Title: Intelligent Assessment and Learning Personalisation in Virtual and 3D Environments
Overview: Science, Technology, Engineering and Maths (STEM) subjects are increasingly important in the 21st century as the world looks for new solutions to global problems. However, in the UK the demand for qualified engineers who are able to bridge the academic/scientific and social divide has never been greater (Clark and Andrews, 2010). A major problem associated with engineering is increasing its appeal as a subject discipline amongst prospective students. It is therefore essential that educational institutions attract the most capable students, nurture them, encourage critical thinking skills and provide them with the skills and depth of understanding needed to face the challenges of globalisation. E-learning is considered as an essential component of teaching and learning in the 21st century, having been widely used in educational systems within higher education (Ward and Kushner Benson, 2010). Virtual learning environments (VLE) are particularly effective e-learning tools for theoretical content and have helped facilitate the shift towards online education. Meanwhile much excitement has been generated by the idea of harnessing levels of deep engagement offered by serious games in higher education (Beggs et al., 2009), specifically the use of Virtual Worlds (VW) which provide a rich presentation layer delivering highly interactive flexible and adaptive environments. Such technologies are ideal for visualisations, simulations and practical learning, an important and distinguishing element of engineering education (Rowe et al., 2011). Effective content to suit individual student skills, knowledge and competences remains the key to ensuring a high quality of education (Bagchi, Kaushik and Kapoor, 2012). Problematically learning provision is generally uniform, students have different learning styles, skills and needs which affect the way in which they learn. It is therefore important to develop adaptive educational systems (AES) in order to make the learning process as effective, efficient, and motivating as possible (Ruiz et al., 2009). Learning Styles (LS) are popular tools used to identify student preferences and are widely used in AES ultimately facilitating the personalisation of content through assessment.
This research presents a twofold solution to manage students LS and content in an AES. A standardised multiple choice LS assessment is firstly presented primarily to categorise students LS upon first entry in a VLE. Students are then directed to a course specific to their LS. Throughout subsequent activity in the VLE, specific raw data is collected into a dataset and is intelligently assessed through Education Data Mining (EDM) and used to constantly manage the LS and content during student engagement by either confirming or changing the students primary LS. The novelty of the approach of this research is that the dual LS assessment will be extended to cover not only the theoretical content in the VLE but also a game based learning approach facilitated through an integrated VW.
This research therefore offers a complete solution supporting theoretical and the practical material content offering intelligent assessment and personalisation of content for each student.