A Framework to Personalise Open Learning Environments by Adapting to Learning Styles

Heba Fasihuddin, Geoff Skinner, Rukshan Athauda

2015

Abstract

This paper presents an adaptive framework to personalise open learning environments. The design of the framework has been grounded in cognitive science and learning principles. The theory of learning styles, and more specifically the model of Felder and Silverman, has been considered and applied. The developed framework has two main functions. First, it automatically identifies the learners’ learning styles by tracking their behaviours and interactions with the provided learning objects. Secondly, it provides adaptive navigational support based on the identified learning styles. Sorting learning materials based on learners’ preferences and hiding the least preferred materials are the two techniques of navigational support that have been applied in the proposed framework. Detailed descriptions of the framework functionalities and different components are presented in this paper. Future piloting and evaluation will test and verify this proposed framework.

References

  1. Ahmad, N., Tasir, Z., Kasim, J. & Sahat, H. 2013. Automatic detection of learning styles in learning management systems by using literature-based method. Procedia-Social and Behavioral Sciences, 103, 181-189.
  2. Atman, N., Inceoglu, M. M. & Aslan, B. G. 2009. Learning styles diagnosis based on learner behaviors in web based learning. Computational Science and Its Applications-ICCSA 2009. Springer.
  3. Bajraktarevic, N., Hall, W. & Fullick, P. 2003. Incorporating learning styles in hypermedia environment: Empirical evaluation. Workshop on Adaptive Hypermedia and Adaptive Web-Based Systems. Nottingham, UK, pp. 41-52.
  4. Brusilovsky, P. 1996. Methods and techniques of adaptive hypermedia. User modeling and user-adapted interaction, 6, 87-129.
  5. Brusilovsky, P. 2001. Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11, 87-110.
  6. Brusilovsky, P. 2003. Adaptive navigation support in educational hypermedia: the role of student knowledge level and the case for meta adaptation. British Journal of Educational Technology, 34, 487-497.
  7. Cabada, R. Z., Estrada, M. L. B., Cabada, R. Z. & Garcia, C. a. R. 2009. A fuzzy-neural network for classifying learning styles in a Web 2.0 and mobile learning environment. Web Congress, 2009. LA-WEB 7809. Latin American, 9-11 Nov. 2009. 177-182.
  8. Carmona, C., Castillo, G. & Millan, E. 2008. Designing a dynamic Bayesian network for modeling students' learning styles. 8th IEEE International Conference on Advanced Learning Technologies. 2008. 346-350.
  9. Carver, C. A., Jr., Howard, R. A. & Lane, W. D. 1999. Enhancing student learning through hypermedia courseware and incorporation of student learning styles. IEEE Transactions on Education 42, 33-38.
  10. Cha, H., Kim, Y., Park, S., Yoon, T., Jung, Y. & Lee, J.- H. 2006. Learning styles diagnosis based on user interface behaviors for the customization of learning interfaces in an intelligent tutoring system. In: Ikeda, M., Ashley, K. & Chan, T.-W. (eds.) Intelligent Tutoring Systems. Springer Berlin Heidelberg.
  11. Chang, Y.-C., Kao, W.-Y., Chu, C.-P. & Chiu, C.-H. 2009. A learning style classification mechanism for elearning. Computers & Education, 53, 273-285.
  12. Coffield, F., Moseley, D., Hall, E. & Ecclestone, K. 2004. Should we be using learning styles?: What research has to say to practice. Learning & Skills Research Centre.
  13. Coursera. 2012. Coursera [Online]. Available: https://www.coursera.org/ [Accessed 25-7-2012].
  14. Edx. 2012. edX [Online]. Available: http://www.edxonline.org/ [Accessed 26-5-2012].
  15. Fasihuddin, H., Skinner, G. & Athauda, R. 2013a. Insights into the use of Knowledge Maps in Online Learning Environments: A Pilot Study. The 1st Int. Conference on Technical Education, 2013. Bangkok. 27-32.
  16. Fasihuddin, H. A., Skinner, G. D. & Athauda, R. I. 2013. Boosting the opportunities of open learning (MOOCs) through learning theories. JoC, 3, 112-117.
  17. Felder, R. M. & Silverman, L. K. 1988. Learning and teaching styles in engineering education. Engineering education, 78, 674-681.
  18. García, P., Amandi, A., Schiaffino, S. & Campo, M. 2007. Evaluating Bayesian networks' precision for detecting students' learning styles. Computers & Education, 49, 794-808.
  19. García, P., Schiaffino, S. & Amandi, A. 2008. An enhanced Bayesian model to detect students' learning styles in Web-based courses. Journal of Computer Assisted Learning, 24, 305-315.
  20. Gilbert, J. E. & Han, C. Y. 1999. Adapting instruction in search of 'a significant difference'. Journal of Network and Computer applications, 22, 149-160.
  21. Graf, S. 2007. Adaptivity in learning management systems focussing on learning styles. Ph.D. Thesis, Vienna University of Technology.
  22. Graf, S. & Kinshuk, K. 2007. Providing adaptive courses in learning management systems with respect to learning styles. In: Bastiaens, T. & Carliner, S. (eds.) World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2007. Quebec City, Canada: AACE.
  23. Graf, S. & Kinshuk 2014. Adaptive technologies. Handbook of Research on Educational Communications & Technology. Springer New York.
  24. Graf, S., Kinshuk & Tzu-Chien, L. 2008. Identifying learning styles in learning management systems by using indications from students' behaviour. 8th IEEE International Conference on Advanced Learning Technologies. 482-486.
  25. Graf, S. & Tzu-Chien, L. 2009. Supporting teachers in identifying students' learning styles in learning management systems: An automatic student modelling approach. Journal of Educational Technology & Society, 12, 3-14.
  26. Graf, S. & Viola, S. 2009. Automatic student modelling for detecting learning style preferences in learning management systems. Int. Conference on Cognition and Exploratory Learning in Digital Age, 172-179.
  27. Hong, H. & Kinshuk, D. 2004. Adaptation to student learning styles in web based educational systems. World Conference on Educational Multimedia, Hypermedia and Telecommunications, 2004. 491-496.
  28. Klašnja-Milicevic, A., Vesin, B., Ivanovic, M. & Budimac, Z. 2011. E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56, 885-899.
  29. Kuljis, J. & Liu, F. 2005. A comparison of learning style theories on the suitability for elearning. Web Technologies, Applications, and Services, vol. 2005, pp. 191-197.
  30. Latham, A., Crockett, K. & Mclean, D. 2013. Profiling student learning styles with multilayer perceptron neural networks. IEEE International Conference on Systems, Man, and Cybernetics, 2510-2515.
  31. Özpolat, E. & Akar, G. B. 2009. Automatic detection of learning styles for an e-learning system. Computers & Education, 53, 355-367.
  32. Simsek, Ö., Atman, N., Inceoglu, M. & Arikan, Y. 2010. Diagnosis of learning styles based on active/reflective dimension of Felder and Silverman's learning style model in a learning management system. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E. & Apduhan, B. (eds.) Computational Science and its Applications-ICCSA2010. Springer Berlin Heidelberg.
  33. Soloman, B. A. & Felder, R. M. nd. Index of Learning Styles Questionnaire [Online]. Available: http://www.engr.ncsu.edu/learningstyles/ilsweb.html [Accessed 7/2/2014].
  34. Udacity. 2012. Meet Udacity! [Online]. Available: http://www.udacity.com/.
  35. Udemy. 2014. Udemy [Online]. Available: https://www.udemy.com/ [Accessed 22-1-2014].
  36. Williams, J. J. 2013. Improving learning in MOOCs with Cognitive Science. AIED 2013 Workshops Proceedings Volume, 2013. 49.
  37. Yuan, L. & Powell, S. 2013. MOOCs and open education: Implications for higher education. CETIS JISC, 21, 2013.
Download


Paper Citation


in Harvard Style

Fasihuddin H., Skinner G. and Athauda R. (2015). A Framework to Personalise Open Learning Environments by Adapting to Learning Styles . In Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-107-6, pages 296-305. DOI: 10.5220/0005443502960305


in Bibtex Style

@conference{csedu15,
author={Heba Fasihuddin and Geoff Skinner and Rukshan Athauda},
title={A Framework to Personalise Open Learning Environments by Adapting to Learning Styles},
booktitle={Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2015},
pages={296-305},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005443502960305},
isbn={978-989-758-107-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - A Framework to Personalise Open Learning Environments by Adapting to Learning Styles
SN - 978-989-758-107-6
AU - Fasihuddin H.
AU - Skinner G.
AU - Athauda R.
PY - 2015
SP - 296
EP - 305
DO - 10.5220/0005443502960305