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that have turned experimental equipment into virtual machines and have provided such innovation to students for them to create simulations of the different experimental conditions (such as traditional machining and electrical laboratory), operations of such web-based virtual machines are actually simple to achieve, and therefore it has been difficult to assess the actual attributions that applications of such technologies have made when utilized in teaching. With technological education in microfabrication, on the other hand, since the needs for the required hardware usually consume an enormous amount of resources and since an experiment is often conducted in a space where there is vacuity, it is relatively simple to assess the actual usefulness of the applied techniques that are driven by virtual-reality technologies. In addition, since the students are able to conduct experiments on a repeated basis that permits them to learn the errors that they have made when they are still in the middle of the experiment that is being conducted, they become even more familiarized with the procedures and are more able to have their error rates mitigated. Courses with concentrations on experimentations and practical training are notably difficult to teach in virtual reality because of their needs for physical spaces such as laboratories, and how engineering education can be offered on-line without being restrained by such physical limitations and yet with quality, scale, and breadth deserves more attention. The endeavor shall encourage online engineering education to become more widely accepted and applied further. Acknowledgments The authors gratefully acknowledge the support of this study by the National Science Council of Taiwan, under the Grant No. NSC98-2511-S-003-017-MY2 and NSC 98-2511-S-003-015-MY2. References Barbour, N., Brown, E., Connelly J., Dowdle, J., Brand, G., Nelson, J., & O'Bannon, (1997). Micromachined inertial sensors for vehicles. Proceedings of IEEE Conference on Intelligent Transportation System, 86, 1640-1659. doi:10.1109/ITSC.1997.660620 Bary, R., & Rees, M. (2006). Is (self-directed) learning the key skill for tomorrow’s engineers? European Journal of Engineering Education, 31(1), 73-81. Boud, D., Keogh, R., & Walker, D., (1985). Reflection: Turning experience into learning. NewYork, NY: Kogan Page Bouras, C., Philopoulos, A., & Tsiatsos, T. (2001). E-learning through distributed virtual environments. Journal of Network and Computer Applications, 24(3), 175-199. Burnard, P., (1988). Experiential learning: Some theoretical considerations. International Journal of Lifelong Education, 7(2), 127–133. Caliano, G., Lamberti, N., Iula, A., & Pappalardo, M., (1995). A piezoelectric bimorph static pressure sensor. Sensors and Actuators A: Physical, 46, 176-178. Craig, A. B., Sherman W. R., (2003). Understanding virtual reality–Interface, application, and design. San Francisco, CA: Morgan Kaufmann Publishers Desikan, P., DeLong, C., Beemanapalli, K., Bose, A., & Srivastava, J. (2005). Web mining for self-directed e-learning. (Report No. TR # 2005-030) Southampton, UK: WIT Press. Gripton, A. (2002, August). The application and future development of a MEMS SiVS/spl reg/ for commercial vehicles. Paper presented at the IEEE Position Location and Navigation Symposium. doi: 10.1109/PLANS.2002.998885 Idros, S. N. S., Mohamed, A. R., Esa, N., Samsudin, M. A., & Daud, K. A. M., (2010). Enhancing self-directed learning skills through e-SOLMS for Malaysian learners. Procedia Social and Behavioral Sciences, 2(2), 698-706. Knowles, M. S. (1975). Self-directed learning. New York, NY: Association Press. Knowles, M. (1998). The Adult Leaner: A Neglected Species (5th ed.). Houston, TX: Gulf Publishing Company Kolb, D.A., (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice Hall. Leonard, D. (1993). Workplace education: Adult education in a hospital nursing staff development unit. Journal of Nursing Staff Development, 9(2), 68–73. 212
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