A Framework to Personalise Open Learning Environments by Adapting to Learning Styles Heba Fasihuddin, Geoff Skinner and Rukshan Athauda Faculty of Science and Information Technology, The University of Newcastle, Callaghan, Australia Keywords: Adaptive Framework, Learning Styles, MOOCs, Open Learning, Personalisation. 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. 1 INTRODUCTION Online learning evolves to take advantage of continuous advancement of technology. Open learning is a form of online learning that allows learning materials to be freely available on the Internet for anyone who is interested. Currently, several prestigious learning institutions, such as Harvard, MIT and Stanford, provide learning materials in an open approach. Coursera (Coursera, 2012), edX (edX, 2012), Udacity (Udacity, 2012) and Udemy (Udemy, 2014) are examples of open learning initiatives. Courses that are provided through these open learning environments are known as Massive Open Online Courses (MOOCs). As with any new model for learning, MOOCs are still in their early stages of evolution. There are many areas and opportunities for improvement, such as teaching and learning methods; learning content; assessments; identity authentication; accreditation; and learners’ varying needs, among others. The authors believe that considering cognitive science and learning principles has opportunity to enhance learning environments such as MOOCs (Fasihuddin et al., 2013b). This view is also supported by others (Williams, 2013). This paper focuses on personalisation of open learning environments based on learning styles. Learning style refers to the way a learner receives and processes information; therefore, every learner has a different learning style (Felder and Silverman, 1988). Among the existing models of learning styles, Felder and Silverman Learning Style Model (FSLSM) was selected. This paper proposes an adaptive framework that identifies the learners’ learning styles and consequently provides personalised navigational support. The literature- based approach (Graf, 2007) is used to automatically identify the learning style. This approach has been shown to have higher accuracy of results in detecting learning styles (Graf, 2007). It is mainly based on monitoring the learners’ behaviours on determined patterns based on the FSLSM. These patterns are determined based on learning objects that are common in open learning environments, such as in edX, Coursera, Udemy and Udacity. Based on our knowledge, no previous study has attempted to personalise the open environment using learning styles, and this is what distinguishes this study and the proposed framework. The rest of this paper is organised as follows: first, a background of the related concepts is presented in section 2; next, section 3 presents a review of previous work on adaptive systems based on learning styles; after that, the proposed adaptive framework and the development of the prototype are 296 Fasihuddin H., Skinner G. and Athauda R.. A Framework to Personalise Open Learning Environments by Adapting to Learning Styles. DOI: 10.5220/0005443502960305 In Proceedings of the 7th International Conference on Computer Supported Education (CSEDU-2015), pages 296-305 ISBN: 978-989-758-107-6 Copyright c  2015 SCITEPRESS (Science and Technology Publications, Lda.)