隨著各行各業的資訊化以及網路化快速腳步帶動資訊服務業的快速成長，近年來對程式設計資訊人才需求快速增加。資訊人才不僅是國家發展資訊軟體產業的關鍵，也是影響我國知識產業成長的重要因素之一，因此培養一位優秀的程式設計人才以提升我國產業的競爭力，是目前資訊教育的目標之一。程式設計對於資訊科學相關科系的學生而言，是一門重要的基礎學科，但是對某些學生而言想要學好程式語言卻不是一件容易的事。除了學校的程式語言課程學習外，學生也希望能在課外找到一些輔助教材、甚至是學習平台以加強學習。近幾年來，雖然有些允許教師建立學習教材並且推薦學習路徑的數位學習平台逐漸出現，但是不需領域專家事先設定，而能自動化提供學習路徑以及蒐集補充教材的學習系統，仍是相當地稀少。
本研究旨在於建立一套有效的系統運作模式，能夠根據學習者的錯誤概念自動提供適合個人的學習路徑以及補救教材，並建立一以Moodle 為基礎之個人化補救學習系統(Moodle-based Personalized Remedial Learning System, MPRLS)來輔助學習者進行補救學習。系統以一C++程式語言學習課程為範例來運作，以測試其可行性及評估
其效能。系統之運作模式可根據學習者在課程學習後的測驗所顯露之錯誤概念，利用模糊理論建立一條適合學習者的學習路徑，並於學習路徑上的每一個概念自動蒐集提供最合適的補救教材，藉以達到有效的補救學習。
本研究所進行數個實驗之驗證結果可以證實MPRLS 個人化補救學習系統的架構確實能夠有效的提供學習者完整且多樣的學習教材，其個人化之學習機制，更能有效的輔助學習者進行適性化之輔助學習，並提升學習者的學習成效。

The trend toward digitization and networking in all walks of life serves to expedite the rapid growth of the information industry, which, in turn, in recent years, boosts the demand for computer programming professionals in a large scale. These professionals not only play a key role in the development of national information infrastructure and software industries, but also have a significant influence on the success of knowledge industry. Therefore, one of the objectives in information science education is to train professionals specializing in computer programming so as to improve industry competitiveness. For many information science students, a programming language, though a fundamental course, is far from easy to master. Besides course materials, they may try to find more auxiliary ones to intensify their comprehension of computer programming techniques. In recent years, although some
remedial e-learning platforms appear and claim to be able to construct learning paths based on predefined classes and hierarchically organized learning contents, few of them, without expert interference, can provide learners with learning paths constructed dynamically and associated learning contents collected automatically.
The purpose of this study is to establish an operational model which can, based on a learner’s misconceptions, automatically plan and offer a personalized learning path with auto-collected remedial learning contents. Based on the established operational model, this study builds a Moodle-based Personalized Remedial Learning System (MPRLS) to assist learners in remedial learning and uses a C++ programming course as a testing platform for
feasibility and performance evaluation. The operational model harnesses the fuzzy logic theory to auto-construct a learning path, along which, MPRLS will auto-collect remedial
materials for each associated concept, in attempting to correct a learner’s misconceptions found in a pre-test for each course topic. When collecting the materials, MPRLS considers also a learner’s preference and learning style to facilitate more efficient remedial learning.
MPRLS, confirmed by the results of several conducted evaluation experiments in this study, offers a comprehensive and stable personalized remedial learning environment for learners of any e-learning courses. The analysis of learners’ achievements also confirms the
feasibility and the performance of the operational model in remedial and adaptive learning.

Bibliography

[1] D. P. Ausubel, “Educational psychology: A cognitive view,” New York: Holt, Rinehart & Winston, 1968.
[2] R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in large databases,” In Proceedings of the ACM SIGMOD Conference on Management of Data, pp. 207-216, 1993.
[3] R. Agrawal, and R. Srikant, “Fast algorithms for mining association rules,” In Proceedings of 20th International Conference on Very Large Data Bases, pp. 487-499, 1994.
[4] H. Alomyan, “Individual differences: Implications for web-based learning design,” International Education Journal, Vol. 4, no. 4, pp. 188-196, 2004.
[5] P. Brusilovsky, “Adaptive navigation support in education hypermedia: the role of student knowledge level and the case for meta-adaptation,” British Journal of Educational Technology, Vol. 34, no. 4, pp. 487-497, 2003.
[6] P. Brusilovsky, “Adaptive hypermedia: from intelligent tutoring system to web-based education,” In Proceedings of the 5th International Conference on Intelligent Tutoring Systems, Springer-Verlag, 2000.
[7] P. Brusilovsky, “Adaptive hypermedia,” User Modeling and User Adapted Interaction, 11 (1/2), pp. 87-110, Kluwer Academic Publishers, 2001.
[8] P. Brusilovsky and L. Pesin, “Visual annotation of links in adaptive hypermedia,” In Proceedings of Conference Human Factors in Computing Systems (CHI’95), pp. 222-223, 1995.
[9] B. Beldagli, and T. Adiguzel, “Illustrating an ideal adaptive e-learning: A conceptual framework,” Procedia-Social and Behavioral Science, Vol. 2, no. 2, pp. 5755-5761, 2010.
[10] P. De Bra, P. Brusilovsky, and G. J. Houben, “Adaptive Hypermedia: From Systems to Framework,” ACM Computing Surveys, Vol. 31, no. 4, 1999.
[11] B. Bontchev and D. Vassileva, “Towards automatic construction of adaptable courseware storyboards,” In Proceedings of CSEDU International Conference on Computer Supported Education, 2010.
[12] J. Bergasa-Suso, D. A. Sanders, and G. E. Tewkesbury, “Intelligent browser-based systems to assist Internet users,” IEEE Transactions on Education, Vol. 48, no. 4, pp. 580-585, 2005.
[13] P. D. Boucher-Ryan and D. Bridge, “Collaborative recommending using formal concept analysis,” Knowledge-Based Systems, Vol. 19, no. 5, pp. 309-315, 2006.
[14] S. M. Bai and S. M. Chen, “Automatically constructing concept maps based on fuzzy rules for adapting learning systems,” Expert Systems with Applications, Vol. 35, no. 1-2, pp. 41-49, 2008.
[15] J. Conklin, “Hypertext: An introduction and survey,” Computer, Vol. 20, no. 9, pp. 17-41, 1987.
[16] C. M. Chen and C. J. Chung, “Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle,” Computers & Education, Vol. 51, no. 2, pp. 624-645, 2008.
[17] C. M. Chen, C. Y. Liu, and M. H. Chang, “Personalized curriculum sequencing utilizing modified item response theory for web-based instruction,” Expert Systems with Applications, Vol. 30, no. 2, pp. 378-396, 2006.
[18] F. Colace, M. De Santo, and M. Iacone, “Work in progress – ASAP: an automatic student adapted learning path generator,” In Proceedings of 35th Annual Conference Frontiers in Education, pp. 13-14, 2005.
[19] F. Colace and M. De Santo, “Ontology for e-learning: A Bayesian approach,” IEEE Transactions on Education, Vol. 53, no. 2, pp. 223-233, 2010.
[20] C. Carlsson, M. Fedrizzi, and R. Fuller, “Fuzzy Logic in Management,” Kluwer Academy Publisher, 2004.
[21] Y. Cao, and Y. Li, “An intelligent fuzzy-based recommendation system for consumer electronic products,” Expert Systems with Applications, Vol. 33, no. 1, pp. 230-240, 2007.
[22] T. Y. Chang and Y. T. Chen, “Cooperative learning in e-learning: A peer assessment of student-centered using consistent fuzzy preference,” Expert System with Application, Vol. 36, no. 4, pp. 8342-8349, 2009.
[23] C. K. Chiou, Judy C. R. Tseng, G. J. Hwang, and S. Heller, “An adaptive navigation support system for conducting context-aware ubiquitous learning in museums,” Computers & Education, Vol. 55, no. 2, pp. 834-845, 2010.
[24] C. M. Chen, “Intelligent web-based learning system with personalized learning path guidance,” Computers & Education, Vol. 51, no. 2, pp. 787-814, 2008.
[25] C. M. Chen and L. J. Duh, “Personalized web-based tutoring system based on fuzzy item response theory,” Expert Systems with Applications, Vol. 34, no. 4, pp. 2298-2315, 2008.
[26] C. M. Chen, H. M. Lee, and Y. H. Chen, “Personalized e-learning system using item response theory,” Computers & Education, Vol. 44, no. 3, pp. 237-255, 2005.
[27] C. S. Claxton and Y. Ralston, “Learning styles: Their impact on teaching and administration,” AAHE-ERIC/Higher Education Research Report No. 10, Washington, 1978.
[28] Y. C. Chang, W. Y. Kao, C. P. Chu, and C. H. Chiu, “A learning style classification mechanism for e-learning,” Computers & Education, Vol. 53, no. 2, pp. 273-285, 2009.
[29] V. Carchiolo, A. Longheu, and M. Malgeri, “Adaptive formative paths in a web-based learning environment,” Educational Technology & Society, Vol. 5, no. 4, pp. 64-75, 2002.
[30] Y. L. Chi, “Elicitation synergy of extracting conceptual tags and hierarchies in textual document,” Expert Systems with Applications, Vol. 32, no. 2, pp. 349-357, 2007.
[31] L. Curry, “Patterns of Learning Styles across Selected Medical Specialties,” Educational Psychology, Vol. 11, no. (3/4), pp. 247-277, 1991.
[32] CKIP AutoTag, Chinese Knowledge Information Processing Group, Academic sinica, Taiwan, 1998.
[33] T. Dietinger, “Aspects of e-learning environments,” Institute for Information Processing and Computer Supported New Media (IICM), Graz University of Technology A-8010 Graz, Austria, 2003.
[34] B. A. Davey, and H. A. Priestley, “Introduction to Lattices and Order,” Cambridge University Press, pp. 65-84, 2002.
[35] R. Dunn, K. Dunn, & J. Perrin, “Teaching young children through their individual learning styles: Practical approaches for grades K-2,” Boston: Allyn and Bacon, 1994.
[36] R. Dunn and K. Dunn, “Teaching elementary student through their individual learning styles,” Boston: Allyn & Bacon, 1992.
[37] J. Dewey, “Art as Experience,” New York: G. P. Putnam, Capricorn Books, 1934.
[38] A. Formica, “Ontology-based concept similarity in formal concept analysis,” Information Sciences, Vol. 176, no. 18, pp. 2624-2641, 2006.
[39] H. Fazlollahtabar and I. Mahdavi, “User/tutor optimal learning path in e-learning using comprehensive neuro-fuzzy approach,” Educational Research Review, Vol. 4, no. 2, pp. 142-155, 2009.
[40] K. H. D. Foreman, “Cognitive style, Cognitive ability, and the Acquisition of Initial Computer Programming Competence,” In Proceedings of selected research papers presented at the annual meeting of the Association for Educational Communication and Technology, pp. 14-19, 1988.
[41] R. M. Felder and L. K. Silverman, “Learning and teaching Styles in engineering education,” Engineering Education, Vol. 78, no. 7, pp. 674-681, 1988.
[42] F. Ghaleb, S. Daoud, A. Hasna, J. M. ALJa’am, S. A. El-Seoud, and H. El-Sofany, “E-learning model based on semantic web technology,” International Journal of Computing & Information Sciences, Vol. 4, no. 2, 2006.
[43] B. Ganter and R. Wille, R., “Formal Concept Analysis: Mathematical Foundations,” Berlin Heidelberg: Springer-Verlag, 1999.
[44] R. M. Gagne and L. T. Brown, “Some factors in programming of conceptual learning,” Journal of Experimental Psychology, Vol. 62, no. 4, pp. 313-321, 1961.
[45] A. Haji and M. Assadi, “Fuzzy expert systems and challenge of new product pricing,” Computers & Industrial Engineering, Vol. 56, no. 2, pp. 616-630, 2009.
[46] K. A. Hwang and C. H. Yang, “Assessment of affective state in distance learning based on image detection by using fuzzy fusion,” Knowledge-based Systems, Vol. 22, no. 4, pp. 256-260, 2009.
[47] P. Hajek, “What is mathematical fuzzy logic,” Fuzzy Sets and Systems, Vol. 157, no. 5, pp. 597-603, 2006.
[48] C. Hidber, “Online association rule mining,” In Proceedings of the ACM SIGMOD Conference on Management of Data, pp. 145-156, 1999.
[49] M. J. Huang, H. S. Huang, and M. Y. Chen, “Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach,” Expert Systems with Applications, Vol. 33, no. 3, pp. 551-564, 2007.
[50] C. J Huang, M. C. Liu, S. S. Chu, and C. L. Cheng, “An intelligent learning diagnosis system for web-based thematic learning platform,” Computers & Education, Vol. 48, no. 4, pp. 658-679, 2007.
[51] N. Herrmann, “The Creative Brain, Lake Lure,” N.C.: Brain Books, 1990.
[52] P. Honey and A. Mumford, “Manual of Learning Styles,” London: P. Honey, 1992.
[53] J. Ismail, “The design of an e-learning system beyond the hype,” Internet and Higher Education, Vol. 4, no. 3-4, pp. 329-336, 2001.
[54] G. Jiang, K. Ogasawara, A. Endoh, and T. Sakurai, “Context-based ontology building support in clinical domains using formal concept analysis,” International Journal of Medical Informatics, Vol. 71, no. 1, pp. 71-81, 2003.
[55] T. Jun, Y. J. Du, and J. F. Shen, “Research in concept lattice based automatic document ranking,” In Proceedings of Fourth International Conference on Machine Learning and Cybernetics, pp. 5560-5565, 2005.
[56] A. Keles, R. Ocak, A. Keles, and A. Gulcu, “ZOSMAT: Web-based intelligent tutoring system for teaching-learning process,” Expert Systems with Applications, Vol. 36, no. 2, pp. 1229-1239, 2009.
[57] D. M. Kennedy, “Challenges in evaluating Hong Kong students’ perceptions of Moodle,” In Proceedings of the 22nd Annual Conference of ASCILITE, pp. 327-336, 2005.
[58] J. W. Keefe, “Learning style: An overview,” Student Learning Styles: Diagnosing and Prescribing Programs, J. W. Keefe, ed., NASSP, pp. 1-17, 1979.
[59] D. A. Kolb, “Experiential Learning: Experience as the Source of Learning and Development,” Englewood Cliffs, N.J.: Prentice-Hall, 1984.
[60] H. I. Liu and M. N. Yang, “QoL guaranteed adaptation and personalization in e-learning systems,” IEEE Transactions on Education, Vol. 48, no. 4, pp. 676-687, 2005.
[61] J. C. Lin and K. C. Wu, “Finding a fitting learning path in e-learning for juvenile,” In Proceedings of 7th IEEE International Conference on Advanced Learning Technologies, pp. 449-453, 2007.
[62] H. I. Lee, W. C. Chen, and C. J. Chang, “A fuzzy and BSC approach for evaluation performance of IT department in the manufacturing industry in Taiwan,” Expert Systems with Applications, Vol. 34, no. 1, pp. 96-107, 2008.
[63] C. S. Lee, Z. W. Jian, and L. K. Huang, “A fuzzy ontology and its application to news summarization,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol. 35, no. 5, pp. 859-880, 2005.
[64] K. Lewin, “Field Theory in Social Sciences,” New York: Harper & Row, 1951.
[65] N. N. Myat and K. H. S. Hla, “Organizing web document resulting from an information retrieval system using formal concept analysis,” In Proceedings of the 6th Asia Pacific Symposium on Information and Telecommunication Technologies, pp. 198-203, 2005.
[66] N. N. Myat and K. H. S. Hla, “A combined approach of formal concept analysis and text mining for concept based document clustering,” In Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 330-333, 2005.
[67] J. Ma and D. Zhou, “Fuzzy set approach to the assessment of student-centered learning,” IEEE Transactions on Education, Vol. 43, no. 2, pp. 237-241, 2000.
[68] C. Miao, Q. Yang, H. Fang, and A. Goh, “A cognitive application for agent-based personalized recommendation,” Knowledge-based Systems, Vol. 20, no. 4, pp. 397-405, 2007.
[69] Moodle. Modular Object-Oriented Dynamic Learning Environment. Available from http://moodle.org/.
[70] E. Ozpolat and G. B. Akar, “Automatic detection of learning styles for an e-learning system,” Computers & Education, Vol. 53, no. 2, pp. 355-367, 2009.
[71] J. Ohene-Djan, R. Zimmer, M. Gorle, and S. Naqvi, “A personalisable electronic book for video-based sign language education,” Educational Technology & Society, Vol. 6, no. 4, pp. 86-99, 2003.
[72] A. Ortigosa, P. Paredes, and P. Rodriguez, “AH-questionnaire: An adaptive hierarchical questionnaire for learning styles,” Computers & Education, vol. 54, no. 4, pp. 999-1005, 2010.
[73] K. A. Papanikolaou, M. Grigoriadou, G. D. Magoulas, and H. Kornilakis, “Towards new forms of knowledge communication: the adaptive dimension of a web-based learning environment,” Computers & Education, Vol. 39, no. 4, pp. 333-360, 2002.
[74] P. Phobun and J. Vicheanpanya, “Adaptive intelligent tutoring systems for e-learning systems,” Procedia-Social and Behavioral Science, Vol. 2, no. 2, pp. 4064-4069, 2010.
[75] J. Piaget, “Genetic Epistemology,” New York: Columbia University Press, 1970.
[76] Y. Qu, C. Wang, and L. Zhong, “The research and discussion of web-based adaptive learning model and strategy,” Lecture Notes in Computer Science (LNCS), Vol. 5685, Springer-Verlag, pp. 412-420, 2009.
[77] A. M. Roshandeh, O. C. Puan, and M. Joshani, “Data analysis application for variable message signs using fuzzy logic in Kuala Lumpur,” International Journal of Systems Applications, Engineering & Development, Vol. 3, no. 1, pp. 18-27, 2009.
[78] C. Romero, S. Ventura, A. Zafra, and P. de Bra, “Applying web usage mining for personalizing hyperlinks in web-based adaptive educational systems,” Computers & Education, Vol. 53, no. 3, pp. 828-840, 2009.
[79] S. Rafaeli, M. Barak, Y. Dan-Gur, and E. Toch, “QSIA-a Web-based environment for learning, assessing and knowledge sharing in communities,” Computers & Education, Vol. 43, no. 3, pp. 273-289, 2004.
[80] P. Resta, “The internet and university teaching: Will the networking technologies change the teaching-learning process?,” In Proceedings of Symposium on the Effects of the Internet on Education, pp. 7-21, 2000.
[81] Judy C. R. Tseng, H. C. Chu, G. J. Hwang, and C. C. Tsai, “Development of an adaptive learning system with two sources of personalization information,” Computers & Education, Vol. 51, no. 2, pp. 776-786, 2008.
[82] Q. T. Tho, S. C. Hui, A. C. M. Fong, and T. H. Cao, “Automatic fuzzy ontology generation for semantic web,” IEEE Transactions on Knowledge and Data Engineering, Vol. 18, no. 6, pp. 842-856, 2006.
[83] S. R. Terrell, “The effect of learning style on doctoral course completion in a web-based learning environment,” The Internet and Higher Education, Vol. 5, no. 4, pp. 345-352, 2002.
[84] K. H. Tsai, T. K. Chiu, M. C. Lee, and T. I. Wang, “Automated Course Composition and Recommendation based on a Learner Intention,” In Proceedings of the 7th IEEE International Conference on Advanced Learning Technologies, pp. 274-278, 2007.
[85] M. J. Tsai, “The model of strategic e-learning: Understanding and evaluating student e-learning from metacognitive perspectives,” Educational Technology & Society, Vol. 12, no. 1, pp. 34-48, 2009.
[86] A. M. M. S. Ullah, and K. H. Harib, “An intelligent method for selecting optimal materials and its application,” Advanced Engineering Informatics, Vol. 22, no. 4, pp. 473-483, 2008.
[87] D. Vassileva and B. Bontchev, “Self adaptive hypermedia navigation based on learner model characters,” In Proceedings of IADAT-e2006, pp. 46-52, 2006.
[88] T. I Wang, K. T. Wang, and Y. M. Huang, “Using a style-based ant colony system for adaptive learning,” Expert Systems with Applications, Vol. 34, no. 4, pp. 2449-2464, 2008.
[89] C. Wang, D. Z. Wang, and J. L. Lin, “ADAM: An adaptive multimedia content description mechanism and its application in web-based learning,” Expert Systems with Applications, Vol. 37, no. 12, pp. 8639-8649, 2010.
[90] R. Wille, “Restructuring lattice lattice theory: an approach based on hierarchies of concepts,” In: I. Rival (Ed.), Ordered Sets, Reidel, Dordrecht-Boston, pp. 445-470, 1982.
[91] S. S. Weng, H. J. Tsai, S. C. Liu, and C. H. Hsu, “Ontology construction for information classification,” Expert Systems with Applications, Vol. 31, no. 1, pp. 1-12, 2006.
[92] L. E. Winslow, “Programming pedagogy: A psychological overview,” ACM SIGCSE Bulletin, Vol. 28, no. 3, pp. 17-22, 1996.
[93] Y. J. Yang and C. Wu, “An attribute-based ant colony system for adaptive learning object recommendation,” Expert Systems with Applications, Vol. 36, no. 2, pp. 3034-3047, 2009.
[94] J. Yen, and R. Langari, “Fuzzy Logic: Intelligence, Control, and Information,” Prentice Hall, 1998.
[95] L. A. Zadeh, “Making computers think like people,” IEEE. Spectrum, pp. 26-32, 1984.
[96] L. A. Zadeh, “Fuzzy sets,” Information and Control, Vol. 8, no. 3, pp. 338-353, 1965.
[97] L. A. Zadeh, “Fuzzy logic and approximate reasoning,” Synthese, Vol. 30, pp. 407-428, 1975.
[98] H. J. Zimmermann, “Fuzzy set theory and its applications,” Boston MA: Kluwer Academic Publishers, 1991.
[99] D. Zhang, J. L. Zhao, L. Zhou, and Jr J. F. Nunamaker, “Can e-learning replace classroom learning?,” Communications of the ACM, Vol. 47, no. 5, pp. 75-79, 2004.