Human Behavior,
Chu, C. P., Chang, Y. C. & Lin, D. S. (2006) SCORM-Compatible Learning Management System Based on Peer to Peer Infrastructure. In Proceeding of the International Computer Symposium 2006 (ICS 2006), 3, 1509-1514.
Chu, C. P., Chang, Y. C. & Tsai, C.-C. (2009) PC2PSO: Personalized e-course composition based on Particle Swarm Optimization Applied Intelligence, DOI: <http://dx.doi.org/10.1007/s10489-009-0186-7>,
De Jong, K. A. (1975) An analysis of the behavior of a class of genetic adaptive systems. University of MichiganAnn Arbor, MI.
Dunn, R., Dunn, K. & Price, G. E. (1984) Productivity environmental preference survey. Lawrence, KS: Price Systems.
Dwyer, M. B. & Clarke, L. A. (1996) A compact Petri net representation and its implications for analysis. IEEE Transactions on Software Engineering, 22(11), 794-811.
Felder, R. & Silverman, L. (1988) Learning and teaching styles. Journal of Engineering Education, 78(7), 674-681.
Ferdig, R. E. (2007) Editorial: Examining Social Software in Teacher Education. Journal of Technology and Teacher Education, 15(1), 5-10.
Fu, Y. & Shen, R. (2004) GA based CBR approach in Q&A system. Expert Systems with Applications, 26(2), 167-170.
García, P., Amandi, A., Schiaffino, S. & Campo, M. (2007) Evaluating Bayesian Networks' precision for detecting students’ learning styles. Computers and Education, 49(3), 794-808.
Goldberg, D. E. (1989) Genetic Algorithms in Search, Optimization & Machine Learning. Addison- Wesley.
Graf, S. & Kinshuk, P. (2006) An approach for detecting learning styles in Learning Management Systems. In Proceeding of the Sixth International Conference on Advanced Learning Technologies (ICALT'06), 161-163.
Gregorc, A. F. & Ward, H. B. (1977) Implications for learning and teaching. A new definition for individual. NASSP Bulletin, 61(406), 20-26.
Harasim, L. (2000) Shift happens: online education as a new paradigm in learning. The Internet and Higher Education, 3(1-2), 41-61.
Hernandez-Leo, D., Bote-Lorenzo, M. L., Asensio-Perez, J. I., Gocmez-Sanchez, E., Villasclaras-Fernandez, E. D., Jorrin-Abellan, I. M. & Dimitriadis, Y. A. (2007) Free-and Open-Source Software for a Course on Network Management: Authoring and Enactment of Scripts Based on Collaborative Learning Strategies. IEEE Transactions on Education, 50(4), 292-301.
Holland, J. H. (1975a) Adaptation in Natural and Artificial System. The University of Michigan PressAnn Arbor.
Holland, J. H. (1975b) Adaptation in Natural and Artificial System. The University of Michigan Press, Ann Arbor.
Huang, J., Wang, Y., Tan, T. & Cui, J. (2004) A new Iris segmentation method for recognition. In Proceeding of the 17th International Conference on Pattern Recognition (ICPR 2004), 3, 554-557.
Huang, M. J., Huang, H. S. & Chen, M. Y. (2007) Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach. Expert Systems with Applications, 33(3), 551-564.
Huang, Y. M., Chen, J. N., Huang, T. C., Jeng, Y. L. & Kuo, Y. H. (2008) Standardized course generation process using Dynamic Fuzzy Petri Nets. Expert Systems with Applications, 34(1), 72-86.
Hwang, G. J., Yin, P. Y., Wang, T. T., Tseng, J. C. R. & Hwang, G. H. (2008) An enhanced genetic approach to optimizing auto-reply accuracy of an e-learning system. Computers & Education, 51(1), 337-353.
Jain, A. K., Bolle, R. & Pankanti, S. (1999) Biometrics: Personal identification in a Networked Society. MA: Kluwer, Norwell.
Jensen, K. (1992) Colored Petri Nets: Basic Concepts, Analysis Methods, and Practical Use, vol. 1. Berlin: Springer-Verlag.
Johnson, D. W., Johnson, R. T. & Smith, K. A. (1991) Cooperative Learning: Increasing College Faculty Instructional Productivity. School of Education and Human Development, George Washington University.
Kaplan-Leiserson, E. (2005) Trend: Podcasting in academic and corporate learning. [Electronic Version]. Learning Circuits. Retrieved 26/10/2008, from http://www.astd.org/LC/2005/0605_kaplan.htm.
Keefe, J. W. (1987) Learning styles: Theory and practice. Reston, VA: National Association of Secondary School Principals.
Kolb, D. A. (1976) The Learning Style Inventory: Technical Manual, Boston, Ma.: McBer.
Kolb, D. A. (1984) Learning style inventory. Boston: McBerr.
Koriem, S. M. (2000) A Fuzzy Petri Net tool for modeling and verification of knowledge-based systems. The Computer Journal, 43(3), 206-223.
Kuncheva, L. I. (1995) Editing for the k-nearest neighbors rule by a genetic algorithm. Pattern Recognition Letter, 16(8), 809-814.
Kuncheva, L. I. & Jain, L. C. (1999) Nearest neighbor classifier: Simultaneous editing and feature selection. Pattern Recognition Letters, 20(11-13), 1149-1156.
Lawrence, S. & Giles, C. L. (1998) Searching the World Wide Web. Science, 280(5360), 98-100.
Lee, M. G. (2001) Profiling students' adaptation styles in web-based learning. Computers & Education, 36(2), 121-132.
Li, F., Liu, L. & Shi, Y. (2008) Fuzzy Genetic Algorithm Based on Principal Indices Operation and Quasi-linear Fuzzy Number and Its Performance. International Journal of Innovative Computing, Information and Control, 4(6), 1455-1464.
Liu, X. Q., Wu, M. & Chen, J. X. (2002) Knowledge aggregation and navigation high-level Petri nets-based in e-learning. In Proceeding of the First International Conference on Machine Learning and Cybernetics, 1, 420-425. Beijing, China.
Meng, A., Ye, L., Roy, D. & Padilla, P. (2007) Genetic algorithm based multi-agent system applied to test generation. Computers & Education, 49(4), 1205-1223.
Michalewicz, Z. (1996) Genetic Algorithms+ Data Structures= Evolution Programs. Springer.
Mitchell, M. (1996) An Introduction to Genetic Algorithms. Bradford Books.
Mitchell, T. (1997) Machine Learning. McGraw-Hill Companies.
Moundridou, M. & Virvou, M. (2003) Analysis and design of a Web-based authoring tool generating intelligent tutoring systems. Computers & Education, 40(2), 157-181.
Muhlhauser, M. & Trompler, C. (2002) Learning in the digital age: Paving a smooth path with digital lecture halls. In Proceeding of IEEE 35th Hawaii international conference on system sciences, Honolulu, Hawaii.
Murata, T. (1989) Petri nets: Properties, analysis and applications. Proceedings of the IEEE, 77(4), 541-580.
Ogata, H. & Yano, Y. (2004) Context-Aware Support for Computer-Supported Ubiquitous Learning. In Proceeding of the 2nd IEEE International Workshop on Wireless and Mobile Technologies in Education, JhongLi, Taiwan.
Paechter, M., Maier, B. & Macher, D. (2010) Students' expectations of, and experiences in e-learning: Their relation to learning achievements and course satisfaction. Computers & Education, 54(1), 222-229.
Papanikolaou, K. A., Grigoriadou, M., Magoulas, G. D. & Kornilakis, H. (2002) Towards new forms of knowledge communication: the adaptive dimension of a web-based learning environment. Computers & Education, 39(4), 333-360.
Park, Y. H. & Fader, P. S. (2004) Modeling browsing behavior at multiple websites. Marketing Science, 23(3), 280-303.
Pena, C., Narzo, J., & Rosa, J. (2002) Intelligent agents in a teaching and learning environment on the Web. In Proceeding of the 2nd IEEE International Conference on Advanced Learning Technologies (ICALT2002 ), 9-12.
Peterson, J. L. (1977) Petri Nets. ACM Computing Surveys (CSUR), 9(3), 223-252.
Peterson, J. L. (1981) Petri Net Theory and the Modeling of Systems. NJ, USA: Prentice Hall PTR Upper Saddle River.
Pettenati, M. C., & Ranieri, M. (2006) Informal learning theories and tools to support knowledge management in distributed CoPs. Paper presented at the Innovative Approaches for Learning and Knowledge Sharing, EC-TEL. Workshop Proceeding.
Pettit Iv, R. G. & Gomaa, H. (2004) Modeling behavioral patterns of concurrent software architectures using Petri nets. In Proceeding of Fourth Working IEEE/IFIP Conference on Software Architecture (WICSA2004), 57-66.
Portinale, L. (1997) Behavioral Petri nets: A model for diagnostic knowledge representation and reasoning. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 27(2), 184-195.
Pouyan, A. A. & Reeves, S. (2004) Behavioral modeling for mobile agent systems using Petri nets. In Proceeding of IEEE International Conference on Systems, Man and Cybernetics, 5, 4935-4940.
Quinn, C. (2001) Get ready for m-learning. Training and Development, 20(2), 20-21.
Rada, R. & Murphy, C. (1992) Searching versus browsing in hypertext. Hypermedia, 4(1), 1-30.
Ramchandani, C. (1974) Analysis of asynchronous concurrent systems by timed Petri nets. Massachusetts Institute of TechnologyCambridge, MA.
Rogers, Y., Price, S., Randell, C., Fraser, D. S., Weal, M. & Fitzpatrick, G. (2005) Ubi-learning integrates indoor and outdoor experiences. Communications of the ACM, 48(1), 55-59.
Rothlauf, F. (2006) Representations for Genetic and Evolutionary Algorithms. Springer Verlag.
Sanchez, E., Shibata, T. & Zadeh, L. A. (1997) Genetic Algorithms and Fuzzy Logic Systems: Soft Computing Perspectives. World Scientific.
Schiaffino, S., Garcia, P. & Amandi, A. (2008) eTeacher: Providing personalized assistance to e-learning students. Computers and Education, 51(4), 1744-1754.
Schwartz, L., Clark, S., Cossarin, M., & Rudolph, J. (2004) Educational wikis: Features and selection criteria. International Review of Research in Open and Distance Learning, 5(1)
Sessink, O., Beeftink, R., Tramper, J. & Hartog, R. (2003) Author-defined storage in the next generation learning management systems. In Proceeding of the 3rd IEEE International Conference on Advanced Learning Technologies (ICALT’03), 57-61.
Shang, Y., Shi, H. C. & Chen, S. S. (2001) An intelligent distributed environment for active learning. In Proceeding of the 10th international conference on World Wide Web, 308-315.
Shin, K. S. & Lee, Y. J. (2002) A genetic algorithm application in bankruptcy prediction modeling. Expert Systems with Applications, 23 (3), 312-328.
Der HALB-Test [The HALB test]. <http://arbeitsblaetter.stangl-taller.at/TEST/HALB/> Accessed 18.04.08.
Su, J. M., Tseng, S. S., Chen, C. Y., Weng, J. F. & Tsai, W. N. (2006) Constructing SCORM compliant course based on High-Level Petri nets. Computer Standards & Interfaces, 28(3), 336-355.
Takahata, M., Shiraki, K., Sakane, Y. & Takebayashi, Y. (2004) Sound Feedback for Powerful Karate Training. In Proceeding of the International Conference on New Interfaces for Musical Expression, Hamamatsu, Japan.
Trantafillou, E., Poportsis, A. & Demetriadis, S. (2003) The design and the formative evaluation of an adaptive educational system based on cognitive. Computer and Education, 41(1), 87-103.
Triantafillou, E., Pomportsis, A. & Demetriadis, S. (2003) The design and the formative evaluation of an adaptive educational system based on cognitive styles. Computers & Education, 41(1), 87-103.
Tseng, C. R., Chu, H. C., Hwang, G. J. & Tsai, C. C. (2008) Development of an adaptive learning system with two sources of personalization information. Computers and Education, 51(2), 776-786.
Tzouveli, P., Mylonas, P. & Kollias, S. (2008) An intelligent e-learning system based on learner profiling and learning resources adaptation. Computers & Education, 51(1), 224-238.
Usluel, Y. K. & Mazman, S. G. I. (2009) Adoption of Web 2.0 tools in distance education. Procedia - Social and Behavioral Sciences, 1(1), 818-823.
Uzunboylu, H., Cavus, N. & Ercag, E. (2009) Using mobile learning to increase environmental awareness. Computers & Education, 52(2), 381-389.
Wang, D. (2008a) Fast Constructive-Covering Algorithm for neural networks and its implement in classification. Applied Soft Computing 8(1), 166-173.
Wang, J. (1998) Timed Petri Nets: Theory and Application. Norwell, MA: Kluwer Academic.
Wang, Y. (2008b) Fuzzy Clustering Analysis by Using Genetic Algorithm. ICIC Express Letters, 2(4), 331-337.
Xenos, M. (2004) Prediction and assessment of student behaviour in open and distance education in computers using Bayesian networks. Computers and Education, 43(4), 345-359.
Yao, X. (1999) Evolving artificial neural networks. Proceedings of the IEEE, 87(9), 1423-1447.
Yu, F. Y., Liu, Y. H. & Chan, T. W. (2005) A web-based learning system for question-posing and peer assessment. Innovations in Education and Teaching International, 42(4), 337-348.
Zhang, G., Jin, Q. & Lin, M. (2005) A Framework of Social Interaction Support for Ubiquitous Learning. In Proceeding of the 19th International Conference of Advanced Information Networking and Applications, Tamkang University, Taiwan.
吳弘凱 (2004) 國小學童數位學習擷取課程型為樣式分析. 國立台南大學資訊教育研究所碩士論文,台南.