Eurasia Journal of Mathematics, Science and Technology Education

• Akbulut, Y., & Cardak, C. S. (2012). Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011. Computers & Education, 58(2), 835-842.
• Bodrova, E., & Leong, D. J. (1998). Scaffolding emergent writing in the zone of proximal development. Literacy, Teaching and Learning, 3(2), 1.
• Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11, 87-110.
• Brusilovsky, P., & Maybury, M. T. (2002). From adaptive hypermedia to the adaptive web. Communications of the ACM, 45(5), 30-33.
• Brusilovsky, P., & Weber, G. (1996, July). Collaborative example selection in an intelligent example-based programming environment. In Proceedings of the 1996 international conference on Learning sciences (pp. 357-362). International Society of the Learning Sciences.
• Cronbach, L. J. (1951). Coefficient Alpha and the Internal Structure of Tests. Psychometrika, 16(3), 273-334.
• Cronbach, L. J. (1957). The two disciplines of scientific psychology. American Psychologist, 12(11), 671.
• De Bra, P., Aroyo, L., & Chepegin, V. (2004). The next big thing: Adaptive Web-based systems. Journal of Digital Information, 5(1).
• Dominic, M., & Francis, S. (2015). An adaptable e-learning architecture based on learners’ profiling. International Journal of Modern Education and Computer Science, 7(3), 26.
• Ford, N., & Chen, S. Y. (2000). Individual differences, hypermedia navigation, and learning: an empirical study. Journal of Educational Multimedia and Hypermedia, 9(4), 281-311.
• Froschl, C. (2005). User modeling and user profiling in adaptive e-learning systems (Unpublished master thesis). Graz University of Technology, Austria.
• Graf, S., Liu, T. C., Chen, N. S., & Yang, S. J. (2009). Learning styles and cognitive traits-Their relationship and its benefits in web-based educational systems. Computers in Human Behavior, 25(6), 1280-1289.
• Hung, S. Y., Yu, W. J., Liou, K. L., & Hsu, S. C. (2009). Exploring e-learning effectiveness based on activity theory: An example of asynchronous distance learning. MIS REVIEW: An International Journal, 15(1), 63-87.
• Kim, M.C., & Hannafin, M.J. (2011). Scaffolding problem solving in technology-enhanced learning environments (TELEs): Bridging research and theory with practice, Computers & Education, 56(2), 403-417.
• Mödritscher, F. (2008). Adaptive e-learning environments: theory, practice, and experience. VDM, Müller.
• Modritscher, F., Garcia-Barrios, V. M., & Gütl, C. (2004). The Past, the Present, and the Future of adaptive E-Learning. In Proceedings of the international conference on interactive computer aided learning (ICL2004).
• Murray, T. (2003). MetaLinks: Authoring and affordances for conceptual and narrative flow in adaptive hyperbooks. International Journal of Artificial Intelligence in Education, 13(2-4), 199-233.
• Nunnally, J. C. (1978). Psychometric Theory, NY: McGraw-Hill.
• Park, O. & Lee, J. (2004). Adaptive instructional systems. In D. H. Jonassen (Ed.) Handbook of Research for Educational Communications and Technology (pp. 651-685). Mahwah, NJ: Lawrence Erlbaum.
• Popescu, E. (2010). Adaptation provisioning with respect to learning styles in a web-based educational system: an experimental study. Journal of Computer Assisted Learning, 26(4), 243–257.
• Premlatha, K. R., Dharani, B., & Geetha, T. V. (2016). Dynamic learner profiling and automatic learner classification for adaptive e-learning environment. Interactive Learning Environments, 24(6), 1054-1075.
• Rosenberg, M. J. (2001). E-learning: Strategies for delivering knowledge in the digital age (Vol. 9). NY: McGraw-Hill.
• Roy, S., & Roy, D. (2011). Adaptive e-learning system: a review. International Journal of Computer Trends and Technology, 1, 115-118.
• Rukanuddin, M., Hafiz, K. D., & Asfia, R. (2016). Knowledge of Individual Differences of the Learners of Second Language Enriches Second Language Teaching. Journal of Literature, Languages and Linguistics, 19, 11-15.
• Sedumedi, T. D. T. (2017). Practical Work Activities as a Method of Assessing Learning in Chemistry Teaching. EURASIA Journal of Mathematics, Science and Technology Education, 13(6), 1765-1784.
• Shi, H., Rodriguez, O., Shang, Y., & Chen, S. S. (2002). Integrating adaptive and intelligent techniques into a web-based environment for active learning. In Intelligent Systems: Technology and Applications, 4, 229-260.
• Smith-Jentsch, K. A., Jentsch, F. G., Payne, S. C., & Salas, E. (1996). Can pretraining experiences explain individual differences in learning? Journal of Applied Psychology, 81(1), 110.
• Snow, R. E. (1986). Individual differences and the design of educational programs. American Psychologist, 41(10), 1029-1039.
• Stewart, C., Cristea, A., Brailsford, T., & Ashman, H. (2005). ‘Authoring once, delivering many’: creating reusable adaptive courseware (Doctoral dissertation, CiteSeerX).
• Stoyanov, S., & Kirchner, P. (2004). Expert concept mapping method for defining the characteristics of adaptive e-learning: ALFANET project case. Educational Technology Research and Development, 52(2), 41-54.
• Surjono, H. D. (2011). The design of adaptive e-learning system based on student’s learning styles. International Journal of Computer Science and Information Technologies, 5(2), 2350-2353.
• Vygotskii, L. S. (1978). Mind in Society: The Development of Higher Mental Processes, Cambridge, MA: Harvard University Press.
• Wang, M. C., & Lindvall, C. M. (1984). Chapter 5: Individual Differences and School Learning Environments. Review of research in education, 11(1), 161-225.
• Wang, Y. S. (2003). Assessment of Learner Satisfaction with Asynchronous E-Learning Systems. Information & Management, 41(1), 75-86.
• Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89-100.
• Zhang, D. (2004). Virtual Mentor and the lab system-toward building an interactive, personalized, and intelligent e-learning environment. Journal of Computer Information Systems, 44(3), 35-43.

APA

Wu, C.-H., Chen, Y.-S., & Chen, T.-G. (2018). An Adaptive e-Learning System for Enhancing Learning Performance: Based on Dynamic Scaffolding Theory. Eurasia Journal of Mathematics, Science and Technology Education, 14(3), 903-913. https://doi.org/10.12973/ejmste/81061

Vancouver

Wu CH, Chen YS, Chen TG. An Adaptive e-Learning System for Enhancing Learning Performance: Based on Dynamic Scaffolding Theory. EURASIA J Math Sci Tech Ed. 2018;14(3):903-13. https://doi.org/10.12973/ejmste/81061

AMA

Wu CH, Chen YS, Chen TG. An Adaptive e-Learning System for Enhancing Learning Performance: Based on Dynamic Scaffolding Theory. EURASIA J Math Sci Tech Ed. 2018;14(3), 903-913. https://doi.org/10.12973/ejmste/81061

Chicago

Wu, Chun-Hui, You-Shyang Chen, and Ta-g Chen. "An Adaptive e-Learning System for Enhancing Learning Performance: Based on Dynamic Scaffolding Theory". Eurasia Journal of Mathematics, Science and Technology Education 2018 14 no. 3 (2018): 903-913. https://doi.org/10.12973/ejmste/81061

Harvard

Wu, C.-H., Chen, Y.-S., and Chen, T.-G. (2018). An Adaptive e-Learning System for Enhancing Learning Performance: Based on Dynamic Scaffolding Theory. Eurasia Journal of Mathematics, Science and Technology Education, 14(3), pp. 903-913. https://doi.org/10.12973/ejmste/81061

MLA

Wu, Chun-Hui et al. "An Adaptive e-Learning System for Enhancing Learning Performance: Based on Dynamic Scaffolding Theory". Eurasia Journal of Mathematics, Science and Technology Education, vol. 14, no. 3, 2018, pp. 903-913. https://doi.org/10.12973/ejmste/81061