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 RERERENCE Cortizo, J. L., Rodriguez, E., Vijande, R., Sierra, J. M., & Noriega, A. (2009). Blended learning applied to the study of mechanical couplings in engineering. Computers & Education, 54(4), 1006-1019.. Chen, Y. L., Liu, E. Z. F., Shih, R. C., Wu, C. T., & Yuan, S. M. (2011). Use of peer feedback to enhance elementary students’ writing through blogging. British Journal of Educational Technology, 42(1), E1-E4. Chiang, I. T., Liu, Z. E. F., Shih, R. C., & Chen, S. T. (2011). Using Web 2.0 Social Networking to Enhance Collaborative Learning in Preparing Graduation Events. In Springer Lecture Notes in Computer Science volume 6872, pp.440-444. Proceedings of 2011 Edutainment Workshop on Game-Assisted Language Learning. Dickinson, M., Eom, S., Kang, Y., Lee, C. H., & Sachs, R. (2008). A balancing act: how can intelligent computer-generated feedback be provided in learner-to-learner interactions? Computer Assisted Language Learning, 21(4), 369-382. Godwin-Jones, R. (2003). Emerging technologies. Tools for distance education: Toward convergence and integration. Language Learning & Technology, 7(3), 18-22. Harriman, G. (2004). What is blended learning? E-learning resources: blended learning. http://www.grayharriman.com/blended_learning.htm Retrieved Jan 28, 2010. Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of elearning: A case study of the Blackboard system. Computers & Education, 51(2), 864-873. Liaw, S. S., Chen, G. D., & Huang, H. M. (2008). Users’ attitudes toward Web-based collaborative learning systems for knowledge management. Computers & Education. 50(3), 950-961. Liaw, S. S., Huang, H. M., & Chen, G. D. (2007). Surveying instructor and learner attitudes toward e-learning. Computers & Education, 49(4), 1066-1080. Liu, E. Z. F., Ho, H. C., & Song, Y. J. (2011). Effects of an online rational emotive curriculum on primary school students’ tendencies for online and real-world aggression. Turkish Online Journal of Educational Technology, 10(3), 83-93. Liu, E. Z. F. & Chang, Y. F. (2010). Gender differences in usage, satisfaction, self-efficacy, and performance of blogging. British Journal of Educational Technology, 41(3), E39-E43. Liu, E. Z. F. & Lin, S. S. J. (2007). Relationship between peer feedback, cognitive and meta-cognitive strategies and achievement in networked peer assessment. British Journal of Educational Technology, 38 (6), 1122- 1125. Liu, E. Z. F., Shih, R. C., & Tsai, Y. L. (2011). Hyperlink network analysis of the educational blog. British Journal of Educational Technology, 42(2), E25-E29. Lou, S. J., Wu, S. C., Shih, R. C., & Tseng, K. H. (2010). Adoption of blogging by a Chinese language composition class in a vocational high school in Taiwan. Australasian Journal of Educational Technology, 26(6), 898-916. http://www.ascilite.org.au/ajet/ajet26/lou.html Richardson,W. (2006). Blogs, wikis, podcasts, and other powerful web tools for classroom. Thousand Oaks, CA: Corwin Press. Shih, R. C. (2010). Blended learning using video-based blogs: Public speaking for English as a second language students. Australasian Journal of Educational Technology, 26(6), 883-897. http://www.ascilite.org.au/ajet/ajet26/shih.html Tam, S. S., Kan, N. H., & Ng., L. C. (2010). Low proficiency leaners in synchronous computer-assisted and face to face interactions. The Turkish Online Journal of Educational Technology, 9(3), 60-75. Tsai, C. C., Chen, N. S., & Chen, G. D. (2010). The current status and future of e-learning in Taiwan. Innovations in Education and Teaching International, 47 (1), 5-7. Well, M., Pegler, C., & Mason, R. (2005). Use of innovative technologies on an e-learning course. Internet and Higher Education, 8, 61-71. Yusuf, M. O. & Afolabi, A. O. (2010). Effects of computer assisted instruction (CAI) on secondary school students’ performance in Biology. The Turkish Online Journal of Educational Technology, 9(1), 62-69. Copyright © The Turkish Online Journal of Educational Technology 209
TOJET: The Turkish Online Journal of Educational Technology – October 2012, volume 11 Issue 4 LEARNER CHARACTERISTIC BASED LEARNING EFFORT CURVE MODE: THE CORE MECHANISM ON DEVELOPING PERSONALIZED ADAPTIVE E- LEARNING PLATFORM Pi-Shan Hsu Department of Human Resource Development, Ching Kuo Institute of Management and Health Taiwan pshsu@ems.edu.tw ABSTRACT This study aims to develop the core mechanism for realizing the development of personalized adaptive elearning platform, which is based on the previous learning effort curve research and takes into account the learner characteristics of learning style and self-efficacy. 125 university students from Taiwan are classified into 16 groups according to learning efficiency, learning style and self-efficacy. The learner characteristic based learning effort curve mode (LECM) is developed by conducting multi-factor regression on the corresponding learning effort curves generated by the specific group. The research findings conclude that the learner characteristic based LECM is able to represent the specific learning characteristics of the corresponding learning style and self-efficacy effectively. The core value of the learner characteristic based LECM is to realize the future development of personalized adaptive e-learning platform through taking it as the core mechanism. Keywords: e-learning, learner characteristics, learning effort, learning style, self-efficacy INTRODUCTION Simonson et al. (2003) argued that most of e-learning platforms focused on ICT (Information and Communication Technology) operation instead of learner characteristics. However, Kumar (1999) recommended that the design of e-learning platforms should consider learner characteristics in order to promote learning efficacy. Because the future trend of services and products is towards personalization, there are some research confirm that multimedia teaching materials improve students’ studies, for example, a experimental design prove multimedia teaching materials lead to significance difference in students’ chemistry test grades (Lou, Lin, Shih & Tseng, 2012). How to set e-learning material, such as the design of an e-learning platform should consider personalization and adaptability as key criterions. Brusilovsky (2001) noted that the importance of e-learning was to develop the personalized adaptive e-learning mechanism which adapted to individual learner characteristic. Therefore, this study aims to develop the core mechanism which supports the development of personalized adaptive e-learning platform. A good e-learning mechanism must be based on learner characteristics. The classification of learning style makes adaptive e-learning more sensible (Keh, 2004) and self-efficacy is the key factor of e-learning performance (Yu, 2007; Thompson & Lynch, 2003). However, most of e-learning platforms do not consider learner characteristics and even lack for dynamic real-time based mechanisms which promote effective learning. Hence, most of elearning platforms are not able to achieve adaptive learning effectively. This study takes learning style and self-efficacy as learner characteristics. And then utilize the dynamic real-time based learning effort quantification technique (Hsu et al, 2009, Hsu & Chang, 2011) to construct learner characteristic based LECM which is in line with the learning characteristic of corresponding learner characteristic. For the future research, the normalized learner characteristic based LECM is generated by enlarging the quantity and coverage areas of sampling. And then take the normalized learner characteristic based LECM as the core mechanism in developing learning progress diagnosis database. In the meantime the specific adaptive learning path and support is also developed based on the corresponding learning characteristics which are interpreted from the learner characteristic based LECM at modular base. Through embedding modulized adaptive learning path and support, dynamic real-time based learning effort quantification technique and learning progress diagnosis database into an e-learning platform, authors plan to realize the development of personalized adaptive e-learning platform. LITERATURE REVIEW In this study, learning style and self-efficacy are taken into consideration for the learner characteristics according to the findings of literature review. Learning Style The learning style is the preference of message acquisition approach in learning process and context (Kraus et al., 2001). People can promote learning through constructing the learning process and learning context which fit with the preference of specific learning style (Gau & Tzai, 1999; Reiff, 1992). Because of the technology Copyright © The Turkish Online Journal of Educational Technology 210
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