ASSESSING ACCELEROMETERS AS A MEANS TO INFER INDIVIDUAL USER LEARNING STYLES FOR INCLUSIVE MLEARNING SYSTEMS
Identifying individual learner styles has recently become an essential component in the development of learner models, especially as we move towards mLearning (mobile learning) environments. This paper considers the potential for the future establishment of a usable and accessible mobile learning system for the inclusion of blind and vision-impaired users within mainstream, collaborative and ubiquitous mobile learning environments. A specific aim of such a learning system would be to enable these students to work successfully alongside their fully sighted peers for the collaborative achievement of a single learning outcome. There is therefore an increasing need to establish learning models to facilitate the provision of adaptive content to specific learner needs, especially for the incorporation of adaptive media. To-date, wireless and handheld technologies have allowed the incorporation of mobile applications both in and out of the classroom for the achievement of ubiquitous, independent and collaborative learning outcomes. With the development of this technology, and given the personal nature of these devices, there is a lot to be gained in education by harnessing technologies incorporated within these devices to provide a collaborative environment for learning regardless of the difficulties faced by individual learners. The incorporation of technologies such as the accelerometer within many modern mobile devices, in particular mobile phones, provides an opportunity to develop mobile learning applications to integrate vision-impaired and blind students in an adaptive and inclusive collaborative mLearning system. The ability to create an engaging and equal playing field could reduce the digital divide currently in existence between blind and fully sighted students within future mobile learning environments. To facilitate the advancement of this potential area within mobile learning, it is necessary to assess accelerometer technology as an alternative interaction device and, to assess the potential for using these devices to infer individual learning styles. This paper outlines a proposed mLearning system to gather learner style feedback from user interaction through an accelerometer. The aim of the system is to allow for the assessment of user accelerometer input as an alternative to user direct manipulation input inferring individual user learning styles within mobile learning environments based on the Felder-Silverman Index of Learning Styles Model.