Advertisement

Design and Analysis of Recommendation Learning System Based on Multiple Intelligences Theory

  • Hong-Ren ChenEmail author
  • Yu-Hsuan Chang
Living reference work entry

Abstract

The information overload is becoming increasingly serious, and the current search engine is still unable to reach the optimization of information filtering. Many previous studies prove that the recommendation learning can reduce time cost and increase efficiency to learn. People pay more attention on the multiple intelligences because diversified thoughts were developed. Through statistical analysis of the multiple intelligences, we discuss issues on the learner’s strong and non-strong intelligent influence for learning efficiency. By doing this, we try to understand what kind of intelligence suits our recommendation learning system. Students in the experimental group that utilize intelligent recommendation learning system are more prominent than students in control groups with traditional teaching. This means that the learning efficiency with intelligent recommendation learning system is better than the learning efficiency with traditional teaching. In the strong intelligences, there exist prominent differences of logical-mathematical intelligence and intrapersonal intelligence in eight intelligences of learning efficiency. This means that learners who have logical-mathematical intelligence or intrapersonal intelligence are the best suited for the teaching with intelligent recommendation learning system.

Keywords

Recommendation learning Multiple intelligences Technology acceptance model Learning effect 

References

  1. Andronico, A., Carbonaro, A., Casadei, G., Colazzo, L., Molinari, A., & Ronchetti, M. (2003). Integrating a multi-agent recommendation system into a mobile learning management system. Paper presented at the Artificial Intelligence in Mobile System, USA.Google Scholar
  2. Armstrong, T. (1994). Multiple intelligences in the classroom. Alexandria, VA: ASCD.Google Scholar
  3. Benlian, A., Titah, R., & Hess, T. (2012). Differential effects of provider recommendations and consumer reviews in e-commerce transactions: An experimental study. Journal of Information Management Systems, 29(1), 237–272.CrossRefGoogle Scholar
  4. Chen, H.-R., & Huang, J.-G. (2012). Exploring learner attitudes toward web-based recommendation learning service system for interdisciplinary applications. Educational Technology & Society, 15(2), 89–100.Google Scholar
  5. Chiu, Y. H. (2003). The study of applying neural network and data mining techniques to course recommendation base on e-learning environment (Unpublished master dissertation). Chaoyang University of Technology, Taichung City, Taiwan.Google Scholar
  6. Cho, H. Y., & Kim, J. K. (2004). Application of web usage mining and product taxonomy to collaborative recommendations in e-commerce. Expert Systems with Applications, 26(2), 233–246.CrossRefGoogle Scholar
  7. Chuang, C.-H. (2006). Building recommendation learning on e-learning system by web mining (Unpublished master dissertation). Chaoyang University of Technology, Taichung City, Taiwan.Google Scholar
  8. Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. San Francisco: Jossey-Bass.Google Scholar
  9. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of compute technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.CrossRefGoogle Scholar
  10. Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human-Computer Studies, 45, 19–45.CrossRefGoogle Scholar
  11. Gardner, H. (1999). Intelligence reframed: Multiple intelligences (21st ed.). New York: Basic Books.Google Scholar
  12. Hsieh, T.-J. (2003). The effects of multiple intelligences instruction on multiple intelligences in computer instruction for the junior high school students (Unpublished master dissertation). National Kaohsiung Normal University, Kaohsiung City, Taiwan.Google Scholar
  13. Hsu, Y. H. (2007). The effect of role-playing moral teaching on the moral judgment, moral act and intra-personal intelligence of elementary school students (Unpublished master dissertation). DaYeh University, Changhua City, Taiwan.Google Scholar
  14. Kim, Y. S., Yum, B. J., Song, J. H., & Kim, S. M. (2005). Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites. Expert System with Applications, 28(2), 381–393.CrossRefGoogle Scholar
  15. Kuan, Y. T. (2004). The study of learning resource recommendation mechanism based on learning style (Unpublished master dissertation). Chung Yuan Christian University, Taoyuan City, Taiwan.Google Scholar
  16. Lee, C. Y. (2005). Multiple intelligence application in english of primary school e-learning website (Unpublished master dissertation). I-Shou University, Kaohsiung City, Taiwan.Google Scholar
  17. Lee, C.-I., & Hwang, W. P. (2004). A study of the effects of multiple intelligences on learning achievements of internet project-based learning. Journal of Taiwan Normal University Mathematics & Science Education, 49(1), 65–80.Google Scholar
  18. Li, K. W. (2005). The improvement of learning effectiveness by the application of strong intelligences on chemical equilibrium web-based learning system (Unpublished Ph.D. dissertation). Providence University, Taichung City, Taiwan.Google Scholar
  19. Li, Y., Lu, L., & Xuefeng, L. (2005). A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in e-commerce. Expert Systems with Applications, 28(1), 67–77.CrossRefGoogle Scholar
  20. Lin, Y. H. (2000). The effects of integrated teaching model of multiple intelligences and problem-solving on mathematics performance of elementary school students (Unpublished master dissertation). National Taiwan Normal University, Taipei City, Taiwan.Google Scholar
  21. Liu, Y. J. (2006). The study of developing students’ strong intelligences in practiced teaching strategy. Journal of the Chinese Society of Education, 1, 34–37.Google Scholar
  22. Massimini, F., & Carli, M. (1988). The systematic assessment of flow in daily experience. In M. Csikszentmihalyi & I. Csikszentmihalyi (Eds.), Optimal experience: Psychological studies of flow in consciousness (pp. 266–287). New York: Cambridge University Press.CrossRefGoogle Scholar
  23. Morita, J., & Shinoda, Y. (1994). Information filtering based on user behavioranalysis and best match text retrieval. In Proceeding of the 7th annual ACM-SIGIR conference on research and development in information retrieval (pp. 272–281). New York: ACM Press.Google Scholar
  24. Pinkerton, B. (2000). Webcrawler:Finding what people want (PhD dissertation). University of Washington, Seattle, WA.Google Scholar
  25. Schafer, J. B., Konstan, J., & Riedl, J. (1999). Recommender system in e-commerce. Paper presented at the first ACM on E-Commerce Conference.Google Scholar
  26. Shore, J. R. (2002). An investigation of multiple intelligences and self-efficacy in the university English as second language classroom (Unpublished doctoral dissertation). The George Washing University, St. Louis, MO.Google Scholar
  27. Silver, H. F., Strong, R. W., & Perini, M. J. (2000). So Each May Learn: Integrating learning styles and multiple intelligences. Alexandria, VA: Association for Supervision and Curriculum Development.Google Scholar
  28. Wei, K. N., Huang, J. H., & Fu, S. H. (2007). A survey of e-commerce recommender systems. Paper presented at the International Conference on Service Systems and Service Management.Google Scholar
  29. Yu, C.-M. (2005). The multiple intelligences of college students with different majors (Unpublished master dissertation). DaYeh University, Changhua City, Taiwan.Google Scholar
  30. Zhong, Z. M., Wang, T. I., Qiu, D. K., & Tsai, K. H. (2007). Learning component recommendation using implicit feedback and Plato distributed in E-learning platform. Paper presented at the National Computer Symposium.Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Digital Content and TechnologyNational Taichung University of EducationTaichungTaiwan

Personalised recommendations