Current Science

Open Access Open Access  Restricted Access Subscription Access

Predicting Learning Styles Based on Students’ Learning Behaviour Using Correlation Analysis


Affiliations
1 Department of Artificial Intelligence, Universiti of Malaya, Malaysia
 

Past research has proposed various approaches to automatically detect students’ learning styles to address problems associated with traditional research methods (i.e. questionnaire). However, results obtained through traditional research methods have issues in terms of accuracy and precision which need to be addressed. In general, the existing automatic detection approaches are only able to provide satisfactory results for specific learning style models and/or dimensions, or even only work for certain learning management systems. The aim of this study is to propose an automatic detection of learning styles from the analysis of students’ learning behaviour by constructing a mathematical model. This study specifically explores the relationship between students’ learning behaviour and their learning styles. To investigate this relationship, a pilot experiment was conducted with 33 students. The students used Moodle platform, a learning management system, as supplementary online learning material for Java programing. The students’ learning behaviour was tracked and recorded. Thirty students’ data (i.e. their learning behaviour and learning styles; measured using the Index of Learning Styles (ILS) instrument) were analysed using the proposed correlation analysis to identify the relationship. The remaining three students’ learning behaviour data were used to predict their learning styles. The findings are discussed with regard to accuracy of automatic detection of learning styles using the ILS instrument.

Keywords

Automatic Learning Style Assessment, Learning Behaviour Pattern, Student Modelling.
User
Notifications
Font Size

  • Qutechate, W. A., Almarabeh, T. and Alfayez, R., E-learning system in the University of Jordan: problem solving case study. J. Theor. Appl. Inf. Technol., 2013, 53(3), 353–358.

  • Surjono, H. D., The evaluation of a Moodle based adaptive e-learning system. Int. J. Inf. Educ. Technol., 2014, 4(1), 89–92.

  • Yarandi, M., Jahankhani, H. and Tawil, A.-R. H., Towards adaptive E-learning using decision support systems. Int. J. Emerg. Technol. Learn., 2013, 8(1), 44–51.

  • Bajraktarevic, N., Hall, W. and Fullick, P., Incorporating learning styles in hypermedia environment: Empirical evaluation. In Proceedings of the Workshop on Adaptive Hypermedia and Adaptive Web-based System spp., 2003, pp. 41–52.

  • Kinshuk, G. S., Providing Adaptive Courses in Learning Management Systems with Respect to Learning Style, the World Conference on E-Learning in Corporate, Government Healthcare, and Higher Education. AACE, 2007, pp. 2576–2583.

  • Dung, P. Q. and Florea, A. M., An approach for detecting learning styles in learning management systems based on learners’ behaviours. Int. Proc. Econ. Dev. Res., 2012, 30, 171–177.

  • Feldman, J., Monteserin, A. and Amandi, A., Automatic detection of learning styles: state of the art. Artif. Intell. Rev., 2015, 44(2), 157–196.

  • Ahmad, N., Tasir, Z., Kasim, J. and Sahat, H., Automatic detection of learning styles in learning management systems by using literature-based method. Procedia – Soc. Behav. Sci., 2013, 103, 181–189.

  • Dung, P. Q. and Florea, A. M., A Literature-based Method to Automatically Detect Learning Styles in Learning Management Systems. Paper presented at the Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, 2012, p. 46.

  • Graf, S., Using cognitive traits for improving the detection of learning styles. In Database and Expert Systems Applications (DEXA), 2010 Workshop on IEEE, 2012, pp. 74–78.

  • Graf, S. and Liu, T. C., Supporting teachers in identifying students' learning styles in learning management systems: an automatic student modelling approach. J. Educ. Technol. Soc., 2009, 12(4), 3.

  • Cha, H. J., Kim, Y. S., Park, S. H., Yoon, T. B., Jung, Y. M. and Lee, J. H., Learning styles diagnosis based on user interface behaviors for the customization of learning interfaces in an intelligent tutoring system. In Intelligent Tutoring Systems, Springer, Berlin, Heidelberg, 2006, pp. 513–524.

  • Limongelli, C., Sciarrone, F., Temperini, M. and Vaste, G., Adaptive learning with the LS-plan system: a field evaluation. IEEE Trans. Learn Technol., 2009, 2(3), 203–215.

  • Cabada, R. Z., Estrada, M. L. B. and Garcia, C. A. R., A fuzzy-neural network for classifying learning styles in a Web 2.0 and mobile learning environment. In Web Congress, LA-WEB’09. Latin American, 2009, pp. 177–182.

  • Garcia, P., Amandi, A., Schiaffino, S. and Campo, M., Evaluating Bayesian networks’ precision for detecting students’ learning styles. Comput. Educ., 2009, 49(3), 794–808.

  • Zatarain-Cabada, R., Barrón-Estrada, M. L., Zepeda-Sánchez, L., Sandoval, G., Osorio-Velazquez, J. M. and Urias-Barrientos, J. E., A Kohonen network for modeling students’ learning styles in web 2.0 collaborative learning systems. In MICAI 2009: Advances in Artificial Intelligence, Springer, Berlin, Heidelberg, 2009, pp. 512–520.

  • Castillo, G., Gama, J. and Breda, A. M., An adaptive predictive model for student modeling. Advances in Web-based Education: Personalized Learning Environments, Information Science Publishing, Hershey, 2005, p. 7092.

  • Ozyurt, O., Ozyurt, H. and Baki, A., Design and development of an innovative individualized adaptive and intelligent E-learning system for teaching–learning of probability unit: details of UZWEBMAT. Expert Syst. Appl., 2013, 40(8), 2914–2940.

  • Hong, H. and Kinshuk, D., Adaptation to student learning styles in web based educational systems. In EdMedia: World Conference on Educational Media and Technology, Association for the Advancement of Computing in Education (AACE), Waynesville, NC, 2004, pp. 491–496.

  • Graf, S. and Lin, T., Analysing the relationship between learning styles and cognitive traits. In Seventh IEEE International Conference on Advanced Learning Technologies, ICALT, 2007, pp. 235–239.

  • Garcia, P., Amandi, A., Schiaffino, S. and Campo, M., Evaluating Bayesian networks’ precision for detecting students’ learning styles. Comp. Educ., 2007, 49(3), 794–808.

  • Kinshuk, G. S. and Tzu-Chien, L., Identifying Learning Styles in Learning Management Systems by Using Indications from Students’ Behaviour. Paper Presented at the Eighth IEEE International Conference Advanced Learning Technologies, 2008. ICALT’08, 2008, pp. 482–486.

  • Popescu, E., Trigano, P. and Badica, C., Relations between learning style and learner behavior in an educational hypermedia system: An exploratory study. In Eighth IEEE International Conference on Advanced Learning Technologies, ICALT'08, 2008, pp. 725–726.


Abstract Views: 384

PDF Views: 113




  • Predicting Learning Styles Based on Students’ Learning Behaviour Using Correlation Analysis

Abstract Views: 384  |  PDF Views: 113

Authors

Li Ling Xiao
Department of Artificial Intelligence, Universiti of Malaya, Malaysia
Siti Soraya Binti Abdul Rahman
Department of Artificial Intelligence, Universiti of Malaya, Malaysia

Abstract


Past research has proposed various approaches to automatically detect students’ learning styles to address problems associated with traditional research methods (i.e. questionnaire). However, results obtained through traditional research methods have issues in terms of accuracy and precision which need to be addressed. In general, the existing automatic detection approaches are only able to provide satisfactory results for specific learning style models and/or dimensions, or even only work for certain learning management systems. The aim of this study is to propose an automatic detection of learning styles from the analysis of students’ learning behaviour by constructing a mathematical model. This study specifically explores the relationship between students’ learning behaviour and their learning styles. To investigate this relationship, a pilot experiment was conducted with 33 students. The students used Moodle platform, a learning management system, as supplementary online learning material for Java programing. The students’ learning behaviour was tracked and recorded. Thirty students’ data (i.e. their learning behaviour and learning styles; measured using the Index of Learning Styles (ILS) instrument) were analysed using the proposed correlation analysis to identify the relationship. The remaining three students’ learning behaviour data were used to predict their learning styles. The findings are discussed with regard to accuracy of automatic detection of learning styles using the ILS instrument.

Keywords


Automatic Learning Style Assessment, Learning Behaviour Pattern, Student Modelling.

References





DOI: https://doi.org/10.18520/cs%2Fv113%2Fi11%2F2090-2096