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An Enhanced Learning Style Index: Implementation and Integration into an Intelligent and Adaptive e-Learning System
1
University of Aizu, Japan
Online publication date: 2017-07-12
Publication date: 2017-07-12
EURASIA J. Math., Sci Tech. Ed 2017;13(8):4449-4470
KEYWORDS
ABSTRACT
Advances and accessibility of Internet services around the world have transformed the traditional classroom learning into web-based e-learning systems. In recent years, designing adaptive e-learning systems has become one of striking topic of discussions in the literature. Additionally, integrating such systems with intelligent and adaptive systems that can measure the learning preferences of the user can enable learners to obtain the most suitable learning objects that might be matched with their learning styles. Moreover, even in the classroom teaching, knowing the learning styles of students can also help teachers to adopt appropriate learning materials for efficient learning. This paper is concerned with the study, implementation, and application of a web-based learning style index. The paper also described a case study on the integration of the learning style index into an adaptive and intelligent e-learning system.
REFERENCES (44)
1.
Alias, N. (2014). An evaluation of gas law WebQuest based on active learning style in a secondary school in Malaysia. Eurasia Journal of Mathematics, Science & Technology Education, 10(3), 175--184.
2.
Anshari, M. (2015). Pervasive Knowledge, Social Networks, and Cloud Computing: E-Learning 2.0. Eurasia Journal of Mathematics, Science & Technology Education, 11(5), 909-921.
4.
Barbe, W. (1979). Teaching through modality strengths: Concepts and practices. Columbus, OH: Zaner-Bloser. New York: Inc.
5.
Brusilovsky, P. (1998). Methods and techniques of adaptive hypermedia. In P. a. Brusilovsky, Adaptive hypertext and hypermedia (pp. 1--43). Springer.
6.
Chang, Y., Chen, Y., Chen, N., Lu, Y., & Fang, R. (2016). Yet another adaptive learning management system based on Felder and Silverman’s learning styles and Mashup. Eurasia Journal of Mathematics, Science & Technology Education, 12(5), 1273--1285.
7.
Chengjie, Y. (2015). Challenges and changes of MOOC to traditional classroom teaching mode. Canadian Social Science, 11(1), 135.
8.
Coffield, F. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review. Learning and Skills Research Centre London.
9.
Felder, R. (1997). Index of learning style questionnaire. Available online at in: http://www2. ncsu. edu/unity/lockers/users/f/felder/public/ILSdir/ilsweb. html .
10.
Felder, R. M. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(7), 674-681.
11.
Floyde, A. A. (2013). The design and implementation of knowledge management systems and e-learning for improved occupational health and safety in small to medium sized enterprises. Safety science, 60, 69--76.
12.
Hamada, M. (2016). A multimedia learning environment for information theory. Teaching, Assessment, and Learning for Engineering (TALE), 2016 IEEE International Conference on (pp. 55--61). IEEE.
13.
Hamada, M. & Hasan, M. (2017). An Interactive Learning Environment for Information and Communication Theory. Eurasia Journal of Mathematics, Science & Technology Education, 13(1), 35--59.
14.
Hamada, M. (2008). An integrated virtual environment for active and collaborative e-learning in theory of computation. IEEE Transaction on Learning Technologies, 1(2).
15.
Hassan, M. (2016). Recommending Learning Peers for Collaborative Learning Through Social Network Sites. 7th International Conference on Intelligent Systems, Modelling and Simulation (pp. 60--63). IEEE.
16.
Hassan, M. (2016). Enhancing learning objects recommendation using multi-criteria recommender systems. Teaching, Assessment, and Learning for Engineering (TALE), 2016 IEEE International Conference on (pp. 62--64). IEEE.
17.
Hassan, M. (2015). Learning system and analysis of learning styles for African and Asian students. Teaching, Assessment, and Learning for Engineering (TALE) (pp. 83--87). IEEE.
18.
Hou, M. (2017). A generic framework of intelligent adaptive learning systems: from learning effectiveness to training transfer. Theoretical Issues in Ergonomics Science, 18(2), 167--183.
19.
Hsieh, M.-Y. (2016). Online Learning Era: Exploring the Most Decisive Determinants of MOOCs in Taiwanese Higher Education. Eurasia Journal of Mathematics, Science & Technology Education, 12(5), 1163--1188.
20.
Johnson, D. (2016). The Potential Transformation of Higher Education Through Computer-Based Adaptive Learning Systems. Global Education Journal, 2016(1).
21.
Kazemi, A. (2016). The Role of Cognitive and Learning Style in Second Language Learning. Modern Journal of Language Teaching Methods, 6(2), 527--534.
22.
Keefe, J. W. (1979). Learning style: An overview. Student learning styles: Diagnosing and prescribing programs, 1, 1-17.
24.
Keller, J. M. (1987). Development and use of the ARCS model of instructional design. Journal of instructional development, 10(3), 2-10.
25.
Kolb, D. A. (1993). Learning-style inventory: Self-scoring inventory and interpretation booklet: Revised scoring. TRG, Hay/McBer.
26.
Kort, B. (2001). An affective model of interplay between emotions and learning: Reengineering educational pedagogy-building a learning companion. International Conference on Advanced Learning Technologies (pp. 43--46). IEEE.
27.
Kumiko, F. M. (2009). Cloninger's temperament dimensions, emotional experiences and emotional regulation, (In Japanese). Education Science, 14(4), 387--397.
28.
Magnisalis, I. (2011). Adaptive and intelligent systems for collaborative learning support: A review of the field. IEEE Transactions on Learning Technologies, 4(1), 5--20.
30.
Montgomery, S. M. (1995). Addressing diverse learning styles through the use of multimedia. Frontiers in Education Conference, 1995. Proceedings, 1995.1, pp. 3a2--13. IEEE.
31.
Ozerem, A. (2015). Learning Environments Designed According to Learning Styles and Its Effects on Mathematics Achievement. Eurasian Journal of Educational Research, 61, 61--80.
33.
Russell, T. L. (1997). Technology Wars: Winners and Losers-The No Significant Difference phenomenon. Educom Review, 32, 44--47.
34.
Santos, J. (2002). On the Application of the semantic Web Concepts to Adaptive E-learning. EurAsia-ICT 2002: Information and Communication Technology, 536--543.
35.
Snow, R. (1987). Cognitiveconafive-affective processes in aptitude, learning, and instruction: An introduction. Conative and Affective Process Analysis. Hillsdale, New Jersey: Erlbaum, 3, 1--10.
36.
Tallmadge, G. K. (1969). Relationships among learning styles, instructional methods, and the nature of learning experiences. Journal of Educational Psychology, 60(3), 222.
38.
Tseng, J. C.-C.-J.-C. (2008). Development of an adaptive learning system with two sources of personalization information. Computers & Education, 5(2), 776--786.
39.
Violante, M. G. (2014). Implementing a new approach for the design of an e-learning platform in engineering education. Computer Applications in Engineering Education, 22(4), 708--727.
40.
Waite, S. (2007). Our flexible friend: The implications of individual differences for information technology teaching. Computers & Education, 48(1), 80--99.
41.
Wang, T.-S. (2013). Design and assessment of joyful mobile navigation systems based on TAM and integrating learning models applied on ecological teaching activity. Eurasia Journal of Mathematics, Science & Technology Education, 9(2), 201--212.
42.
Yang, J., Huang, R., & Kinshuk (2016). The Learning Preferences of Digital Learners in K-12 Schools in China. Eurasia Journal of Mathematics, Science & Technology Education, 12(4), 1047--1064.
43.
Yazici, H. J. (2016). Role of learning style preferences and interactive response systems on student learning outcomes. International Journal of Information and Operations Management Education, 6(2), 109--134.
44.
Yeh, S., & Fu, H. (2014). Effects of Cooperative E-Learning on Learning Outcomes. Eurasia Journal of Mathematics, Science & Technology Education, 10(6), 531--536.
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