october 2012 - TOJET the Turkish online journal of educational ...

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TOJET: The Turkish Online Journal of Educational Technology – October 2012, volume 11 Issue 4 characteristics of the specific learning sector of the normalized learner characteristic based LECM. Therefore, once the learning progress can be diagnosed through e-learning process at dynamic real-time based approach, and then the e-learning platform can provide the suitable adaptive learning path and support for a specific learner at personally base according to the anticipation of future learning progress accordingly. Consequently, it’s a feasible approach to achieve personalized adaptive e-learning by the core mechanism of learner characteristic based LECM. Hence, it’s the core value of learner characteristic based LECM on this study. The detailed future research is shown as following: 1. Normalized Learner Characteristic Based LECM The learner characteristic based LECM can be normalized by enlarging the quantity and coverage areas of sampling. Then the variance between a specific learner’s learning effort curve and the normalized learner characteristic based LECM is reduced. That is, the normalized learner characteristic based LECM becomes an effective tool in representing a learner’s learning effort resume at learner characteristic base. Hence, specific normalized learner characteristic based LECMs are established for each learner characteristic based group. 2. Learning progress diagnosis Database The learning characteristics of each normalized learner characteristic based LECM can be extracted by analyzing the mathematical characteristics of the curve profile of learning sectors which include descending sector, ascending sector, convergent sector, divergent sector and inflection points. And then the learning progress diagnosis database is developed by classifying those learning characteristics extracted from normalized learner characteristic based LECMs in the mathematical format for every learning characteristic based group. 3. Modulized Adaptive Learning Path and support For a particular learner characteristic, the learning characteristics can be interpreted under specific learning sector of the normalized learner characteristic based LECM. And then the corresponding learning path and support is designed to suit with the specific requirements accordingly in order to enhance learning, which is an adaptive approach. Such is designed to adapt to the requirements of specific learning sector of the normalized learner characteristic based LECM accordingly; therefore, it is also a modulized approach to establish different modulized adaptive learning path and support for the corresponding requirements of specific learning sector. 4. The Personalized Adaptive e-Learning Platform By crossly comparing a learner’s learning effort curve with the learning progress diagnosis database at specific learning sector, the corresponding learning characteristics in the learning progress diagnosis database is identified. And then the future learning progress of the learner’s learning effort curve can be anticipated. The suitable modulized adaptive learning path and support is provided according to the anticipation status in order to improve learning at dynamic real-time based approach. The learning sector is moving forward dynamically along the learning process in order to conduct learning progress diagnosis at real-time based approach. The modulized adaptive learning path and support will be replaced by a new module according to the learning progress diagnosis results. As long as the learning sector is moving forward continuously along the whole learning process, then the corresponding modulized adaptive learning path and support is provided according to learning progress diagnosis results at real-time based approach. Consequently, the personalized adaptive e-learning platform is constructed to promote learning by receiving suitable modulized adaptive learning path and support through the continuously feed-forward learning progress diagnosis at real-time based approach. CONCLUSION E-learning should consider learner characteristics in order to promote learning. Learning style and self-efficacy become the key factors in the development of adaptive e-learning, which are the key learner characteristics considered in this study. Subjects were classified into 16 learner characteristic based groups in light of learning style, self-efficacy and learning efficiency. The learning effort curves generated by the subjects in the specific group were transformed into the learning effort curve mode (LECM) at learner characteristic base, which represented the specific learning effort curve mode for the corresponding profile of learner characteristics based group. By the analysis of learner characteristic based LECMs for 16 groups, the findings indicate that the learning effort of learner characteristic based LECMs tends to descend continuously for the high learning efficiency groups; the learning effort of learner characteristic based LECMs tends to ascend continuously for the low learning efficiency groups. That is, no matter what learner characteristics belong to, descending learning effort results in high learning performance and ascending learning effort results in low learning efficiency, which is in accordance with the previous research findings on learning efficiency and learning effort curves (Hsu et al, 2009, Hsu & Chang, 2011). Furthermore, the particular learning characteristic of the person with specific learning style and self-efficacy is in line with the progress of learning effort represented by the corresponding Copyright © The Turkish Online Journal of Educational Technology 217
TOJET: The Turkish Online Journal of Educational Technology – October 2012, volume 11 Issue 4 learner characteristic based LECM. Therefore, for the specific learner characteristics, the learner characteristic based LECM is proved to be an effective core mechanism for the development of learning progress diagnosis. The normalized learner characteristic based LECM is required to be established because there are missing learner characteristic based LECMs for two groups and the quantity and coverage areas of sampling is also relative smaller in this study. For the future research, the first step is to establish the normalized learner characteristic based LECM, and then take the normalized learner characteristic based LECMs as the core mechanism to develop the learning progress diagnosis database. In the meantime the adaptive learning path and support is designed at modular base for specific requirements at different learning progress status. The final target of the future research is to realize personalized adaptive e-learning by embedding the modulized adaptive learning path and support, the dynamic real-time based learning effort quantification technique and learning progress diagnosis database into an e-learning platform. By the personalized adaptive e-learning platform, learner’s learning progress can be feed-forward diagnosed and anticipated continuously by the learning progress diagnosis database. And then the corresponding modulized adaptive learning path and support is provided according to learning progress diagnosis results at real-time based approach. ACKNOWLEDGMENTS The author gratefully acknowledge the support of this study by the National Science Council of Taiwan, under the Grant No. NSC99-2511-S-254-001-MY3. The author also would like to thank Ching Kuo Institute of Management and Health for providing the necessary equipments for the project. REFERENCES Akdemir, O., & Koszalka, T. A. (2008). Investigating the relationships among instructional strategies and learning styles in online environments. Computers & Education, 50(4), 1451-61. Allal, L., & Ducrey, G.P. (2000). Assessment of -- or in -- the zone of proximal development. Journal of Learning and Instruction, 34(1), 137-152. Bandura, A. (1986). Social foundations of thought and action‥A social cognitive theory. Englewood Cliffs, NJ:Prentice Hall. Bandura, A. (1992). Exercise of personal agency though the self-efficacy mechanism. In R. Schwarzer (Ed.), Self-Efficacy: Thought Control of Action (pp.3-38). Washington, DC: Hemisphere. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman. Brusilovsky, P. (2001). Adaptive Hypermedia. User Modeling and User-Adapted Interaction, 11, 87-110. Certiport (2008). Internet and computing benchmark and mentor. Retrieved Nov 10, 2011, from http://certprep.certiport.com Chou, H.W. & Wang, T.B. (2000). The influence of learning style and training method on self-efficacy and learning performance in WWW homepage design training. International Journal of Information Management, 20, 455-472. Commings, T.G. & Wirley, C.G. (2001). Organization Development and Change (7th ed). Cincinnati, OH: Southwestern College Publishing. Demirbas, O.O., & Demirkan, H. (2007). Learning styles of design students and the relationship of academic performance and gender in design education. Learning and Instruction, 17, 345-359. Demirkan, H., & Demirbas, O.O. (2008). Focus on the learning styles of freshman design students. Design Studies, 29, 254-266. Diaz, D.P., & Cartnal, R.B. (1999). Student’s learning styles in two classes: Online distance learning and equivalent on-campus. College Teaching, 47(4), 130-135. Federico, P. A. (2000). Learning styles and student attitudes toward various aspects of network-based instruction. Computers in Human Behavior, 16, 359-379. Gau, T., & Tzai, C. (1999). Learning styles and instructional design. Educational Resources and Research, 29, 46-49. Gecas, V. (1989). The social psychology of self-efficacy. Annual Review of Sociology, 15, 291. Greenglass, E., Schwarzer, R., & Taubert S. (1999). The proactive coping inventory (PCI). Retrieved Nov 08, 2011, from http://userpage.fu-berlin.de/~health/greenpci.htm Hsu, P.S., Chang, T. J., & Wu, M.H. (2009). A new diagnostic mechanism of instruction: A dynamic, real-time and non-interference quantitative measurement technique for adaptive e-learning. International Journal of Distance Education Technologies, 7(3), 85-96. Hsu, P.S., & Chang, T .J. (2011). Validation of learning effort algorithm for real-time non-interfering based diagnostic technique. International Journal of Distance Education Technologies, 9(3), 31-44. Huang, Y.M., Chen, C.C., & Wang, D.C. (2012). Optimizing T-Learning course scheduling based on genetic algorithm in benefir oriented data broadcast environments. The Turkish Online Journal of Educational Copyright © The Turkish Online Journal of Educational Technology 218
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