This research presented a problem-based e-Learning model and system framework to help teachers effectively develop knowledge of “mathematical teaching for students with mild disabilities” and enhance their ability to solve students’ problems on mathematics learning. This model used problem based learning (PBL) theory as the core concept to provide cases, and learning procedure for adaptive learning based on the background and experiences of the learners and the students’ learning difficulties. With this model, the learner was able to receive immediate and interactive online learning through question descriptions, online discussions, and experience sharing with other learners. This framework was different from other e-Learning platforms in that we emphasized interactive and adaptive learning based on the theories of situational learning, social constructivism, and case-based learning. In addition to personalized learning, the framework also helped guide the learner in learning, contemplating, innovating, and sharing knowledge. The learner’s benefits could therefore be maximized and the content of the learning platform could continuously be expanding and updated.
This study used Natural Language Processing technology to improve the interaction flexibility between the users and the platform. With the consideration of the characteristics of both the
student and the learner, case-base reasoning and rule-base reasoning techniques were employed to develop learning plans and provide adaptive learning resources, thus overcoming the obstacle that many users face when they cannot fully describe their problem with the keywords and fail to receive the full benefits from learning activities.
Methods for expert teacher knowledge acquisition and elicitation as well as knowledge analysis and modeling were presented. Domain concept knowledge, practical knowledge and empirical knowledge were developed and built in a knowledge repository to support problem based learning. Since ontology is capable of reducing ambiguity of information by representing domain concept knowledge in a structured format that is understandable for both humans and machines, it was used for representing domain concept knowledge, the meta knowledge of practical knowledge, and empirical knowledge to enhance knowledge sharing and to facilitate knowledge managements. In the areas of eLearning, building ontology for application domain also provides an opportunity to analyze domain concept knowledge, make domain concept knowledge explicit, separate it from operational knowledge, provide common understanding of the knowledge structure, and enable reuse of domain concept knowledge.
For further extensions, web mining techniques can be used to extract relevant knowledge from outside, and automatic knowledge extraction on content of Q&A and on-line discussion can be conducted to take the advantage of continuous expansions and upgrades of knowledge.
References
Booch, G., Rumbaugh, J. and Jacobson, I., 1999. The Unified Modeling Language User Guide.
Reading, MA: Addison-Wesley.
Brown, J.S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning.
Educational Researcher, 18 (1), 32-41.
Bruegge B. And Dutoit A. H. (2004). Object-Oriented Software Engineering Using UML, Patterns, and Java. Prentice Hall, 2nd Edition.
Changchien, S. Wesley and Lin, Ming-Chia (2005). Design and implementation of a case-based reasoning system for marketing plans. Expert Systems with Applications, 28,43-53.
Chin, C. & Lin, F.L. (2000). A Case Study of a Mathematics Teacher's Pedagogical Values: Using a Methodological Framework of Interpretation and Reflection. Proceedings of the National Science Council, Part D: Mathematics, Science, and Technology Education, 10(2), 99-101.
Chinese Knowledge and Information Processing (CKIP), http://ckipsvr.iis.sinica.edu.tw/.
Fenstermacher, G. D. (1994). The knower and the known: The nature of knowledge in research on teaching. In L. Darling-Hammond (Ed.), Review of research in teaching, 20, 3-56.
Washington: AERA.
Fenwick, T.J., & Parsons, J. (1998). Boldly solving the world: a critical analysis of problem-based learning as a method of professional education, Studies in the Education of Adults, 30(1), 53-66.
Fuchs, L. S., Fuchs, D., Hamlett, C. L., & Pamela, P. M. (1991). Effects of curriculum-based measurement and consultation on teacher planning and student achievement in mathematics operations. American Educational Research Journal, 28 (3), 617-641.
Gomez-Perez, Asuncion; Fernandez-Lopez, Mariano and Corcho Oscar (2004). Ontological Engineering, with expmples from the areas of Knowledge Management. e-Commerce and the Semantic Web, Springer-Verlag London Limited.
Holen, A. (2000). The PBL group: self-reflections and feedback for improved learning and growth. Medical Teacher, 22(5), 485 - 488.
Kelly, A. M. (2000). A problem-based learning resource in emergency medicine for medical students. Journal of accident and emergency medicine, 17(5), 320-233.
Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., Puntambekar, S., &
Ryan, M. (2003). Problem-Based Learning Meets Case-Based Reasoning in the Middle-School Science Classroom: Putting Learning by Design(tm) Into Practice. Journal of the Learning Sciences, 12(4), 495-547.
LMML(Learning Material Markup Language), http://www.lmml.de.
LTSC (IEEE Learning Technology Standards Committee). http://ltsc.ieee.org/wg12.
Jacobson, I., Booch, G., & Rumbaugh, J. (1999). The Unified Modeling Language User Guide, Addison-Wesley, Reading, Massachusetts.
Merseth, K. K. (1996). Cases and case methods in teacher education. In J. Sikula (Ed.) Handbook of Research on Teacher Education (2nd), (pp.722-744). New York: Macmillan.
Moss, D., & Van Duzer, C. (1998). Project-based learning for adult English language learners.
ERIC Digest. Washington, DC: National Center for ESL Literacy.
Orfali, R.; Harkey, D. and Edwards, J., 1996. The Essential Distributed Objects: Survival Guide. New York: John Wiley & Sons, Inc.
Patel, A., Kinshuk, & Russell, D. (2000). Intelligent tutoring tools for cognitive skill acquisition in life long learning. Educational Technology & Society, 3(1).
Ram, P. (1999). Problem-based learning in undergraduate education. Journal of Chemical Education, 76(8) ,1122-1127.
Richardson, V. (1993). Use of cases in considering methods for motivating students. In H.
Harrington & M. Thompson (Eds.), Student motivation and case study manual (pp. 5760).
Boone, NC: Appalachian State University.
Richert, A.E. (1991). Using teacher cases for reflection and enhanced understanding. In A.
Lieberman & L.Miller (Eds.), Staff development for education in the 90's (pp. 113-132). New York: Teachers College Press.
Roberston DM, Hertan R, & Farnworth PG (2000). Is the action of inhibin mediated via a unique receptor? Reviews of Reproduction, 5, 131–135.
Roderick A. Farmer, & Baden Hughes (2005). A Situated Learning Perspective on Learning Object Design. Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies.
Rogoff, B. (1990). Apprenticeship in thinking: Cognitive development in social context. New York: Oxford.
Salton, G., & Buckley, C. (1988). Term weighting approaches in automatic text retrieval. In Information Processing & Management, 24(5), 513-523.
Salton, G., & McGill (1983). M. Introduction to Modern Information Retrieval. McGraw-Hill, New York.
Shen, Weiming; Hao, Qi; Wang, Shuying; Li, Yinsheng; Ghenniwa, Hamada, 2007, “An
Agent-based Service-oriented Integration Architecture for Collaborative Intelligent
Manufacturing”, Robotics and Computer Integrated Manufacturing, Volume: 23, Issue: 3, pp.
315-325
SCORM (Sharable Content Object Reference Model), http://www.adlnet.org/.
Shulman, L. S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 57, l-22.
Solomon, J. (1987). Social influences on the construction of pupils' understanding of science.
Studies in Science Education, 14, 63-82.
Souders, J. and Prescott, C. (1999). A Case for Contextual Learning. High School Magazine, 7 (3), 38-43.
Sun Microsystems, Inc.(2002). Web Services Made Easier: The Java APIs and Architectures for XML.
Trop, L., & Sage, S. (1998). Problems as possibilities: problem-based learning for K-12 Virginia:
ASDC.
VanLehn, K. (1996). Cognitive skill acquisition. Annual Review of Psychology, 42, 513-539.
Zhang Dong-Mo, Sheng Huan-Ye, Li Fang, & Yao Tian-Fang (2002). The model design of a case-based reasoning multilingual natural language interface for database. Proceeding of the First International Conference on Machine Learning and Cybernetics, 1474-1478.