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

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.

[1]  F. Coffield.  Should We Be Using Learning Styles? What Research Has to Say to Practice , 2004 .

[2]  Juan E. Gilbert,et al.  Adapting instruction in search of 'a significant difference' , 1999, J. Netw. Comput. Appl..

[3]  Sabine Graf,et al.  Providing Adaptive Courses in Learning Management Systems with Respect to Learning Styles , 2007 .

[4]  W. Hall,et al.  Incorporating learning styles in hypermedia environment: Empirical evaluation , 2003 .

[5]  Ramón Zataraín-Cabada,et al.  A Fuzzy-Neural Network for Classifying Learning Styles in a Web 2.0 and Mobile Learning Environment , 2009, 2009 Latin American Web Congress.

[6]  Zaidatun Tasir,et al.  Automatic Detection of Learning Styles in Learning Management Systems by Using Literature-based Method☆ , 2013 .

[7]  Mustafa Murat Inceoglu,et al.  Learning Styles Diagnosis Based on Learner Behaviors in Web Based Learning , 2009, ICCSA.

[8]  Rukshan Athauda,et al.  Boosting the Opportunities of Open Learning (MOOCs) through Learning Theories , 2013 .

[9]  Curtis A. Carver,et al.  Enhancing student learning through hypermedia courseware and incorporation of student learning styles , 1999 .

[10]  Essaid El Bachari,et al.  E-LEARNING PERSONALIZATION BASED ON DYNAMIC LEARNERS ' PREFERENCE , 2011 .

[11]  Peter Brusilovsky,et al.  Methods and techniques of adaptive hypermedia , 1996, User Modeling and User-Adapted Interaction.

[12]  Analía Amandi,et al.  Evaluating Bayesian networks' precision for detecting students' learning styles , 2007, Comput. Educ..

[13]  R. Felder,et al.  Learning and Teaching Styles in Engineering Education. , 1988 .

[14]  Peter Brusilovsky,et al.  Adaptive Hypermedia , 2001, User Modeling and User-Adapted Interaction.

[15]  Analía Amandi,et al.  An enhanced Bayesian model to detect students' learning styles in Web-based courses , 2008, J. Comput. Assist. Learn..

[16]  Peter Brusilovsky,et al.  Adaptive navigation support in educational hypermedia: the role of student knowledge level and the case for meta-adaptation , 2003, Br. J. Educ. Technol..

[17]  Gladys Castillo,et al.  Designing a Dynamic Bayesian Network for Modeling Students' Learning Styles , 2008, 2008 Eighth IEEE International Conference on Advanced Learning Technologies.

[18]  Sabine Graf,et al.  AUTOMATIC STUDENT MODELLING FOR DETECTING LEARNING STYLE PREFERENCES IN LEARNING MANAGEMENT SYSTEMS * , 2007 .

[19]  Chih-Ping Chu,et al.  A learning style classification mechanism for e-learning , 2009, Comput. Educ..

[20]  Yong Se Kim,et al.  Learning Styles Diagnosis Based on User Interface Behaviors for the Customization of Learning Interfaces in an Intelligent Tutoring System , 2006, Intelligent Tutoring Systems.