An enhanced Bayesian model to detect students' learning styles in Web-based courses: Original article

Journal of Computer Assisted Learning

Volumn 24, Issue 4, 2008, Pages 305-315

  Garcia P ^b   Schiaffino, S ^a   Amandi, A ^b  

^a University Campus   (Argentina)

^b NONE

Author keywords

Bayesian networks; E learning; Learning styles; Student modeling

Indexed keywords

EID: 47149086598     PISSN: 02664909     EISSN: 13652729     Source Type: Journal    
DOI: 10.1111/j.1365-2729.2007.00262.x     Document Type: Article

References (28)

1. Allinson C. Hayes J. (1996) The cognitive style index: a measure of intuition analysis for organizational research. Journal of Management Studies 33, 119 135.
2. Arroyo I. Woolf B. (2005) Inferring learning and attitudes from a Bayesian network of log file data. In AIED 05, 12th International Conference on Artificial Intelligence in Education, 18-22 July 2005, Amsterdam.
3. Brusilovsky P. Peylo C. (2003) Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education 13, 159 172.
4. Carver C., Howard R. Lavelle E. (1996) Enhancing student learning by incorporating learning styles into adaptive hypermedia. In ED-MEDIA'96 World Conference on Educational Multimedia Hypermedia, pp. 118 123. AACE, Charlottesville, VA.
5. Chen S., Magoulas G. Dimakopoulos D. (2005) A flexible interface design for web directories to accommodate different cognitive styles. Journal of the American Society for Information Science and Technology 56, 70 83.
6. Coffield F., Moseley D., Hall E. Ecclestone K. (2004) Learning Styles and Pedagogy in Post-16 Learning: A Systematic and Critical Review. Learning and Skills Research Center, London.
7. Conati C., Gertner A. VanLehn K. (2002) Using Bayesian networks to manage uncertainty in student modeling. Journal of User-Modeling and User-Adapted Interaction 12, 371 417.
8. Dufresne A. Turcotte S. (1997) Cognitive styles and its implications for navigation strategies. In Proceedings of the World Conference on Artificial Intelligence and Education AI-ED'97, 18-22 August 1997, Kobe, Japan.
9. Felder R. Brent R. (2005) Understanding student differences. Journal of Engineering Education 94, 57 72.
10. Felder R. Silverman L. (1988) Learning and teaching styles. Journal of Engineering Education 78, 674 681.
11. Ford N. Chen S. (2000) Individual differences, hypermedia navigation, and learning: an empirical study. Journal of Educational Multimedia and Hypermedia 9, 281 311.
12. Gamboa H. Fred A. (2001) Designing intelligent tutoring systems: a Bayesian approach. In ICEIS 01, 3rd International Conference on Enterprise Information Systems, 7-10 July 2001, Setúbal, Portugal.
13. Garcia P., Amandi A., Schiaffino S. Campo M. (2007) Evaluating Bayesian networks' precision for detecting students' learning styles. Computers and Education 49, 794 808.
14. Gertner A. VanLehn K. (2000) Andes: a coached problem solving environment for physics. In Intelligent Tutoring Systems, ITS 2000, 19-23 June 2000, Montréal, Canada, pp. 131 142.
15. Gilbert J. Ham C. (1999) Adapting instruction in search of a significant difference. Journal of Network and Computer applications 22, 149 160.
16. Honey P. Mumford A. (1992) The Manual of Learning Styles, 3rd edn. Peter Honey, Berkshire.
17. Jensen F. (2001) Bayesian Networks and Decision Graphs. Springer Verlag, New York.
18. Jeremic Z. Devedzic V. (2004) Design patters ITS: student model implementation. In IEEE International Conference on Advanced Learning Technologies, 30 August-1 September 2004, Joensuu, Finland.
19. Kolb D. (1984) Experiential Learning: Experience as the Source of Learning and Development. Prentice Hall, New York.
20. Litzinger M. Osif B. (1993) Accommodating diverse learning styles: designing instruction for electronic information sources. In What is Good Instruction Nows? Library Instruction for the 90s (ed. L. Shirato). Pierian Press, Ann Arbor, MI.
21. Olesen K., Lauritzen S. Jensen F. (1992) Hugin: a system creating adaptive causal probability networks. In 8th Annual Conference Uncertainty in Artificial Intelligence, 17-19 July 1992, Stanford, CA.
22. Papanikolaou K., Grigoriadou M., Knornilakis H. Magoulas G. (2003) Personalizing the interaction in a web-based educational hypermedia system: the case of INSPIRE. User Modeling and User-Adapted Interaction 13, 213 267.
23. Paredes P. Rodriguez P. (2004) A mixed approach to modelling learning styles in adaptive educational hypermedia. In Proceedings of 3rd IASTED Conference on Web-Based Education (WBE2004), 16-18 February 2004, Innsbruck, Austria.
24. Pearl J. (1988) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA.
25. Peña C., Marzo J., de la Rosa J. Fabregat R. (2002) Un sistema de tutoría inteligente adaptativo considerando estilos de aprendizaje. In IE 2002, 20-22 November 2002, Vigo, Spain.
26. Shang Y., Shi H. Chen S. (2001) An intelligent distributed environment for active learning. ACM Journal on Educational Resources in Computing 1. Doi: 10.1145/384055.384059
27. Villaverde J., Godoy D. Amandi A. (2006) Learning styles' recognition in e-learning environments with feed-forward neural networks. Journal of Computer Assisted Learning 22, 197 206.
28. Walters D., Egert C. Cuddihy E. (2000) Learning styles and Web-based education: a quantitative approach. In Proceedings of 9th Annual FACT Conference on Instructional Technology, 30 May-1 June 2000, Buffalo, NY, pp. 115 177.