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Education and Information Technologies

, Volume 22, Issue 3, pp 807–825 | Cite as

Towards adaptive open learning environments: Evaluating the precision of identifying learning styles by tracking learners’ behaviours

  • Heba FasihuddinEmail author
  • Geoff Skinner
  • Rukshan Athauda
Article

Abstract

Open learning represents a new form of online learning where courses are provided freely online for large numbers of learners. MOOCs are examples of this form of learning. The authors see an opportunity for personalising open learning environments by adapting to learners’ learning styles and providing adaptive support to meet individual learner needs and preferences. Identifying learning styles of learners in open learning environments is crucial to providing adaptive support. Learning styles refer to the manner in which learners receive and perceive information. In the literature, a number of learning style models have been proposed. The Felder and Silverman Learning Styles Model (FSLSM) has been selected as the most appropriate model for open learning. In previous studies two approaches have been used to automatically identify learning styles based on the FSLSM. These approaches are known as the data-driven method and the literature-based method. In the literature, the literature-based method has been shown to be more accurate in identifying learning styles. This method relies on tracking learners’ interactions with the provided learning objects based on a set of pre-determined patterns that help in inferring learning styles. The patterns are monitored based on pre-identified threshold values. This paper aims to apply the literature-based method to open learning environments and introduce the optimal patterns and threshold values for identifying learning styles based on the FSLSM. To achieve this aim, a study was conducted whereby a prototype that simulates the open learning environment was developed and piloted on an undergraduate IT course so that learner behaviour could be tracked and data could be collected. Next, different sets of threshold values from the literature were considered along with some updated threshold values considering the context of open learning environments, and the precision of identifying learning styles was calculated. Eighty-three students participated in the study and used the developed prototype. Precision results from different threshold values presented in the literature along with customised threshold values for this study are reported and analysed in this paper. It is shown that threshold values derived from literature and customised to suit open learning environments provide a high level of accuracy in identifying learning styles. The paper presents the first study of its kind in evaluating threshold values and precision in identifying learning styles based on the FSLSM in open learning environments. The results are promising and indicate that the proposed methodology is efficient in detecting learning styles in open learning environments and useful for developing an adaptive framework.

Keywords

Adaptive learning Felder and Silverman model Learning styles identification MOOCs Open learning Web-based learning 

References

  1. Ahmad, N., & Tasir, Z. (2013). Threshold value in automatic learning style detection. Procedia-Social and Behavioral Sciences, 97, 346–352.CrossRefGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. Atman, N., Inceoğlu, M. M., & Aslan, B. G. (2009). Learning styles diagnosis based on learner behaviors in web based learning Computational Science and its Applications–ICCSA 2009 (pp. 900–909): Springer.Google Scholar
  4. Bajraktarevic, N., Hall, W., & Fullick, P. (2003). Incorporating learning styles in hypermedia environment: Empirical evaluation. Paper presented at the Proceedings of the Workshop on Adaptive Hypermedia and Adaptive Web-Based Systems, Nottingham, UK.Google Scholar
  5. Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction, 6(2–3), 87–129.CrossRefzbMATHGoogle Scholar
  6. Cabada, R. Z., Estrada, M. L. B., Cabada, R. Z., & Garcia, C. A. R. (2009, 9–11 Nov. 2009). A fuzzy-neural network for classifying learning styles in a web 2.0 and mobile learning environment. Paper presented at the Web Congress, 2009. LA-WEB ′09. Latin American.Google Scholar
  7. Carmona, C., Castillo, G., & Millan, E. (2008, 1–5 July 2008). Designing a dynamic Bayesian network for modeling students’ learning styles. Paper presented at the 8th IEEE International Conference on Advanced Learning Technologies ICALT ′08.Google Scholar
  8. Carro, R. M., Pulido, E., & Rodriguez, P. (2001). TANGOW: a model for internet-based learning. International Journal of Continuing Engineering Education and Life Long Learning, 11(1–2), 25–34.CrossRefGoogle Scholar
  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(1), 33–38. doi: 10.1109/13.746332.CrossRefGoogle Scholar
  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 M. Ikeda, K. Ashley & T.-W. Chan (Eds.), Intelligent Tutoring Systems (Vol. 4053, pp. 513–524): Springer Berlin Heidelberg.Google Scholar
  11. Chang, Y.-C., Kao, W.-Y., Chu, C.-P., & Chiu, C.-H. (2009). A learning style classification mechanism for e-learning. Computers & Education, 53(2), 273–285. doi: 10.1016/j.compedu.2009.02.008.CrossRefGoogle Scholar
  12. Claxton, C. S., & Murrell, P. H. (1987). Learning Styles: Implications for Improving Educational Practices: ERIC.Google Scholar
  13. Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Should we be using learning styles?: What research has to say to practice. London: Learning & Skills Research Centre.Google Scholar
  14. Coursera. (2012). Coursera. Retrieved 25-7-2012, 2012, from https://www.coursera.org/.
  15. edX. (2012). edX. Retrieved 26-5-2012, 2012, from http://www.edxonline.org/.
  16. Fasihuddin, H., Skinner, G., & Athauda, R. (2013). Boosting the opportunities of open learning (MOOCs) through learning theories. Journal on Computing, 3(3), 112–117.Google Scholar
  17. Fasihuddin, H., Skinner, G., & Athauda, R. (2014). Towards an adaptive model to personalise open learning environments using learning styles. Paper presented at the International Conference on Information, Communication Technology and System (ICTS).Google Scholar
  18. Fasihuddin, H., Skinner, G., & Athauda, R. (2015a). A framework to personalise open learning environments by adapting to learning styles Paper presented at the The 7th International Conference on Computer Supported Education, Lisbon, Portugal.Google Scholar
  19. Fasihuddin, H., Skinner, G., & Athauda, R. (2015b). Knowledge maps in open learning environments: an evaluation from learners’ perspectives. Journal of Information Technology and Application in Education, 4, 18–29. doi: 10.14355/jitae.2015.04.003.CrossRefGoogle Scholar
  20. Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(7), 674–681.Google Scholar
  21. Felder, R. M., & Spurlin, J. (2005). Applications, reliability and validity of the index of learning styles. International Journal of Engineering Education, 21(1), 103–112.Google Scholar
  22. García, P., Amandi, A., Schiaffino, S., & Campo, M. (2007). Evaluating Bayesian networks’ precision for detecting students’ learning styles. Computers & Education, 49(3), 794–808.CrossRefGoogle Scholar
  23. 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(4), 305–315. doi: 10.1111/j.1365-2729.2007.00262.x.CrossRefGoogle Scholar
  24. Graf, S. (2007). Adaptivity in learning management systems focusing on learning styles. (Ph.D. Thesis), Vienna University of Technology, Austria.Google Scholar
  25. Graf, S., & Kinshuk, K. (2007). Providing adaptive courses in learning management systems with respect to learning styles. Paper presented at the World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, Quebec City, Canada. http://www.editlib.org/p/26739.
  26. 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(4), 3–14.Google Scholar
  27. Graf, S., & Viola, S. (2009). Automatic student modelling for detecting learning style preferences in learning management systems. Paper presented at the Proc. International Conference on Cognition and Exploratory Learning in Digital Age.Google Scholar
  28. Graf, S., Kinshuk, & Tzu-Chien, L. (2008, 1–5 July 2008). Identifying learning styles in learning management systems by using indications from students’ behaviour. Paper presented at the 8th IEEE International Conference on Advanced Learning Technologies, 2008. ICALT ′08.Google Scholar
  29. Honey, P., & Mumford, A. (1992). The manual of learning styles (3rd ed.): Peter Honey.Google Scholar
  30. Hong, H., & Kinshuk, D. (2004). Adaptation to student learning styles in web based educational systems. Paper presented at the World Conference on Educational Multimedia, Hypermedia and Telecommunications.Google Scholar
  31. Keefe, J. W. (1988). Profiling and utilizing learning style: ERIC.Google Scholar
  32. Klašnja-Milićević, A., Vesin, B., Ivanović, M., & Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56(3), 885–899. doi: 10.1016/j.compedu.2010.11.001.CrossRefGoogle Scholar
  33. Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development: Prentice-Hall.Google Scholar
  34. Kuljis, J., & Liu, F. (2005). A comparison of learning style theories on the suitability for e-learning. Web Technologies, Applications, and Services, 2005, 191–197.Google Scholar
  35. Latham, A., Crockett, K., & Mclean, D. (2013). Profiling student learning styles with multilayer perceptron neural networks. Paper presented at the IEEE International Conference on Systems, Man, and Cybernetics (SMC).Google Scholar
  36. Moran, A. (1991). What can learning styles research learn from cognitive psychology? Educational Psychology, 11(3–4), 239–245.CrossRefGoogle Scholar
  37. Ozpolat, E., & Akar, G. B. (2009). Automatic detection of learning styles for an e-learning system. Computers & Education, 53(2), 355–367. doi: 10.1016/j.compedu.2009.02.018.CrossRefGoogle Scholar
  38. Peña, C.-I., Marzo, J.-L., & Rosa, J.-L. d. l. (2002). Intelligent agents in a teaching and learning environment on the web. Paper presented at the International Conference on Advanced Learning Technologies.Google Scholar
  39. Pritchard, A. (2013). Ways of learning: Learning theories and learning styles in the classroom (3rd ed.): Routledge.Google Scholar
  40. Şimşek, Ö., Atman, N., İnceoğlu, 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 D. Taniar, O. Gervasi, B. Murgante, E. Pardede & B. Apduhan (Eds.), Computational Science and Its Applications – ICCSA 2010 (Vol. 6017, pp. 544–555): Springer Berlin Heidelberg.Google Scholar
  41. Soloman, B. A., & Felder, R. M. (n.d.). Index of learning styles questionnaire. Retrieved 7/2/2014, from http://www.engr.ncsu.edu/learningstyles/ilsweb.html.
  42. Udacity (2012). Meet Udacity! , from http://www.udacity.com/.
  43. Udemy (2014). Udemy. Retrieved 22-1-2014, 2014, from https://www.udemy.com/.
  44. Williams, J. J. (2013). Improving learning in MOOCs with cognitive science. Paper presented at the AIED 2013 Workshops Proceedings Volume.Google Scholar
  45. Witkin, H. A., Moore, C. A., Goodenough, D. R., & Cox, P. W. (1977). Field-dependent and field-independent cognitive styles and their educational implications. Review of Educational Research, 47(1), 1–64. doi: 10.2307/1169967.CrossRefGoogle Scholar
  46. Zywno, M. S. (2003). A contribution to validation of score meaning for Felder-Soloman’s index of learning styles. Paper presented at the Proceedings of the 2003 American Society for Engineering Education annual conference and exposition.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Heba Fasihuddin
    • 1
    Email author
  • Geoff Skinner
    • 1
  • Rukshan Athauda
    • 1
  1. 1.Faculty of Science and ITThe University of NewcastleCallaghanAustralia

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