SPECIAL ISSUE PAPER
An Adaptive e-Learning System for Enhancing Learning Performance: Based on Dynamic Scaffolding Theory
Chun-Hui Wu 1
,  
You-Shyang Chen 2  
,  
 
 
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1
National Formosa University, Information Management, Huwei, TAIWAN
2
Hwa Hsia University of Technology, Department of Information Management, New Taipei City, TAIWAN
Online publish date: 2017-12-07
Publish date: 2017-12-07
 
EURASIA J. Math., Sci Tech. Ed 2018;14(3):903–913
KEYWORDS
This article has been presented in IEEE ICICE 2017 - International Conference on Information, Communication and Engineering held in Xiamen, Fujian, P.R. China on November 17–20, 2017. This article belongs to the special issue “Selected papers from IEEE ICICE 2017”
ABSTRACT
Adaptive learning for individual learners has recently become popular in education. This study aims to fill the void in the existing literature by building an adaptive e-learning system with self-assessment rubrics based on the dynamic scaffolding theory in response to different student needs. Meanwhile, the purpose of this study is to explore the effectiveness of using adaptive e-learning with dynamic scaffoldings and rubrics in fostering students’ learning outcomes. An experimental design was conducted to evaluate learning effectiveness and learning satisfaction in the Excel (spreadsheet) of the course for using the developed adaptive e-learning system. Sixty undergraduate students from a technology university in central Taiwan participated in this experimental study and executed a pretest and a posttest. Research results revealed that the developed adaptive e-learning system can effectively support students with personalized learning materials and successfully helps students acquired knowledge and develop cognitive abilities. The results recommend that teachers could employ rubrics as a self-assessment tool for supporting students with dynamic scaffoldings to conduct a learner-centered e-learning environment. Additionally, the lack of generalizability is clearly a limitation of the present data due to a few participants. Finally, future research direction of this study was also discussed.
 
REFERENCES (36)
1.
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.
 
2.
Bodrova, E., & Leong, D. J. (1998). Scaffolding emergent writing in the zone of proximal development. Literacy, Teaching and Learning, 3(2), 1.
 
3.
Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11, 87-110.
 
4.
Brusilovsky, P., & Maybury, M. T. (2002). From adaptive hypermedia to the adaptive web. Communications of the ACM, 45(5), 30-33.
 
5.
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.
 
6.
Cronbach, L. J. (1951). Coefficient Alpha and the Internal Structure of Tests. Psychometrika, 16(3), 273-334.
 
7.
Cronbach, L. J. (1957). The two disciplines of scientific psychology. American Psychologist, 12(11), 671.
 
8.
De Bra, P., Aroyo, L., & Chepegin, V. (2004). The next big thing: Adaptive Web-based systems. Journal of Digital Information, 5(1).
 
9.
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.
 
10.
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.
 
11.
Froschl, C. (2005). User modeling and user profiling in adaptive e-learning systems (Unpublished master thesis). Graz University of Technology, Austria.
 
12.
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.
 
13.
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.
 
14.
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.
 
15.
Mödritscher, F. (2008). Adaptive e-learning environments: theory, practice, and experience. VDM, Müller.
 
16.
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).
 
17.
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.
 
18.
Nunnally, J. C. (1978). Psychometric Theory, NY: McGraw-Hill.
 
19.
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.
 
20.
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.
 
21.
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.
 
22.
Rosenberg, M. J. (2001). E-learning: Strategies for delivering knowledge in the digital age (Vol. 9). NY: McGraw-Hill.
 
23.
Roy, S., & Roy, D. (2011). Adaptive e-learning system: a review. International Journal of Computer Trends and Technology, 1, 115-118.
 
24.
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.
 
25.
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.
 
26.
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.
 
27.
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.
 
28.
Snow, R. E. (1986). Individual differences and the design of educational programs. American Psychologist, 41(10), 1029-1039.
 
29.
Stewart, C., Cristea, A., Brailsford, T., & Ashman, H. (2005). ‘Authoring once, delivering many’: creating reusable adaptive courseware (Doctoral dissertation, CiteSeerX).
 
30.
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.
 
31.
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.
 
32.
Vygotskii, L. S. (1978). Mind in Society: The Development of Higher Mental Processes, Cambridge, MA: Harvard University Press.
 
33.
Wang, M. C., & Lindvall, C. M. (1984). Chapter 5: Individual Differences and School Learning Environments. Review of research in education, 11(1), 161-225.
 
34.
Wang, Y. S. (2003). Assessment of Learner Satisfaction with Asynchronous E-Learning Systems. Information & Management, 41(1), 75-86.
 
35.
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.
 
36.
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.
 
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